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  - Nat Biotechnol 30(1):vii-viii (2012)
  Nature Biotechnology | In This Issue In this issue Journal name:Nature BiotechnologyVolume: 30,Pages:vii–viiiYear published:(2012)DOI:doi:10.1038/nbt.2104Published online09 January 2012 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Capturing human transcriptome complexity RNA-Seq is known to preferentially assay the more abundant transcripts in a sample, simply because these transcripts are more likely to be primed and sequenced. To boost the sequencing coverage of rare transcripts, Mattick and colleagues use tiling arrays to capture a subset of the transcriptome before sequencing, a strategy analogous to exome DNA sequencing. This approach affords saturating coverage of transcripts produced across 0.77 Mb at ~50 loci in human foot fibroblast cells. The results are exciting. The authors assemble RNA-Seq reads into full-length transcripts and find new splice isoforms of noncoding RNAs, such as HOTAIR, and new isoforms of well-studied protein-coding genes, such as p53 and HOX. The abundances of these isoforms could be quantitatively assessed, and they could be validated by long reads from another sequencing platform and by reverse transcriptase–PCR. This study suggests that the human transcriptome is not only larger and more complex than init! ially thought but also shows greater diversity between individual cells. Given that it seems beyond the capacity of current sequencing technologies to provide both a sufficiently comprehensive and deep view of the transcriptome, targeted RNA sequencing allows researchers to focus their sequencing on genes or transcriptional features of interest (for additional commentary, see Genome Med., 74, 2011). CM Genome structures in cell populations Within the nucleus, chromosomes appear to be organized in distinct spatial regions in configurations that vary substantially even between cells of the same type in an otherwise homogeneous population. Chen and colleagues describe two advances in the measurement and analysis of this complexity. They first improve on Hi-C, an experimental method for identifying contacts between and within chromosomes on a genome-wide scale. By performing a key ligation step on a solid substrate rather than in solution, the authors improve the signal-to-noise ratio, which facilitates the measurement of low-frequency contacts and uncovers surprisingly intricate patterns of interchromosomal interactions. A second advance is embodied in a new data analysis approach. Instead of calculating a single model of the three-dimensional structure of the genome as done previously, the authors generate a population of structures such that each one is consistent with a subset of the experimental data and the ! composition of the population captures statistical properties of the data. The resulting models agree with previous experimental observations from imaging studies and provide new insights into the factors that may govern chromosome contacts. These advances should help elucidate the links between the spatial organization of the genome and its function. CM Pigeonpea genome Pigeonpea is an important source of dietary protein to over 1 billion people in developing countries and is most frequently grown by resource-poor subsistence farmers. It is unique relative to many so-called orphan crops insofar as there are hybrid varieties with as much as 40% higher yields than traditional strains. The species is also relatively hardy in the face of drought and high temperatures, which are frequently encountered in marginal environments in many parts of Asia and Africa. Even so, limited genetic resources have constrained rational improvement of the crop. The draft genome sequence of pigeonpea by Varshney and colleagues, the first for a nonindustrial crop and the second for a food legume (after soybean), should help to elucidate the genetic basis of hybrid vigor in the species and facilitate molecular breeding efforts to improve sustainable food production for many of the world's poorest people. PH Comparing whole-genome sequencing Sequencing a whole human genome to high coverage is most economical when either Illumina's (San Diego) sequencing-by-synthesis or Complete Genomics' (Mountain View, CA, USA) sequencing-by-ligation technology is used. Snyder and colleagues use both technology platforms to sequence blood and saliva samples from the same individual to ~76× coverage. They compare the performance of each platform by using established algorithms to call single-nucleotide polymorphisms (SNPs), insertions and deletions. About 12% of all SNPs are detected by only one platform, with >75% of these platform-specific variants called from the Illumina data. Validation by genotyping arrays, Sanger sequencing and custom target-capture sequencing suggests that both platforms identify largely similar sets of known variants and that the platform-specific calls may have a false-positive rate of >35%. Perhaps as expected, much of the platform-specific variation can be attributed to repetitive regions in the gen! ome. The authors conclude that sequencing a sample with both platforms yields the most accurate variant calls. CM Assessing filters for genome variants Data filters are essential for improving the accuracy of genetic variants called from genome sequence data. Lambrechts and colleagues comprehensively analyze commonly used filtering approaches, resulting in guidelines for tailoring filters to balance the calling of fewer false positives with fewer false negatives. A key challenge in this analysis is knowing which of the ~3 million variants typically found in a single human genome are true variants and which ones are errors. The authors address this problem using Complete Genomics' sequencing-by-ligation platform to sequence the genomes of monozygotic twins; the twins' genomes should be identical, except perhaps for a handful of de novo mutations, and thus erroneous variants can be identified as the 'discordant' ones found in only one twin. By optimizing the data filters to remove as many of the discordant variants as possible, the authors reduce the error rate of single-nucleotide variant calling by 290-fold, allowing them t! o identify and validate two true genetic differences between monozygotic twins. The authors speculate that these variants may contribute to the schizophrenia observed in one twin and not the other. The optimized filters were also shown to reduce the error rate on Illumina sequencing data by >15-fold and on cancer genome data. CM Finding bliss through anti-BLyS The many and varied manifestations of systemic lupus erythematosus (SLE) have confounded drug developers for almost a half century, leaving patients with only generalized and potent anti-inflammatory drugs to manage their disease. Hence, last year's approval of Benlytsa (belimumab) for treating adults with active autoantibody–positive SLE received a warm reception from patients and clinicians alike. As part of our series of case studies describing the discovery and development of novel drugs originating from the biotech sector, belimumab's tortuous path to approval is related by William Stohl, who participated in the clinical trials, and David Hilbert, who participated in the early stages of the drug's development at Human Genome Sciences (Rockville, MD, USA). Among the somewhat unpredictable events that contributed to the success was the failure of other promising drug candidates, the commitment by GlaxoSmithKline (London) to the program right before the drug failed in a ! phase 2 clinical trial, and the acceptance by the US Food and Drug Administration of a novel index for evaluating outcomes. As this was one of the first drug programs to capitalize on information derived from human genomics, it presents an interesting case study in perseverance and serendipity. LD Rice genomics A catalog of genetic variation in a crop species facilitates gene mapping, marker-assisted breeding and the analysis of elite traits. Wang and colleagues identify 6.5 million single-nucleotide polymorphisms (SNPs) in Oryza sativa, Asian cultivated rice, by sequencing 40 cultivated and 10 wild strains from the two major subspecies, indica and japonica. SNPs are pinpointed by sequencing each strain to >15× coverage and mapping the reads to the rice reference genome. Additional analysis and de novo assembly of the reads yield structural variants in the genomes and genes that are present in some strains but not the reference genome. The large collection of SNPs allows the authors to investigate the evolutionary history of rice and to identify genes that may have been selected during rice domestication. These genes may be responsible for agronomically important traits. The data reported here should facilitate the genetic analysis of rice and the breeding of improved varieties. CM Patent roundup India's National Biodiversity Authority is suing Jalna-based Maharashtra Hybrid Seed, Monsanto (St. Louis) and several Indian universities, alleging that they used local varieties to develop Bacillus thuringiensis toxin (Bt) brinjal without proper licenses. AM It is known that commercially important patents are maintained over their lifetime and nearly two-thirds of patents expire due to nonpayment of maintenance fees. Saberwal and colleagues investigate the proportion of US patents protecting biologics and small molecules approved by the US Food and Drug Administration that have expired due to nonpayment of maintenance fees. MF Recent patent applications related to gene and DNA synthesis. MF Next month in Nature Biotechnology * Vascular smooth muscle subtypes from pluripotent cells * Choosing antibody drug conjugation sites * Improved trait mapping in crops * Enzyme optimization by crowdsourcing * Disease analysis with a structural interactome * Limiting liver toxicity Additional data
• What happened to personalized medicine?
  - Nat Biotechnol 30(1):1 (2012)
  Nature Biotechnology | Editorial What happened to personalized medicine? Journal name:Nature BiotechnologyVolume: 30,Page:1Year published:(2012)DOI:doi:10.1038/nbt.2096Published online09 January 2012 Personalized medicine falls a long way short of the predictive and preventative healthcare paradigm it once promised. View full text Additional data
• Incyte comes of age with JAK inhibitor approval
  - Nat Biotechnol 30(1):3-5 (2012)
  Nature Biotechnology | News Incyte comes of age with JAK inhibitor approval * Nuala Moran1Journal name:Nature BiotechnologyVolume: 30,Pages:3–5Year published:(2012)DOI:doi:10.1038/nbt0112-3Published online09 January 2012 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg MedicalRF.com A myeloid progenitor cell in the bone marrow. Jakafi is the first treatment against myeloid fibrosis, a proliferative disorder of the bone marrow that results in the replacement of the marrow with connective tissue. There were several reasons to celebrate on November 16 when the US Food and Drug Administration (FDA) approved Jakafi (ruxolitinib), a small-molecule drug against the rare blood cancer myelofibrosis and other myeloproliferative disorders. Not only is Jakafi the first treatment approved against myelofibrosis and similar neoplasms, but it is also Incyte's first drug to market, one that validates a novel mechanism of action in cancer no less—blocking the Janus-kinases JAK1 and JAK2. The small-molecule development program also exemplifies a rapid development time—six years after an activating mutation in JAK2 was first identified in this cancer; and, in this case, Incyte's embrace of patient-reported outcome tools in the trial design was key in facilitating approval, according to the FDA's Richard Pazdur (Box 1). Other companies developing JAK inhibitors against myelofibrosis and two related myeloproliferative disorders, polycythemia vera and essential thrombocythemia, will ! now be pushing to get a slice of the market (Table 1). Box 1: Patient tools and phase 3 trials of Jakafi Full box Table 1: Selected JAK inhibitors in efficacy trials for myeloproliferative neoplasmsa Full table Jakafi will be the first of many JAK inhibitors, believes John O'Shea, an immunologist at the US National Institutes of Health, who has been heavily involved in elucidating the minutiae of how these enzymes operate. "It's taken 20 years from the first discoveries, so it's tremendously gratifying to see the basic science playing out and the first inhibitor reach the market." Wilmington, Delaware–based Incyte developed Jakafi in a $1 billion partnership with Basel-based Novartis. The FDA's recent go-ahead has increased optimism in the other first-generation JAK inhibitors under testing in late-stage trials. Chief among these is Sanofi's SAR302503 (TG101348), a small-molecule ATP-competitive inhibitor that blocks both JAK2 and its constitutively activated mutant form (JAK2V617F), which is commonly found in patients with myeloproliferative neoplasms. The French pharma company acquired SAR302503 in the $560 million acquisition of San Diego–based TargeGen in November 2009 and last month initiated recruitment for a multicenter, randomized phase 3 trial (JAKARTA) of patients with high-risk primary myelofibrosis or post-essential thrombocythemia myelofibrosis. The rest of the JAK inhibitor space has also seen several large deals. These include a $550 million option agreement between Onyx Pharmaceuticals, of S. San Francisco, California, and S*BIO of Singapore signed in January 2009 and the $9.44 million purchase of Australian biotech Cytopia in February 2010 by Lehigh Valley, Pennsylvania–based YM Bioscience. The furthest advanced competitor to Jakafi is at least two years behind, and with marketing in the US ramping up and European approval expected in mid-2012, Incyte and Novartis will have the field to themselves for some time. Jakafi's approval in myeloproliferative disorders lights the way for small-molecule JAK inhibitors under development for other indications, in particular solid cancers and inflammatory diseases. Rheumatoid arthritis is the indication in which competing molecules are most advanced, with New York–based Pfizer's tofacitinib the front runner (Nat. Biotechnol.29, 467–468, 2011). Tofacitinib is a moderately selective JAK3 inhibitor (which also binds JAK1 and JAK2) that is currently in phase 3, with a new drug application filed for rheumatoid arthritis. Tofacitinib and related small-molecule inhibitors could provide orally available treatments for patients who do not respond, or have become immune, to injected tumor necrosis factor-alpha inhibitors. "We are seeing an interest from pharma in the whole arena; they are all looking for JAK inhibitors," says Onno van de Stolpe, CEO of Galapagos. In November, the Mechelen, Belgium–based company released positive phase 2a data i! n rheumatoid arthritis for its JAK1 inhibitor, GLPG0634. Jakafi's approval for myelofibrosis was based on two randomized phase 3 trials, Comfort 1, a placebo-controlled study with 309 patients, was conducted in the US by Incyte, and Comfort 2, in which Jakafi was compared with the best available care, a 219-patient European study run by Novartis (Box 1). The FDA gave the go-ahead to use Jakafi as a treatment for intermediate or high-risk myelofibrosis in advance of the designated Prescription Drug User Fee Act date of December 3. JAK inhibitors provide a nuanced approach: although they do not completely abrogate tumor mitogenic signaling, they do offer the opportunity to block multiple cytokines and growth factors, unlike other cytostatic drugs that target just one signaling pathway. In the case of JAK2, the kinase also migrates to the nucleus, where it phosphorylates Tyr41 on histone 3, leading to the overexpression of oncogenes. Each JAK associates with different cytokine or growth factor receptors. JAK2 signaling, for example, is associated with cytokine receptors activated by growth hormone, thrombopoietin, erythropoietin and interleukin 3, among others. Although the exact cause of myelofibrosis is unclear, the disease is known to be associated with disruptions to intracellular JAK signaling and high circulating levels of cytokines. Patients have a range of mutations, of which JAK2V617F is the most common, occurring in 50% of patients with primary myelofibrosis and in erythrocythemia, and in >90% of patients with polycythemia vera. Although the presence of this mutation can be a diagnostic aid, its contribution to overall pathology is not understood. Myelofibrosis involves excessive generation of blood cells, leading on to the fibrosis and subsequent failure of the bone marrow. This prompts the spleen and the liver to take over production of blood cells, causing enlargement of the spleen, and related, debilitating, symptoms including anemia, extreme fatigue, pruritus (itching), early satiety and bone pain. In the two phase 3 trials of Jakafi, there were significant reductions in spleen size—the primary end point—and also improvements in six other symptoms. "This is a really dismal disease [in which] there's never been a phase 3 study, suddenly people are interested, things are happening and things are moving on at a dramatic pace," says Claire Harrison, consultant hematologist at Guy's and St. Thomas' Hospital, London, who was chief investigator for the Comfort 2 trial. Of the Jakafi approval, she adds that it is "a massive step forward for patients for several reasons. There is clinical benefit from reduced splenomegaly and dramatic control of some very nasty constitutional symptoms." Jakafi has fallen short of initial expectations, however. Although treatment relieves symptoms, there was no difference in progression of the disease between Jakafi-treated and control patients in the phase 3 studies. Unlike Gleevec (imatinib), Novartis's treatment for another myeloproliferative disease, chronic myeloid leukemia, Jakafi is not a cure, Harrison notes. It is encouraging, though, that Harrison is beginning to see slight signs of a disease-modifying effect in patients who remained on the drug after the end of the Comfort 2 trial, and have now been taking Jakafi for over two years, as demonstrated by an increase in hemoglobin levels over baseline. This may be due to an effect on inflammatory cytokines, and may mean there is less fibrosis, Harrison says, noting even bone marrow transplants take time to influence fibrosis. Two studies presented at the American Society of Hematology meeting in San Diego in December revealed conflicting data on the survival benefit of Jakafi. One study matching patients in the phase 1/2 study treated at the MD Anderson Cancer Center, with historical controls, found a significant effect on overall survival. On the other hand, a long-term outcome study in 51 patients treated at the Mayo Clinic who took part in the phase 1/2 trial of Jakafi found no significant difference in survival of the 51 Jakafi-treated patients compared with 410 cases of myelofibrosis seen at the Mayo Clinic. Given some of the limitations of Jakafi, companies with competing JAK inhibitors are looking to stress that their products act through different modes of action, often showing greater selectivity for particular JAK subtypes or exhibiting different side-effect profiles. "We were all waiting for this result to validate the class. Now we have a better sense of how to position our compound and what its potential is in the market," says Tamar Howson, CEO of Singapore-based S*BIO, which is currently looking for a commercialization partner as it progresses preparations for a phase 3 trial of its selective JAK2 inhibitor, pacritinib (SB1518). Howson notes that unlike Jakafi, there was no evidence of myelosuppression and no exacerbation of cytopenias in the phase 2 studies of pacritinib. "I think our compound does have a special and important niche in the JAK space," she says. It is not clear if the different effects seen with pacritinib arise because it is a selective JAK2 inhibitor. "I can't explain at this point: in every therapy area there are compounds where very small variations cause different effects," Howson says. Similarly, James Smith, spokesman for YM Biosciences, comments that approval of Jakafi provides validation both for JAK inhibitors as a whole and for their role in treating myelofibrosis. "For a company like ours, this removes the uncertainty," he says. YM Biosciences' CYT387 inhibits both JAK1 and JAK2 like Jakafi, but multicenter phase 1/2 data released last month at the American Society of Hematology indicate CYT387 has a less myelosuppressive effect than Jakafi, with fewer cases of new-onset anemia. "We think it will have a distinctive profile," says Smith. Salveen Richter, analyst at Collins Stewart, notes that along with its early market entry advantage, though, Jakafi has surpassed expectations in terms of both the label and the price. The broader-than-expected label granted by the FDA includes patients with less severe—intermediate risk—disease, a group that was not included in the phase 3 trials. In addition, "the label does not include a black box warning, REMs [risk evaluation and mitigation strategies] or post-marketing commitments," Richter adds. And at $84,000 per patient per year, Incyte has secured a higher price than the $40,000–60,000 per year it originally forecasted. Eric Schmidt, analyst at Cowen and Company, agrees. The label is "broad and clean," he says. "Jakafi [is] an excellent drug with striking efficacy in a true unmet need." Schmidt forecasts worldwide sales of >$1 billion by 2015, with US sales alone reaching $400 million by 2016. Author information Affiliations * London * Nuala Moran Author Details * Nuala Moran Search for this author in: * NPG journals * PubMed * Google Scholar Additional data Affiliations * London * Nuala Moran Author Details * Nuala Moran Search for this author in: * NPG journals * PubMed * Google Scholar
• Eylea approval transforms Regeneron
  - Nat Biotechnol 30(1):4 (2012)
  Article preview View full access options Nature Biotechnology | News Eylea approval transforms Regeneron * Mark RatnerJournal name:Nature BiotechnologyVolume: 30,Page:4Year published:(2012)DOI:doi:10.1038/nbt0112-4Published online09 January 2012 Regeneron's Tarrytown R&D headquarters Following the anticipated November Food & Drug Administration approval of Eylea (aflibercept), the biotech firm Regeneron Pharmaceuticals is entering the highly competitive, $3 billion-plus worldwide market for drugs to treat wet age-related macular degeneration (wet AMD). The US regulator had extended the review period for the drug, an injectable fusion protein designed to bind to vascular endothelial growth factor-A (VEGF-A) and placental growth factor, from August to give the agency time to digest additional manufacturing information it had requested. Although that move delayed the commercial launch, it did little to dampen excitement around the molecule, which in phase 3 trials helped maintain visual acuity as effectively as the standard of care, Roche unit Genentech's VEGF-A-binding monoclonal antibody (mAb) fragment Lucentis (ranibizumab). Genentech, in S. San Francisco, California, has drawn fire over Lucentis's hefty $2,000 per dose price tag, especially in light of ! data showing that Genentech's own cancer drug Avastin (bevacizumab), the full-length version of the mAb that is being used off-label to treat wet AMD at much lower cost, is equally effective (Nat. Biotechnol., 560, 2011). Priced at a 7.5% discount to Lucentis per dose, Eylea creates "a compelling cost-saving argument for the system by virtue of the potential for fewer injections and more importantly fewer monitoring visits," says RBC Capital Markets analyst Jason Kantor, in San Francisco, saving patients roughly $2,500 annually. Regeneron is collaborating with Berlin-based Bayer HealthCare on global development of Eylea in wet AMD. The Tarrytown, New York, biotech is also pursuing indications for it in central retinal vein occlusion and diabetic macular edema, and is developing an infusion-based form of the drug for cancer therapy. Bayer submitted an application for marketing authorization of the drug in Europe for wet AMD in June 2011. Regeneron's launch of Eylea effec! tively changes the company from one dependent on partnering an! d licensing into a commercial organization. Leerink Swann analyst Joshua Schimmer, in Boston, calls it one of the transformational events in the biotech industry in 2011. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Author Details * Mark Ratner Search for this author in: * NPG journals * PubMed * Google Scholar Additional data Author Details * Mark Ratner Search for this author in: * NPG journals * PubMed * Google Scholar
• Avastin loses breast cancer indication
  - Nat Biotechnol 30(1):6 (2012)
  Article preview View full access options Nature Biotechnology | News Avastin loses breast cancer indication * Karen CareyJournal name:Nature BiotechnologyVolume: 30,Page:6Year published:(2012)DOI:doi:10.1038/nbt0112-6aPublished online09 January 2012 US Food and Drug Administration (FDA) Commissioner Margaret Hamburg handed down in November the final decision to revoke approval of Genentech's Avastin (bevacizumab) in metastatic breast cancer. Avastin, which binds to vascular endothelial growth factor, received accelerated approval in 2008 obligating the S. San Francisco–based company to confirm promising data in post-marketing studies, but two confirmatory trials failed to support the 5.5-month improvement of progression-free survival seen in the pivotal study. Citing a poor benefit-to-risk profile, FDA's Center for Drug Evaluation and Research (CDER) proposed withdrawing approval due to serious hypertension and kidney toxicity side effects, but the Roche unit challenged the move—the first time a drugmaker has ever done so—requesting a two-day hearing last summer. Following the hearing, the FDA's Oncologic Drugs Advisory Committee supported CDER's recommendation, as did Hamburg, but Genentech has not given up. It i! s launching a new phase 3 trial of Avastin with paclitaxel, evaluating a potential biomarker to help identify metastatic breast cancer patients who benefit the most. Hamburg says she will reconsider her decision if Genentech is able to identify super-responders. "FDA will work with Genentech on the design of trials to explore whether such a subset of patients exists," says FDA spokesperson Karen Riley, "and if so, develop ways to identify such patients for whom Avastin treatment is safe and effective. But while these studies are going on, the breast cancer indication should not remain on the drug's labeling." Avastin is approved for colon, lung, kidney and brain cancers. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Author Details * Karen Carey Search for this author in: * NPG journals * PubMed * Google Scholar Additional data Author Details * Karen Carey Search for this author in: * NPG journals * PubMed * Google Scholar
• Pivotal FDA/Exelixis dispute
  - Nat Biotechnol 30(1):6 (2012)
  Article preview View full access options Nature Biotechnology | News Pivotal FDA/Exelixis dispute * Mark RatnerJournal name:Nature BiotechnologyVolume: 30,Page:6Year published:(2012)DOI:doi:10.1038/nbt0112-6bPublished online09 January 2012 The US Food & Drug Administration (FDA) stunned Exelixis when it failed to agree on a special protocol assessment (SPA) of a pivotal trial of the company's small molecule, cabozantinib, in metastatic castration-resistant prostate cancer. The S. San Francisco–based biotech requested an SPA because it intends to use pain response to gauge the efficacy of cabozantinib, a dual inhibitor of the mesenchymal epithelial transition and vascular endothelial growth factor signaling pathways. This novel end point gave FDA pause. The trial will proceed, but Exelixis not having the SPA imprimatur discomfited analysts and Exelixis' stock price has been cut almost in half since the FDA decision. Along with the added regulatory uncertainty, analysts were also concerned by a shorter time to confirmation of the pain response end point than previously announced and a lower starting dose. That said, Lazard Capital Markets biotech analyst Ryan Martins believes cabozantinib will be active and di! fferentiated from its competitors. Founded to pursue drug discovery using genomics and one of biotech's notable initial public offerings in 2000, Exelixis has 11 outlicensed or partnered clinical-stage compounds. But internally, it has narrowed its focus to developing cabozantinib. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Author Details * Mark Ratner Search for this author in: * NPG journals * PubMed * Google Scholar Additional data Author Details * Mark Ratner Search for this author in: * NPG journals * PubMed * Google Scholar
• Safety profiles come to fore as more drugs approach MS market
  - Nat Biotechnol 30(1):6-8 (2012)
  Article preview View full access options Nature Biotechnology | News Safety profiles come to fore as more drugs approach MS market * Cormac Sheridan1Journal name:Nature BiotechnologyVolume: 30,Pages:6–8Year published:(2012)DOI:doi:10.1038/nbt0112-6cPublished online09 January 2012 REUTERS/Mike Segar George Scangos, CEO of Biogen, is eager to strengthen the biotech's multiple sclerosis franchise. Now Biogen's oral molecule BG-12 is being touted as a potential game changer. Two long-awaited multiple sclerosis (MS) drugs, Sanofi's (Genzyme) alemtuzumab (Lemtrada) and Biogen Idec's BG-12 (dimethyl fumarate) are due to enter the approval process in the coming months, following the publication of promising phase 3 trial data last year (Table 1). Each has fast-track designation and could, with a priority review, reach patients in the US before the year is out. Approval for another Sanofi drug, teriflunomide (Aubagio), could come even earlier (Box 1). But some highlight Cambridge, Massachusetts–based Biogen Idec's oral small molecule BG-12 as the pipeline drug with the greatest potential to reconcile the twin goals of efficacy and safety. "I don't think everything out there is a game changer—but one possible exception is BG-12," says Lawrence Steinman, of Stanford University School of Medicine, California, adding "it's a game changer that's not a cure." Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Dublin * Cormac Sheridan Author Details * Cormac Sheridan Search for this author in: * NPG journals * PubMed * Google Scholar Additional data Affiliations * Dublin * Cormac Sheridan Author Details * Cormac Sheridan Search for this author in: * NPG journals * PubMed * Google Scholar
• Spinal device cancer risk
  - Nat Biotechnol 30(1):8 (2012)
  Article preview View full access options Nature Biotechnology | News Spinal device cancer risk * Emily WaltzJournal name:Nature BiotechnologyVolume: 30,Page:8Year published:(2012)DOI:doi:10.1038/nbt0112-8aPublished online09 January 2012 Medical device maker Medtronic is under investigation by the US Senate Finance Committee, a group of spine specialists and media outlets for its financial ties to doctors who downplayed serious side effects of the company's recombinant bone growth treatment. In 13 clinical studies, doctors did not link adverse events such as sterility, infection and bone loss to Infuse, a product used in spinal fusion surgery, according to research published in the June 2011 issue of The Spine Journal. The doctors were also aware of, and did not report, critical information linking a stronger dose of the drug to cancer, according to an October media review of Food and Drug Administration documents. The Senate committee has demanded that Medtronic, based in Minneapolis, turn over records of communication with and payments to the doctors who authored the clinical studies. The doctors were paid a median of $12 million to $16 million each over ten years in consulting and royalty fees for various! Medtronic products. "With those numbers you can be sure you've got a surrogate marketing department," says Arthur Caplan, director of the Center for Bioethics at the University of Pennsylvania in Philadelphia. Infuse, a recombinant form of the protein BMP-2 (bone morphogenetic protein-2) approved by the FDA in 2002 earned Medtronic $900 million in its most recent fiscal year. The company in August gave $2.5 million to Yale University researchers to oversee reviews of all BMP-2 clinical data. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Author Details * Emily Waltz Search for this author in: * NPG journals * PubMed * Google Scholar Additional data Author Details * Emily Waltz Search for this author in: * NPG journals * PubMed * Google Scholar
• $352 million into microRNA
  - Nat Biotechnol 30(1):8 (2012)
  Article preview View full access options Nature Biotechnology | News $352 million into microRNA * Sabine LouëtJournal name:Nature BiotechnologyVolume: 30,Page:8Year published:(2012)DOI:doi:10.1038/nbt0112-8bPublished online09 January 2012 MicroRNA (miRNA) company Miragen Therapeutics of Boulder, Colorado, entered a $352 million licensing and development agreement with French pharma Servier Laboratories of Suresnes in October. Miragen will receive $45 million upfront, and research support and commercial milestones worth ~$307 million. The total deal value may reach $1 billion including royalties and product sales. Miragen's programs use Hoersholm, Denmark–based Santaris Pharma's Locked Nucleic Acid Drug Platform to identify and select drug candidates against Miragen's miRNA targets. The agreement with Servier focuses on two defined miRNA targets: miR-208, involved in heart failure pathogenesis and progression, and miR-15/195, involved in cardiomyocyte survival and proliferation. The deal also includes a third, undefined target that can either be taken from Miragen's pipeline or from jointly identified targets for cardiovascular disease in blood samples. "It is a good fit between [our] innovative microRNA t! echnology coupled with [Servier's] well-driven insight into the clinical development," says William Marshall, president and CEO of Miragen. Striking a deal with a mid-size pharma, such as Servier, "gives a little more flexibility," says Bird, "they are less likely to turn the light off if things don't go well." Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Author Details * Sabine Louët Search for this author in: * NPG journals * PubMed * Google Scholar Additional data Author Details * Sabine Louët Search for this author in: * NPG journals * PubMed * Google Scholar
• Russian fund steps up investments in innovative biotechs
  - Nat Biotechnol 30(1):9-11 (2012)
  Article preview View full access options Nature Biotechnology | News Russian fund steps up investments in innovative biotechs * Alla Katsnelson1Journal name:Nature BiotechnologyVolume: 30,Pages:9–11Year published:(2012)DOI:doi:10.1038/nbt0112-9aPublished online09 January 2012 REUTERS/RIA Novosti Russia's President Medvedev visits an exhibition of Skolkovo Centre projects. An agreement struck between a Russian fund and two US-based biotech startups is the latest example of Russia's desire to court Western biotech innovators. On October 27, the Moscow-based Russian Corporation of Nanotechnologies (Rusnano) fund invested $25 million each in BIND Biosciences, of Cambridge, Massachusetts, and Selecta Biosciences of Watertown, Massachusetts. As part of the deal, the two US nanobiotech firms will invest $22.25 million from new and current investors, and establish subsidiary facilities in Russia. The agreements enable BIND and Selecta to conduct drug development in Russia as well as gain a foothold in the Russian market. "I completely saw the double opportunity," says Selecta CEO Werner Cautreels. Lack of experience in taking products to market and bureaucratic holdups have kept foreign firms from engaging with Russia. But some organizational reforms geared towards helping the sector prosper are starting to build confidence, at a time when access! to the Russian clinic trial network is proving attractive to both Western biotechs and big pharma. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * New York * Alla Katsnelson Author Details * Alla Katsnelson Search for this author in: * NPG journals * PubMed * Google Scholar Additional data Affiliations * New York * Alla Katsnelson Author Details * Alla Katsnelson Search for this author in: * NPG journals * PubMed * Google Scholar
• Obama urges translation speed
  - Nat Biotechnol 30(1):9 (2012)
  Article preview View full access options Nature Biotechnology | News Obama urges translation speed * Jeffrey L FoxJournal name:Nature BiotechnologyVolume: 30,Page:9Year published:(2012)DOI:doi:10.1038/nbt0112-9bPublished online09 January 2012 Late last October, President Barack Obama directed all federal R&D agencies and departments to expedite and streamline their technology transfer efforts, urging them to form public-private research partnerships, issue small business R&D grants, and establish collaborations with university startups. The directive also gives agencies more flexibility to partner with industry and directs them to develop a five-year strategic plan to include the tracking of patents generated in federal laboratories. As part of this effort, the administration will soon expand an electronic portal called BusinessUSA (http://business.usa.gov/) to furnish information on pertinent federal services along with guidance for companies contemplating exporting goods and services. Although federal agencies such as the US National Institutes of Health (NIH) are pretty adept at managing technology transfer programs, others could make improvements that, in turn, could benefit biotech companies, particularly sm! aller ones, says Lila Feisee, vice president for global intellectual property policy at the Biotechnology Industry Organization (BIO) in Washington, DC. For example, tech transfer policies under the auspices of the National Laboratories, which are dispersed at several sites around the country, are not very uniform, sometimes complicating efforts for companies seeking to forge alliances, she says. This new effort towards uniformity is "not a bad thing" says Feisee. "How they go about streamlining is something we will have to watch." In a related development, the NIH issued an electronic catalog of research materials available for companies to license. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Author Details * Jeffrey L Fox Search for this author in: * NPG journals * PubMed * Google Scholar Additional data Author Details * Jeffrey L Fox Search for this author in: * NPG journals * PubMed * Google Scholar
• Monsanto to face biopiracy charges in India
  - Nat Biotechnol 30(1):11 (2012)
  Article preview View full access options Nature Biotechnology | News Monsanto to face biopiracy charges in India * Lucas LaursenJournal name:Nature BiotechnologyVolume: 30,Page:11Year published:(2012)DOI:doi:10.1038/nbt0112-11Published online09 January 2012 Mehrab/istockphoto Eggplants stir debate An Indian government agency has agreed to sue the developers of genetically modified (GM) eggplant for violating India's Biological Diversity Act of 2002. India's National Biodiversity Authority (NBA) is alleging that the developers of India's first GM food crop—Jalna-based Maharashtra Hybrid Seeds Company (Mahyco) partnered with St. Louis–based seed giant Monsanto and several local universities—used local varieties to develop the transgenic crop, but failed to gain the appropriate licenses for field trials. At the same time, activists in Europe are claiming that patents on conventionally bred plants, including a melon found in India, filed by biotech companies violate farmers' rights to use naturally occurring breeds. Both these pending legal cases could set important precedents for biopiracy in India and Europe. In another development in early November, the Munich-based European Patent Office referred to its Enlarged Board of Appeals a case involving conventionally b! red tomatoes, which will likely shape any future enforcement of the Monsanto-owned melon patent, says Christoph Then, spokesman for advocacy group No Patents on Seeds. "It is a signal that the European Patent Office has severe doubts about this kind of patent," he says. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Author Details * Lucas Laursen Search for this author in: * NPG journals * PubMed * Google Scholar Additional data Author Details * Lucas Laursen Search for this author in: * NPG journals * PubMed * Google Scholar
• Embryonic stem cell pioneer Geron exits field, cuts losses
  - Nat Biotechnol 30(1):12-13 (2012)
  Article preview View full access options Nature Biotechnology | News Embryonic stem cell pioneer Geron exits field, cuts losses * Simon Frantz1Journal name:Nature BiotechnologyVolume: 30,Pages:12–13Year published:(2012)DOI:doi:10.1038/nbt0112-12Published online09 January 2012 PROFESSOR MIODRAG STOJKOVIC/SCIENCE PHOTO LIBRARY Getting human embryonic stem cell–based products into the clinic is proving a Sisyphean task. Geron, the company that pioneered translational research into human embryonic stem cell (hESC) therapies, announced in November that it is dropping its entire program owing to financial concerns. The biotech firm, located in Menlo Park, California, revealed that it was halting its phase 1 trial of its hESC-derived oligodendrocyte progenitor cell product, GRNOPC1, in spinal cord injuries. It plans to lay off 66 of its 175 full-time staff and is seeking partners to take on the programs' assets. Given that the company has the most advanced hESC-based candidate in the pipeline and has raised the most money, its decision has left many people wondering where this leaves the field. "It's a psychological blow. What the field really needed was a big success," says Robert Lanza, CSO for Worcester, Massachusetts–based Advanced Cell Technology (ACT), one of the few remaining companies aiming to commercialize hESCs. Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * London * Simon Frantz Author Details * Simon Frantz Search for this author in: * NPG journals * PubMed * Google Scholar Additional data Affiliations * London * Simon Frantz Author Details * Simon Frantz Search for this author in: * NPG journals * PubMed * Google Scholar
• Around the world in a month
  - Nat Biotechnol 30(1):13 (2012)
  Article preview View full access options Nature Biotechnology | News Around the world in a month Journal name:Nature BiotechnologyVolume: 30,Page:13Year published:(2012)DOI:doi:10.1038/nbt0112-13Published online09 January 2012 Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data
• Foundation Medicine
  - Nat Biotechnol 30(1):14 (2012)
  Nature Biotechnology | News | Newsmaker Foundation Medicine * Michael EisensteinJournal name:Nature BiotechnologyVolume: 30,Page:14Year published:(2012)DOI:doi:10.1038/nbt0112-14Published online09 January 2012 The Cambridge, Massachusetts–based company plans to introduce next-generation gene sequencing into oncology practice and pathologists' laboratories. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Author Details * Michael Eisenstein Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• Drug pipeline: Q411
  - Nat Biotechnol 30(1):15 (2012)
  Article preview View full access options Nature Biotechnology | News | Data Page Drug pipeline: Q411 * H Craig Mak1Journal name:Nature BiotechnologyVolume: 30,Page:15Year published:(2012)DOI:doi:10.1038/nbt.2090Published online09 January 2012 Despite several big pharmas leaving neurology, the drug pipeline is the second largest after oncology. The FDA approved Hemacord, the first off-the-shelf human cord blood cell product. Positive efficacy was also obtained for cell therapies in congestive heart failure (MyoCell) and limb ischemia (Imyelocel-T). Panaclar and ocrelizumab met their end points in multiple sclerosis as did DiaPep277 in type I diabetes. The oncolytic treatment Reolysin showed positive phase 2 results in head and neck cancer. On the downside, Sangamo Biosciences' lead zinc-finger program was discontinued. Notable regulatory approvals (Q4 2011) Box 1: Notable regulatory approvals (Q4 2011) Full box Notable regulatory setbacks (Q4 2011) Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * H. Craig Mak is Associate Editor of Nature Biotechnology Author Details * H Craig Mak Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• The NIH's role in accelerating translational sciences
  - Nat Biotechnol 30(1):16-19 (2012)
  Nature Biotechnology | News | Opinion The NIH's role in accelerating translational sciences * John C Reed1 * E Lucile White2 * Jeffrey Aubé3 * Craig Lindsley4 * Min Li5 * Larry Sklar6 * Stuart Schreiber7 * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 30,Pages:16–19Year published:(2012)DOI:doi:10.1038/nbt.2087Published online09 January 2012 The NIH's proposed initiatives in translational science deserve solid financial backing from legislators and vocal support from the biomedical community. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * John C. Reed is at the Sanford-Burnham Medical Research Institute, La Jolla, California, USA. * E. Lucile White is at the High-Throughput Screening Center and Enzymology Laboratory, Southern Research Institute, Birmingham, Alabama, USA. * Jeffrey Aubé is at the Department of Medicinal Chemistry, Kentucky University, Lawrence, Kansas, USA. * Craig Lindsley is in the Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, Tennessee, USA. * Min Li is in the Department of Neuroscience, The Johns Hopkins School of Medicine, Baltimore, Maryland, USA. * Larry Sklar is at the Center for Molecular Discovery, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA. * Stuart Schreiber is in the Department of Chemical Biology, Broad Institute, Cambridge, Massachusetts, USA. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * John C Reed Author Details * John C Reed Contact John C Reed Search for this author in: * NPG journals * PubMed * Google Scholar * E Lucile White Search for this author in: * NPG journals * PubMed * Google Scholar * Jeffrey Aubé Search for this author in: * NPG journals * PubMed * Google Scholar * Craig Lindsley Search for this author in: * NPG journals * PubMed * Google Scholar * Min Li Search for this author in: * NPG journals * PubMed * Google Scholar * Larry Sklar Search for this author in: * NPG journals * PubMed * Google Scholar * Stuart Schreiber Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• Building stem-cell genomics in California and beyond
  - Nat Biotechnol 30(1):20-25 (2012)
  Nature Biotechnology | News | Opinion Building stem-cell genomics in California and beyond * Natalie D DeWitt1 * Michael P Yaffe1 * Alan Trounson1 * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 30,Pages:20–25Year published:(2012)DOI:doi:10.1038/nbt.2086Published online09 January 2012 By devoting funding to whole-genome studies, such as epigenetic and copy-number variation in stem cells, research on new genomic technology, and standards for methodologies and data collection/sharing, CIRM can spur both basic and translational research. View full text Figures at a glance * Figure 1: Technology improvements in DNA-sequencing technology are outpacing Moore's Law (the computing industry's trend of doubling computer power every two years; adapted from http://www.genome.gov/sequencingcosts/; accessed 12/06/11). * Figure 2: A flow chart depicting major stages of the proposed stem-cell genomics pipeline. From left to right: conception of genomics project from clinical, manufacturing or basic research scientists; sample preparation; next-generation sequencing or deep-sequencing technologies; single-cell analytics (where applicable); data handling and analysis, and transfer of information back to scientists and clinicians. Bottlenecks that are potentially opened by CIRM funding of innovation or provision of resources are indicated by the lightning bolts. * Figure 3: Benefits of genomics data for a broad range of activities, from basic research to clinical applications. Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Natalie D. DeWitt, Michael P. Yaffe & Alan Trounson are at the California Institute for Regenerative Medicine, San Francisco, California, USA. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Natalie D DeWitt Author Details * Natalie D DeWitt Contact Natalie D DeWitt Search for this author in: * NPG journals * PubMed * Google Scholar * Michael P Yaffe Search for this author in: * NPG journals * PubMed * Google Scholar * Alan Trounson Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• Approval on a knife edge
  - Nat Biotechnol 30(1):26-29 (2012)
  Nature Biotechnology | News Feature Approval on a knife edge * Michael Eisenstein1Journal name:Nature BiotechnologyVolume: 30,Pages:26–29Year published:(2012)DOI:doi:10.1038/nbt.2084Published online09 January 2012 In spite of its modest performance in clinical trials, Benlysta may offer effective relief against lupus. But physicians are still working to identify the right patients. Michael Eisenstein reports. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Philadelphia * Michael Eisenstein Author Details * Michael Eisenstein Search for this author in: * NPG journals * PubMed * Google Scholar Additional data Affiliations * Philadelphia * Michael Eisenstein Author Details * Michael Eisenstein Search for this author in: * NPG journals * PubMed * Google Scholar
• Bringing business risk into sharp focus
  - Nat Biotechnol 30(1):30-32 (2012)
  Nature Biotechnology | Bioentrepreneur Bringing business risk into sharp focus * Bill Gruber1 * Emily Walsh2 * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 30,Pages:30–32Year published:(2012)DOI:doi:10.1038/nbt.2081Published online09 January 2012 Abstract * Abstract * Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Comprehensive due diligence on research constraints, regulatory and reimbursement barriers, as well as patient and physician preferences is important to increase the chances of success for a life science business. View full text Author information * Abstract * Author information Affiliations * Bill Gruber is CEO at Solace Therapeutics, Framingham, Massachusetts, USA. * Emily Walsh is principal consultant, Halloran Consulting Group, Waltham, Massachusetts, USA. Corresponding author Correspondence to: * Emily Walsh Author Details * Bill Gruber Search for this author in: * NPG journals * PubMed * Google Scholar * Emily Walsh Contact Emily Walsh Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• Big data in small places
  - Nat Biotechnol 30(1):33-34 (2012)
  Article preview View full access options Nature Biotechnology | Correspondence Big data in small places * Daniel MacLean1 * Sophien Kamoun1 * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 30,Pages:33–34Year published:(2012)DOI:doi:10.1038/nbt.2079Published online09 January 2012 To the Editor: Recently, several articles have focused on the need for flexible, scalable approaches to bioinformatics provision in smaller research institutes and university departments1, 2, 3. As a small institute of around 80 researchers, the Sainsbury Laboratory (Norwich, UK) has been working for the past four years to adapt to the influx of big data sets from high-throughput approaches. In that time, we have successfully transitioned from a 'top-down' model of bioinformatics provision to a 'bottom up' model that incorporates several features discussed in recent articles1, 2, 3. As a result, we have sped up the analysis cycle and can now handle increasing workloads in a timely, productive manner with a modest core support team. Here we provide a description of how we achieved this upgrade in the hope that our experience will prove useful for other small institutions seeking to address the informatics challenges posed by large-scale biological research approaches. Dealing with big data sets can be abstracted into three main tasks: we must be able to manage, understand and analyze. 'Managing' is to carry out the computer science–based transfer and storage of data. 'Understanding' implies a clear knowledge of the biological context and caveats of the data as well as the functioning and limitations of the methods. And 'analyzing' refers to the application of the various bioinformatics methods to specific biological questions and data. Our support model distributes labor between bioinformaticians and bench scientists to optimize the delivery of these three tasks. To ensure that data handling runs smoothly, bioinformaticians can help bench researchers by removing the burden of worrying about the mechanics of dealing with the data that their experiments produce. We have found that several simple tools and tricks reduce the perceived barrier to access and improve data management. Mounting storage devices directly to desktop machines by means of a secure shell (SSH) file system (http://en.wikipedia.org/wiki/SSHFS) makes it possible for terabytes of data to be accessed as if they were on a USB stick. Acting as 'lab manager' to the produced data by providing local rules about data descriptions and providing tools to make sure files will validate against these rules helps maintain order over the produced data. Tools like Galaxy4 (our favored workflow-engineering environment) lower the barrier to access by allowing a user to create and share complex analysis pipelines through a straightforward graphical user interface (GUI). In parallel, bio! informaticians can develop tools for immediate deployment in a familiar and flexible framework. For the majority of research projects, bioinformatics can be considered a subdiscipline of molecular biology and biologists must learn bioinformatics methods along side basic wet-lab methods. Given proper training and demystification of what bioinformatics methods actually are, biologists are perfectly capable of working their own informatics. A critical advantage of this model is that bioinformatics and biological concepts are now being thought of by the same brain, which significantly accelerates project turnover and reduces the likelihood of missed insights and misunderstandings. In our experience, many biologists initially approach bioinformatics methods as a set of black box tricks in which the basic rules of rigorous experimentation somehow don't apply. Perhaps it is the mathematical comfort zone provided by E-scores and P-values that gives a false sense of absolute accuracy to the results of bioinformatics analyses, but it is puzzling how careful bench biologists turn into naive experimentalists once they sit at the computer. Bioinformaticians can have the strongest effect on proper use of bioinformatics practices by helping in the design and execution of experiments and controls. A particularly relevant example stems from our experience with the detection of single-nucleotide polymorphisms (SNPs) from next-generation DNA sequence data. We have completed several projects cataloging genetic variation in microbes and plants and using next-generation sequencing alignment and SNP calling algorithms. None of the SNP identification programs give perfect results so the amount of error must be quantified. However, this serious limitation is not initially obvious to a biologist whose main focus is the end goal of generating lists of SNPs. In-depth explanations of the methods may not help as they can mire the discussion in statistical or technical details not fully appreciated by the biologist. We approach this problem by encouraging the use of controls to demonstrate and estimate the error rates, for example, by computationally introducing SNPs into a reference sequence and showing the extent to which recall of these SNPs is accurate (see Supplementary Fig. S2! and methods in ref. 5). It is our experience as informaticians, that such an exercise usually has a profound impact on our biology colleagues because they appreciate the value of controlled experimentation and informed criticism of data. It unequivocally demonstrates that bioinformatics methods have error. It frees experimentalists to see the approach as just another way of estimating something and to approach bioinformatics as a set of methods that can be dissected with the familiar knife of experimentation. We have seen that the attitude of 'bioinformatics as assay' propagates rapidly within a research group. Once a concept is adopted in laboratory meetings and research discussions and the issues are explained by biologists to other biologists, we reach a virtuous cycle that is self-reinforcing in a laboratory. Our ultimate aim is that the biologists use bioinformatics in a mature and critically aware way. A key to achieving this sea change is to sustain a productive working dialog between the two parties by giving the biologist the vocabulary needed to work in the field and discuss issues as a peer of the bioinformatician. At the Sainsbury Laboratory, we focus on training and in getting our bioinformaticians to discuss their tricks and toys in a relaxed yet formal fashion. We began by implementing a wide range of courses aimed at the novice but covering enough ground to introduce all the vital aspects of each topic. Specifically, we teach introductory courses on broad topics like de novo assembly, RNA-Seq and so forth. Advanced training in things like command-line use, scripting languages and statistics are always useful for a smaller number of biologists—research is unpredictable and often existing tools with a GUI will lag behind the cutting edge in a way that researchers don't want to. The ability to run a brand new tool on the command-line and parse its output with a sm! all custom script is an excellent advantage for researchers who need the cutting edge right away. After formal training sessions, follow-up is vital. Help and resources should be available on-demand and the trainer needs to operate an open-door policy for questions. Answers to questions and discussions on request will help to prevent the learner's enthusiasm from stalling early on. Wider laboratory culture changes can be sustained and extended through a range of familiar exercises and resources. Journal club meetings specifically designed to tackle discrete bioinformatics topics help enormously to reinforce awareness of what is being done in the field and what the details of execution are. Laboratory meetings in which the biologist presents their bioinformatics work to an interested audience provide a vital opportunity to develop critical appraisal of informatics methods. Our approach has borne fruit. We have found that when biologists are able to handle part or all of the bioinformatics load on their projects, our productivity increases (Fig. 1). The turnover of jobs run on our computer cluster increased substantially after opening it up to trained biologists. The number of concurrent bioinformatics projects we are now handling is high, too. In total, 25% of our researchers (20 individuals) are now actively involved in running their own bioinformatics projects, way above the 2.5% (two researchers) the old model permitted. The number of biologists, not the size of the core support team, limits the number of projects that we can handle. Extra analysis capacity can now be brought in at the project level when hiring new biologists and is not throttled by the size of the core support team; in addition, the expertise can scale as the number of projects requiring bioinformatics methods being carried out increases. Figure 1: Bioinformatics jobs done and methods used at the Sainsbury Laboratory. () Our model increases productivity: we see a general rise in the bioinformatics jobs being run per hour and per day (t-test, P = 0.027), 'before' and 'after' we implemented our new systems for data management, our Galaxy instance and carried out training. () We have many biologists using many bioinformatics approaches; each color represents a user, each bar the cumulative number of projects for a method. Most biologists work on two to four informatics projects, applying two to three methods. HMM, hidden Markov model. * Full size image (72 KB) Computing power can be a limitation to bioinformatics, but this problem is not as acute in smaller institutions as it is in larger sequencing centers. A massive infrastructure investment is not necessary and it is possible to provide expandable computing infrastructure. At the Sainsbury Laboratory, when the small core team was responsible for the majority of work with our hardware, there was often a lot of spare processing capacity and analyses did not run flat-out. With job-scheduling software, however, modest computing clusters can be made to support the activities of many researchers by distributing resources evenly through time. It is possible to acquire for moderate costs a few high-powered servers and a storage device that can be built into a cluster easily. Well-designed clusters can be expanded by adding new servers and extra disks to storage appliances whenever projects require it. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * The Sainsbury Laboratory, Norwich Research Park, Norwich, UK. * Daniel MacLean & * Sophien Kamoun Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Daniel MacLean Author Details * Daniel MacLean Contact Daniel MacLean Search for this author in: * NPG journals * PubMed * Google Scholar * Sophien Kamoun Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• Exploiting host molecules to augment mycoinsecticide virulence
  - Nat Biotechnol 30(1):35-37 (2012)
  Nature Biotechnology | Correspondence Exploiting host molecules to augment mycoinsecticide virulence * Yanhua Fan1, 2 * Dov Borovsky3 * Chloe Hawkings3 * Almudena Ortiz-Urquiza2 * Nemat O Keyhani2 * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 30,Pages:35–37Year published:(2012)DOI:doi:10.1038/nbt.2080Published online09 January 2012 To the Editor: A pressing need exists for additional tools in insect control, particularly as few new chemical pesticides are under development. Entomopathogenic fungi, such as Metarhizium anisopliae and Beauveria bassiana, both US Environmental Protection Agency (EPA)-approved biological control agents, offer an environmentally friendly alternative to chemical insecticides. One limitation to the use of entomopathogenic fungi is the relatively long time (6–12 days) it takes for the fungus to kill target insects. Expression of a scorpion toxin in M. anisopliae increases fungal toxicity about ninefold toward the yellow fever mosquito, Aedes aegypti1; however, expression of nonspecies-specific toxins to control mosquitoes may promote the development of toxin resistance. Ideally, a strain with enhanced virulence toward target insects with minimal nontarget effects, coupled to a decreased likelihood of the development of resistance to the agent is most desired. To meet this objective, we repo! rt a novel approach to insect control, in which expression of host molecules in an insect pathogen is exploited for augmentation of virulence. View full text Author information * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Biotechnology Research Center, Southwest University, Beibei, Chongqing, P.R. China. * Yanhua Fan * Department of Microbiology and Cell Science, University of Florida, Gainesville, Florida, USA. * Yanhua Fan, * Almudena Ortiz-Urquiza & * Nemat O Keyhani * Florida Medical Entomology Laboratory, University of Florida, Vero Beach, Florida, USA. * Dov Borovsky & * Chloe Hawkings Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Nemat O Keyhani Author Details * Yanhua Fan Search for this author in: * NPG journals * PubMed * Google Scholar * Dov Borovsky Search for this author in: * NPG journals * PubMed * Google Scholar * Chloe Hawkings Search for this author in: * NPG journals * PubMed * Google Scholar * Almudena Ortiz-Urquiza Search for this author in: * NPG journals * PubMed * Google Scholar * Nemat O Keyhani Contact Nemat O Keyhani Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (766k) Supplementary Methods, Supplementary Figures 1–3 Additional data
• European discussion forum on transgenic tree biosafety
  - Nat Biotechnol 30(1):37-38 (2012)
  Article preview View full access options Nature Biotechnology | Correspondence European discussion forum on transgenic tree biosafety * Matthias Fladung1 * Illimar Altosaar2 * Detlef Bartsch3 * Marie Baucher4 * Fabio Boscaleri5 * Fernando Gallardo6 * Hely Häggman7 * Hans Hoenicka1 * Kaare Nielsen8 * Donatella Paffetti9 * Armand Séguin10 * Guenther Stotzky11 * Cristina Vettori12 * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 30,Pages:37–38Year published:(2012)DOI:doi:10.1038/nbt.2078Published online09 January 2012 To the Editor: Since the first published description of a genetically modified (GM) poplar by Fillatti and co-workers1 in 1987, the environmental release of GM trees has spurred public debate worldwide, particularly in the context of commercialization. Although the issues raised in public discussion are similar in many countries, marked political differences have emerged in various countries as to the acceptability of field applications of GM trees, the amount of public funding devoted to GM tree research and the regulatory priorities regarding biosafety issues. Consequently, a large but diverse and fragmented body of knowledge on the environmental interactions and safety of transgenic trees and other (transgenic) organisms has been acquired over the past 25 years. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Johann Heinrich von Thuenen Institute, Institute of Forest Genetics, Grosshansdorf, Germany. * Matthias Fladung & * Hans Hoenicka * University of Ottawa, Biochemistry, Microbiology & Immunology Department, Faculty of Medicine, Ottawa, Ontario, Canada. * Illimar Altosaar * Federal Office of Consumer Protection and Food Safety, Unit 404: Coexistence, GMO-Monitoring, Berlin, Germany. * Detlef Bartsch * Laboratoire de Biotechnologie Végétale, Université Libre de Bruxelles, Gosselies, Belgium. * Marie Baucher * Regione Toscana, Regional Government, DG Competitiveness and Development, Florence, Italy. * Fabio Boscaleri * Universidad de Málaga, Departamento de Biología Molecular y Bioquímica, Facultad de Ciencias e Instituto Andaluz de Biotecnología, Málaga, Spain. * Fernando Gallardo * University of Oulu, Department of Biology, Oulu, Finland. * Hely Häggman * University of Tromsø, Department of Pharmacy, Tromsø, Norway. * Kaare Nielsen * University of Florence, Department of Agricultural and Forest Economics, Engineering, Sciences and Technologies, Florence, Italy. * Donatella Paffetti * Natural Resources Canada, Québec, Canada. * Armand Séguin * Department of Biology, New York University, New York, New York, USA. * Guenther Stotzky * Plant Genetics Institute, Division of Florence, National Research Council, Florence, Italy. * Cristina Vettori Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Matthias Fladung Author Details * Matthias Fladung Contact Matthias Fladung Search for this author in: * NPG journals * PubMed * Google Scholar * Illimar Altosaar Search for this author in: * NPG journals * PubMed * Google Scholar * Detlef Bartsch Search for this author in: * NPG journals * PubMed * Google Scholar * Marie Baucher Search for this author in: * NPG journals * PubMed * Google Scholar * Fabio Boscaleri Search for this author in: * NPG journals * PubMed * Google Scholar * Fernando Gallardo Search for this author in: * NPG journals * PubMed * Google Scholar * Hely Häggman Search for this author in: * NPG journals * PubMed * Google Scholar * Hans Hoenicka Search for this author in: * NPG journals * PubMed * Google Scholar * Kaare Nielsen Search for this author in: * NPG journals * PubMed * Google Scholar * Donatella Paffetti Search for this author in: * NPG journals * PubMed * Google Scholar * Armand Séguin Search for this author in: * NPG journals * PubMed * Google Scholar * Guenther Stotzky Search for this author in: * NPG journals * PubMed * Google Scholar * Cristina Vettori Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• Factors influencing agbiotech adoption and development in sub-Saharan Africa
  - Nat Biotechnol 30(1):38-40 (2012)
  Nature Biotechnology | Correspondence Factors influencing agbiotech adoption and development in sub-Saharan Africa * Obidimma C Ezezika1 * Abdallah S Daar1 * Kathryn Barber1 * Justin Mabeya1 * Fiona Thomas1 * Jennifer Deadman1 * Debbie Wang1 * Peter A Singer1 * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 30,Pages:38–40Year published:(2012)DOI:doi:10.1038/nbt.2088Published online09 January 2012 To the Editor: Despite the technical knowledge available for improving food security in sub-Saharan Africa (SSA), only three African countries (South Africa, Egypt and Burkina Faso) have commercialized biotech crops to date1. An important step toward improving agbiotech development and genetically modified (GM) crop adoption is to understand the factors that affect the transition of new agbiotech products from the product development stage, through commercialization to the hands of farmers and ultimate consumption by the population. As part of a broader study on a social audit preparation for the Water Efficient Maize for Africa Project, we conducted 91 interviews with agbiotech stakeholders from a diverse range of groups within five SSA countries (Supplementary Methods). Analysis of the recordings of these interviews revealed four recurring factors that appear to influence agbiotech development in SSA: communication, culture and religion, capacity building and commercialization (Fig. 1). ! We expand in more detail on these factors below. View full text Author information * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * McLaughlin-Rotman Centre for Global Health, University Health Network and University of Toronto, Toronto, Ontario, Canada. * Obidimma C Ezezika, * Abdallah S Daar, * Kathryn Barber, * Justin Mabeya, * Fiona Thomas, * Jennifer Deadman, * Debbie Wang & * Peter A Singer Competing financial interests Authors received grant from Gates Foundation to study the water-efficient maize mentioned in the paper. Corresponding author Correspondence to: * Peter A Singer Author Details * Obidimma C Ezezika Search for this author in: * NPG journals * PubMed * Google Scholar * Abdallah S Daar Search for this author in: * NPG journals * PubMed * Google Scholar * Kathryn Barber Search for this author in: * NPG journals * PubMed * Google Scholar * Justin Mabeya Search for this author in: * NPG journals * PubMed * Google Scholar * Fiona Thomas Search for this author in: * NPG journals * PubMed * Google Scholar * Jennifer Deadman Search for this author in: * NPG journals * PubMed * Google Scholar * Debbie Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Peter A Singer Contact Peter A Singer Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (37k) Supplementary Methods Additional data
• Reinventing clinical trials
  - Nat Biotechnol 30(1):41-49 (2012)
  Nature Biotechnology | Feature Reinventing clinical trials * Malorye Allison1Journal name:Nature BiotechnologyVolume: 30,Pages:41–49Year published:(2012)DOI:doi:10.1038/nbt.2083Published online09 January 2012 As R&D costs spiral for drug developers, disruptive approaches to clinical trial design and management are gaining traction. Get ready for electronic data capture, precompetitive data sharing, virtual trials and a variety of bold new paradigms. View full text Figures at a glance * Figure 1: Reasons for clinical attrition. () Failures in phase 2. () Failures in phase 3. (Reprinted from refs. 2 () and 3 ().) * Figure 2: FDA drug approvals per year. (Reprinted from ref. 24.) * Figure 3: Your clinical trial is texting you. Exco InTouch eDiary tool provides a mobile technology platform with a user-friendly interface to make participating in trials easy, even remotely. Patients complete diary questionnaires through a series of text messages sent through their own cell phones. (Source: Exco InTouch) * Figure 4: Density of clinical trials worldwide. Trial density is indicated by color, with the darker color having higher densities. Annual growth rate is indicated for some countries. (Reprinted from ref. 25.) Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Malorye Allison is a freelance writer based in Acton, Massachusetts, USA. Author Details * Malorye Allison Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• Maintaining patents protecting biologics or small-molecule drugs
  - Nat Biotechnol 30(1):50-53 (2012)
  Nature Biotechnology | Feature | Patents Maintaining patents protecting biologics or small-molecule drugs * Ganesan Marimuthu1 * Sangita Kumari1 * Muthukrishnakumar Kandasamy1 * Srivatsan Raghunathan1 * Gayatri Saberwal1 * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 30,Pages:50–53Year published:(2012)DOI:doi:10.1038/nbt.2082Published online09 January 2012 A look at the maintenance rates of US patents protecting FDA-approved biologics and small molecules. View full text Author information * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Ganesan Marimuthu, Sangita Kumari, Muthukrishnakumar Kandasamy, Srivatsan Raghunathan and Gayatri Saberwal are at the Institute of Bioinformatics and Applied Biotechnology, Bangalore, India. Competing financial interests This work has been funded by Institut Merieux, which has commerical interests in the biomedical sector. Corresponding author Correspondence to: * Gayatri Saberwal Author Details * Ganesan Marimuthu Search for this author in: * NPG journals * PubMed * Google Scholar * Sangita Kumari Search for this author in: * NPG journals * PubMed * Google Scholar * Muthukrishnakumar Kandasamy Search for this author in: * NPG journals * PubMed * Google Scholar * Srivatsan Raghunathan Search for this author in: * NPG journals * PubMed * Google Scholar * Gayatri Saberwal Contact Gayatri Saberwal Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (844K) Supplementary Tables 1–3 Additional data
• Recent patent applications related to gene and DNA synthesis
  - Nat Biotechnol 30(1):54 (2012)
  Article preview View full access options Nature Biotechnology | Feature | Patents Recent patent applications related to gene and DNA synthesis Journal name:Nature BiotechnologyVolume: 30,Page:54Year published:(2012)DOI:doi:10.1038/nbt.2098Published online09 January 2012 Article preview Read the full article * Instant access to this article: US$32 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data
• Parallel genome universes
  - Nat Biotechnol 30(1):55-56 (2012)
  Article preview View full access options Nature Biotechnology | News and Views Parallel genome universes * Tom Misteli1Journal name:Nature BiotechnologyVolume: 30,Pages:55–56Year published:(2012)DOI:doi:10.1038/nbt.2085Published online09 January 2012 A new computational approach gives us the best chance at understanding how genomes are arranged in three-dimensional space and what that may mean for their function. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Tom Misteli is at the National Cancer Institute, US National Institutes of Health, Bethesda, Maryland, USA. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Tom Misteli Author Details * Tom Misteli Contact Tom Misteli Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• Dopaminergic neurons for Parkinson's therapy
  - Nat Biotechnol 30(1):56-58 (2012)
  Article preview View full access options Nature Biotechnology | News and Views Dopaminergic neurons for Parkinson's therapy * Olle Lindvall1Journal name:Nature BiotechnologyVolume: 30,Pages:56–58Year published:(2012)DOI:doi:10.1038/nbt.2077Published online09 January 2012 A differentiation protocol guided by developmental principles produces more-authentic dopaminergic neurons for transplantation in patients. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Olle Lindvall is at the Wallenberg Neuroscience Center and Lund Stem Cell Center, University Hospital, Lund, Sweden. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Olle Lindvall Author Details * Olle Lindvall Contact Olle Lindvall Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• New competition in RNA regulation
  - Nat Biotechnol 30(1):58-59 (2012)
  Article preview View full access options Nature Biotechnology | News and Views New competition in RNA regulation * Anastasia Khvorova1 * Alexey Wolfson2 * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 30,Pages:58–59Year published:(2012)DOI:doi:10.1038/nbt.2092Published online09 January 2012 An additional layer of RNA regulation in which RNAs encoded by genes and pseudogenes compete for microRNAs could offer new opportunities for oligonucleotide therapeutics. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Biotechnology for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * Rent this article from DeepDyve * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Anastasia Khvorova is at RXi Pharmaceuticals, Worcester, Massachusetts, USA. * Alexey Wolfson is at the University of Massachusetts Medical School, Worcester, Massachusetts, USA. Competing financial interests A.K. is employed by RXi Pharmaceuticals, Worcester, Massachusetts, USA. Corresponding author Correspondence to: * Anastasia Khvorova Author Details * Anastasia Khvorova Contact Anastasia Khvorova Search for this author in: * NPG journals * PubMed * Google Scholar * Alexey Wolfson Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• Optimized filtering reduces the error rate in detecting genomic variants by short-read sequencing
  - Nat Biotechnol 30(1):61-68 (2012)
  Nature Biotechnology | Computational Biology | Analysis Optimized filtering reduces the error rate in detecting genomic variants by short-read sequencing * Joke Reumers1, 2, 12 * Peter De Rijk3, 4, 12 * Hui Zhao1, 2 * Anthony Liekens3, 4 * Dominiek Smeets1, 2 * John Cleary5 * Peter Van Loo6, 7 * Maarten Van Den Bossche3, 4, 8, 9 * Kirsten Catthoor10 * Bernard Sabbe8, 9 * Evelyn Despierre11 * Ignace Vergote11 * Brian Hilbush5 * Diether Lambrechts1, 2, 12 * Jurgen Del-Favero3, 4, 12 * Affiliations * Contributions * Corresponding authorsJournal name:Nature BiotechnologyVolume: 30,Pages:61–68Year published:(2012)DOI:doi:10.1038/nbt.2053Received08 June 2011Accepted28 October 2011Published online18 December 2011 Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Distinguishing single-nucleotide variants (SNVs) from errors in whole-genome sequences remains challenging. Here we describe a set of filters, together with a freely accessible software tool, that selectively reduce error rates and thereby facilitate variant detection in data from two short-read sequencing technologies, Complete Genomics and Illumina. By sequencing the nearly identical genomes from monozygotic twins and considering shared SNVs as 'true variants' and discordant SNVs as 'errors', we optimized thresholds for 12 individual filters and assessed which of the 1,048 filter combinations were effective in terms of sensitivity and specificity. Cumulative application of all effective filters reduced the error rate by 290-fold, facilitating the identification of genetic differences between monozygotic twins. We also applied an adapted, less stringent set of filters to reliably identify somatic mutations in a highly rearranged tumor and to identify variants in the NA19240! HapMap genome relative to a reference set of SNVs. View full text Figures at a glance * Figure 1: Development of individual filters on monozygotic twin genomes. Under the assumption that the number of actual differences between the monozygotic twins is very low, we calculated all discordant and shared SNVs between the twins. We considered discordant SNVs as errors and tested every filter for its capacity to selectively reduce discordances, while keeping as many of the shared variants as possible. Three types of filters were developed: (i) filters removing regions of inferior sequencing quality (quality filters), (ii) filters based on intrinsic genome characteristics (repetitive DNA filters), and (iii) filters selecting variants identified with an independent mapping and SNV calling method (consensus filters). The best individual filters were subsequently combined to remove a maximum number of discordances in the twin genomes. The same rationale was applied on the Yoruban NA19240 genome sequenced by CG and Illumina. The allelic imbalance filter was only applied on Illumina data, whereas the uncertain calls filter was only developed f! or CG data. * Figure 2: Efficacy of the individual filters with respect to the number of shared and discordant SNVs in monozygotic twins (CG filters) and NA19240 genomes (Illumina filters). (,) The fraction of SNVs removed by every filter. Shown is the percentage of discordant (Fdiff, light gray) and shared (Fshared, dark gray) variants removed by every filter. (,) The ratio Fdiff/Fshared is a measure of the specificity of the individual filters. Filters were considered effective if they removed twice the fraction of discordant versus shared variants (ratio Fdiff/Fshared > 2; dashed line). Filters that failed this criterion are denoted with an asterisk (*). (,) The fraction of the reference genome removed by every filter (Fgenome). Data stratified into transcriptome, conserved noncoding and nonconserved noncoding regions are given in Supplementary Note 2. Before filtering, a fraction of the reference genome was not sequenced in both samples (that is, 4.5% for the twins and 4.4% for the NA19240 genomes), representing the lower limit of the Fgenome (dashed line). * Figure 3: ROC curves of all filter combinations. () Results of applying filter combinations to monozygotic twin genomes. Each combination is represented by a circle that is sized and colored according to the number of filters combined. The circle's position indicates the fractions of shared SNVs and discordant SNVs that remained after applying the combination. Shared SNVs represent true variants, and discordant SNVs indicate errors. The combination with the best MCC value (that is, the circle closest to the left-upper corner) consisted of three filters: the near-an-indel, uncertain calls and microsatellite filter. () Results of filtering NA19240 genomes. Circles drawn as in . The combination with the best MCC value comprised the consensus mapping and calling filter, near-an-indel filter and variant score filter. For both and , the effect of individual filter combinations relative to all other filter combinations at different filter thresholds can be assessed at http://genomecomb.sourceforge.net/publications/filters/filters! election.php. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions GenBank * EGAS00001000158 * EGAS00001000152 Author information * Abstract * Accession codes * Author information * Supplementary information Primary authors * These author contributed equally to this work. * Joke Reumers, * Peter De Rijk, * Diether Lambrechts & * Jurgen Del-Favero Affiliations * Vesalius Research Center, Vlaams Instituut voor Biotechnologie (VIB), Leuven, Belgium. * Joke Reumers, * Hui Zhao, * Dominiek Smeets & * Diether Lambrechts * Vesalius Research Center, University of Leuven, Leuven, Belgium. * Joke Reumers, * Hui Zhao, * Dominiek Smeets & * Diether Lambrechts * Applied Molecular Genomics Group, Department of Molecular Genetics, VIB, Antwerp, Belgium. * Peter De Rijk, * Anthony Liekens, * Maarten Van Den Bossche & * Jurgen Del-Favero * Applied Molecular Genomics Group, University of Antwerp, Antwerp, Belgium. * Peter De Rijk, * Anthony Liekens, * Maarten Van Den Bossche & * Jurgen Del-Favero * Real Time Genomics, San Francisco, California, USA. * John Cleary & * Brian Hilbush * Department of Molecular and Developmental Genetics, VIB, Leuven, Belgium. * Peter Van Loo * Department of Human Genetics, University of Leuven, Leuven, Belgium. * Peter Van Loo * Collaborative Antwerp Psychiatric Research Institute (CAPRI), Faculty of Medicine, University of Antwerp, Antwerp, Belgium. * Maarten Van Den Bossche & * Bernard Sabbe * PC Sint-Norbertushuis, Duffel, Belgium. * Maarten Van Den Bossche & * Bernard Sabbe * ZNA Psychiatric Hospital Stuivenberg, Antwerp, Belgium. * Kirsten Catthoor * Division of Gynaecologic Oncology, Department of Obstetrics and Gynaecology, University Hospital Gasthuisberg, Leuven, Belgium. * Evelyn Despierre & * Ignace Vergote Contributions D.L. and J.D.-F. conceptualized this work. J.R. and P.D.R. wrote algorithms and analyzed data. H.Z. analyzed the Yoruban genome, A.L. assisted with the twin analysis. J.C. and B.H. performed RTG-related analyses. P.V.L. provided the ASCAT algorithm, D.S. performed SNP array experiments. K.C., M.V.D.B., B.S., E.D. and I.V. selected and characterized patient samples. All authors approved the manuscript. Competing financial interests B.H. and J.C. are employees of Real Time Genomics and have financial interests in Real Time Genomics. Corresponding authors Correspondence to: * Diether Lambrechts or * Jurgen Del-Favero Author Details * Joke Reumers Search for this author in: * NPG journals * PubMed * Google Scholar * Peter De Rijk Search for this author in: * NPG journals * PubMed * Google Scholar * Hui Zhao Search for this author in: * NPG journals * PubMed * Google Scholar * Anthony Liekens Search for this author in: * NPG journals * PubMed * Google Scholar * Dominiek Smeets Search for this author in: * NPG journals * PubMed * Google Scholar * John Cleary Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Van Loo Search for this author in: * NPG journals * PubMed * Google Scholar * Maarten Van Den Bossche Search for this author in: * NPG journals * PubMed * Google Scholar * Kirsten Catthoor Search for this author in: * NPG journals * PubMed * Google Scholar * Bernard Sabbe Search for this author in: * NPG journals * PubMed * Google Scholar * Evelyn Despierre Search for this author in: * NPG journals * PubMed * Google Scholar * Ignace Vergote Search for this author in: * NPG journals * PubMed * Google Scholar * Brian Hilbush Search for this author in: * NPG journals * PubMed * Google Scholar * Diether Lambrechts Contact Diether Lambrechts Search for this author in: * NPG journals * PubMed * Google Scholar * Jurgen Del-Favero Contact Jurgen Del-Favero Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (5M) Supplementary Notes 1–14 Excel files * Supplementary Table S1 (401K) Validation experiments for the twin and tumor-normal genomes using Sanger sequencing and Sequenom MassARRAY genotyping * Supplementary Table S2 (934K) Metrics and error rates calculated for each filter combination performed on the twin genome comparison using coverage depth cutoffs of 10 and 20 * Supplementary Table S3 (111K) Overlap analysis of somatic variants in Tumor 1 and its replicate using three filter settings * Supplementary Table S4 (70K) Sequenom validation of somatic missense SNVs in the ovarian clear cell tumor genome using three filter settings * Supplementary Table S5 (20K) Prediction of the effect of the validated somatic mutations and somatic non-coding SNVs in the ovarian serous carcinoma * Supplementary Table S6 (516K) Effect of filters cumulatively applied to the NA19240 genome, using stringent CG filters versus unfiltered CG data Additional data
• The discovery and development of belimumab: the anti-BLyS–lupus connection
  - Nat Biotechnol 30(1):69-77 (2012)
  Nature Biotechnology | Research | Perspective The discovery and development of belimumab: the anti-BLyS–lupus connection * William Stohl1 * David M Hilbert2 * Affiliations * Corresponding authorJournal name:Nature BiotechnologyVolume: 30,Pages:69–77Year published:(2012)DOI:doi:10.1038/nbt.2076Received30 August 2011Accepted02 November 2011Published online09 January 2012 Abstract * Abstract * Author information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg For the first time in more than 50 years, the US Food and Drug Administration has approved a drug specifically for the treatment of systemic lupus erythematosus (SLE). This drug, belimumab (Benlysta), is a human monoclonal antibody that neutralizes the B-cell survival factor, B-lymphocyte stimulator (BLyS). The approval of belimumab combined a pioneering approach to genomics-based gene discovery, an astute appreciation of translational medicine, a disciplined clinical strategy, a willingness to take calculated risks, a devoted cadre of patients and physicians and a healthy dose of serendipity. Collectively, these efforts have provided a model for the development of a new generation of drugs to treat the broad manifestations of SLE. However, as a substantial percentage of SLE patients do not respond to belimumab, further research is needed to better characterize the pathogenetic mechanisms of SLE, identify additional therapeutic targets, and develop effective and nontoxic nov! el agents against these targets. View full text Figures at a glance * Figure 1: Important milestones in belimumab (Benlysta) achieving FDA approval in SLE. RA, rheumatoid arthritis; SPA, special protocol assessment; BLA, biologics license application. * Figure 2: Coordinated development of BLyS and anti-BLyS (belimumab) for the treatment of aberrant B-cell function in CVI and SLE, respectively. * Figure 3: The effect of belimumab on the binding of BLyS to its receptors. () BLyS can bind to three receptors (BCMA, TACI, BR3 (also known as BAFF-R)), each of which being a distinct member of the TNF superfamily and each being expressed to different degrees on B cells, T cells and plasma cells. APRIL can bind to two of these receptors (BCMA, TACI) but not to the third (BR3). () Belimumab binds BLyS and blocks engagement of BLyS with BCMA, TACI and BR3. Belimumab does not bind APRIL, so engagement of APRIL with BCMA or TACI is not affected. Author information * Abstract * Author information Affiliations * Division of Rheumatology, University of Southern California Keck School of Medicine, Los Angeles, California, USA. * William Stohl * Zyngenia, Inc., Gaithersburg, Maryland, USA. * David M Hilbert Competing financial interests W.S. received clinical trials support from Human Genome Sciences. D.M.H. is a former employee of Human Genome Sciences and a current employee of Zyngenia. Corresponding author Correspondence to: * William Stohl Author Details * William Stohl Contact William Stohl Search for this author in: * NPG journals * PubMed * Google Scholar * David M Hilbert Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• Performance comparison of whole-genome sequencing platforms
  - Nat Biotechnol 30(1):78-82 (2012)
  Nature Biotechnology | Research | Analysis Performance comparison of whole-genome sequencing platforms * Hugo Y K Lam1, 8 * Michael J Clark1 * Rui Chen1 * Rong Chen2, 8 * Georges Natsoulis3 * Maeve O'Huallachain1 * Frederick E Dewey4 * Lukas Habegger5 * Euan A Ashley4 * Mark B Gerstein5, 6, 7 * Atul J Butte2 * Hanlee P Ji3 * Michael Snyder1 * Affiliations * Contributions * Corresponding authorJournal name:Nature BiotechnologyVolume: 30,Pages:78–82Year published:(2012)DOI:doi:10.1038/nbt.2065Received01 September 2011Accepted15 November 2011Published online18 December 2011 Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Whole-genome sequencing is becoming commonplace, but the accuracy and completeness of variant calling by the most widely used platforms from Illumina and Complete Genomics have not been reported. Here we sequenced the genome of an individual with both technologies to a high average coverage of ~76×, and compared their performance with respect to sequence coverage and calling of single-nucleotide variants (SNVs), insertions and deletions (indels). Although 88.1% of the ~3.7 million unique SNVs were concordant between platforms, there were tens of thousands of platform-specific calls located in genes and other genomic regions. In contrast, 26.5% of indels were concordant between platforms. Target enrichment validated 92.7% of the concordant SNVs, whereas validation by genotyping array revealed a sensitivity of 99.3%. The validation experiments also suggested that >60% of the platform-specific variants were indeed present in the genome. Our results have important implications ! for understanding the accuracy and completeness of the genome sequencing platforms. View full text Figures at a glance * Figure 1: Genome coverage at different read depths. () Percentage of genome covered by different read depths in different platforms. () Histogram of genome coverage at different read depths. * Figure 2: SNV detection and intersection. () SNVs detected from the PBMC and saliva samples in each platform were combined. The unions of SNVs in each platform were then intersected. Sensitivity was measured against the Illumina Omni array. Ti/Tv is the transition-to-transversion ratio. The known and novel counts were based on dbSNP. 'Sanger' and 'validated' represent validation by Sanger sequencing and Illumina sequencing (with Agilent target enrichment capture), respectively. () Comparing platform-specific SNVs to non-SNV calls in another platform. IL, Illumina; CG, Complete Genomics. * Figure 3: SNV association with different genomic elements. () Gene elements: UTR, exonic, intronic and intergenic regions. Inset: number of SNVs associated with UTR5, UTR3 and exonic regions. () Gene elements: splicing sites, noncoding RNA and upstream/downstream (<1 kb) regions of genes. () Repetitive elements: centromere, telomere, tRNA and rRNA. () Repetitive elements: L1, Alu, simple repeat and low-complexity repeat. () SNV frequency at different chromosomal locations. Tracks from outer to inner: SNV frequency for Illumina (IL), Complete Genomics (CG), concordant, IL-specific and CG-specific calls. Outermost: chromosome ideogram. * Figure 4: Indel detection and intersection. () Indels detected from the PBMC and saliva samples in each platform were combined. The unions of indels in each platform were then intersected. Note: 5,668 IL and 8,415 CG indels were removed after 5b-window merging. () Indel size distribution. Negative size represents deletion and positive size represents insertion. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Sequence Read Archive * SRA045736.2 Author information * Abstract * Accession codes * Author information * Supplementary information Affiliations * Department of Genetics, Stanford University, Stanford, California, USA. * Hugo Y K Lam, * Michael J Clark, * Rui Chen, * Maeve O'Huallachain & * Michael Snyder * Division of Systems Medicine, Department of Pediatrics, Stanford University, Stanford, California, USA. * Rong Chen & * Atul J Butte * Department of Medicine, Stanford University, Stanford, California, USA. * Georges Natsoulis & * Hanlee P Ji * Center for Inherited Cardiovascular Disease, Division of Cardiovascular Medicine, Stanford University, Stanford, California, USA. * Frederick E Dewey & * Euan A Ashley * Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA. * Lukas Habegger & * Mark B Gerstein * Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, USA. * Mark B Gerstein * Department of Computer Science, Yale University, New Haven, Connecticut, USA. * Mark B Gerstein * Present address: Personalis, Inc., Palo Alto, California, USA. * Hugo Y K Lam & * Rong Chen Contributions H.Y.K.L. and M.J.C. did the analysis. G.N. and L.H. assisted in the analysis. Rui C. did DNA sequencing. Rong C. did the disease-association study. Rui C. and M.O'H. did the validation experiments. H.Y.K.L., F.E.D., E.A.A., M.B.G., A.J.B., H.P.J. and M.S. coordinated the analysis and revised the manuscript. H.Y.K.L., M.J.C. and M.S. wrote the manuscript. Competing financial interests M.S. is a scientific advisory board member for Genapsys, Inc.; a scientific advisory board member and cofounder of Personalis, Inc.; and a consultant for Illumina. Corresponding author Correspondence to: * Michael Snyder Author Details * Hugo Y K Lam Search for this author in: * NPG journals * PubMed * Google Scholar * Michael J Clark Search for this author in: * NPG journals * PubMed * Google Scholar * Rui Chen Search for this author in: * NPG journals * PubMed * Google Scholar * Rong Chen Search for this author in: * NPG journals * PubMed * Google Scholar * Georges Natsoulis Search for this author in: * NPG journals * PubMed * Google Scholar * Maeve O'Huallachain Search for this author in: * NPG journals * PubMed * Google Scholar * Frederick E Dewey Search for this author in: * NPG journals * PubMed * Google Scholar * Lukas Habegger Search for this author in: * NPG journals * PubMed * Google Scholar * Euan A Ashley Search for this author in: * NPG journals * PubMed * Google Scholar * Mark B Gerstein Search for this author in: * NPG journals * PubMed * Google Scholar * Atul J Butte Search for this author in: * NPG journals * PubMed * Google Scholar * Hanlee P Ji Search for this author in: * NPG journals * PubMed * Google Scholar * Michael Snyder Contact Michael Snyder Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (1M) Supplementary Figures 1 and 2 Excel files * Supplementary Table 1 (45K) Disease association of all platform-specific SNPs. Additional data
• Draft genome sequence of pigeonpea (Cajanus cajan), an orphan legume crop of resource-poor farmers
  - Nat Biotechnol 30(1):83-89 (2012)
  Nature Biotechnology | Research | Article Open Draft genome sequence of pigeonpea (Cajanus cajan), an orphan legume crop of resource-poor farmers * Rajeev K Varshney1, 2 * Wenbin Chen3 * Yupeng Li4 * Arvind K Bharti5 * Rachit K Saxena1 * Jessica A Schlueter6 * Mark T A Donoghue7 * Sarwar Azam1 * Guangyi Fan3 * Adam M Whaley6 * Andrew D Farmer5 * Jaime Sheridan6 * Aiko Iwata4 * Reetu Tuteja1, 7 * R Varma Penmetsa8 * Wei Wu9 * Hari D Upadhyaya1 * Shiaw-Pyng Yang9 * Trushar Shah1 * K B Saxena1 * Todd Michael9 * W Richard McCombie10 * Bicheng Yang3 * Gengyun Zhang3 * Huanming Yang3 * Jun Wang3, 11 * Charles Spillane7 * Douglas R Cook8 * Gregory D May5 * Xun Xu3, 12 * Scott A Jackson4 * Affiliations * Contributions * Corresponding authorJournal name:Nature BiotechnologyVolume: 30,Pages:83–89Year published:(2012)DOI:doi:10.1038/nbt.2022Received19 July 2011Accepted03 October 2011Published online06 November 2011 Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Pigeonpea is an important legume food crop grown primarily by smallholder farmers in many semi-arid tropical regions of the world. We used the Illumina next-generation sequencing platform to generate 237.2 Gb of sequence, which along with Sanger-based bacterial artificial chromosome end sequences and a genetic map, we assembled into scaffolds representing 72.7% (605.78 Mb) of the 833.07 Mb pigeonpea genome. Genome analysis predicted 48,680 genes for pigeonpea and also showed the potential role that certain gene families, for example, drought tolerance–related genes, have played throughout the domestication of pigeonpea and the evolution of its ancestors. Although we found a few segmental duplication events, we did not observe the recent genome-wide duplication events observed in soybean. This reference genome sequence will facilitate the identification of the genetic basis of agronomically important traits, and accelerate the development of improved pigeonpea varieties tha! t could improve food security in many developing countries. View full text Figures at a glance * Figure 1: Extensive synteny between the pigeonpea and soybean genomes. Soybean pseudomolecules, labeled as Gm, are represented as green boxes. Numbers along each chromosome box are sequence length in megabases. Pigeonpea pseudomolecules, labeled as CcLG, are shown with each chromosome as a different color. Syntenic blocks were identified through reciprocal best matches between gene models and block identification using i-ADHoRe. Each line radiating from a pigeonpea pseudomolecule represents a gene match found in a block between soybean and pigeonpea. * Figure 2: Microsynteny analysis between pigeonpea and soybean genomes. One chromosome arm of soybean chromosome 01S (south arm) and pigeonpea CcLG06 (indicated as a green circle in the whole-genome dot-plot in Supplementary Fig. 6) is shown here as a representation of microsynteny. Mapping of the pigeonpea transcriptome assembly contigs (TACs) of the pigeonpea transcriptome assembly (CcTA v2) onto both genomes (indicated by green lines) was used as a measure of conserved gene order. () The first part shows local rearrangements. () The later part indicates very good collinearity among genes in the two genomes. * Figure 3: Distribution of gene families among five eudicot genomes (M. truncatula, soybean, L. japonicus, pigeonpea and grapevine). Homologous genes in pigeonpea, soybean, M. truncatula, L. japonicus and grapevine were clustered to gene families. The numbers of gene families are indicated for each species and species intersection. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions BioProject * PRJNA72815 Author information * Abstract * Accession codes * Author information * Supplementary information Affiliations * International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India. * Rajeev K Varshney, * Rachit K Saxena, * Sarwar Azam, * Reetu Tuteja, * Hari D Upadhyaya, * Trushar Shah & * K B Saxena * CGIAR Generation Challenge Programme (GCP), c/o CIMMYT, Mexico DF, Mexico. * Rajeev K Varshney * Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China. * Wenbin Chen, * Guangyi Fan, * Bicheng Yang, * Gengyun Zhang, * Huanming Yang, * Jun Wang & * Xun Xu * University of Georgia, Athens, Georgia, USA. * Yupeng Li, * Aiko Iwata & * Scott A Jackson * National Center for Genome Resources (NCGR), Santa Fe, New Mexico, USA. * Arvind K Bharti, * Andrew D Farmer & * Gregory D May * University of North Carolina, Charlotte, North Carolina, USA. * Jessica A Schlueter, * Adam M Whaley & * Jaime Sheridan * National University of Ireland Galway (NUIG), Botany and Plant Science, Galway, Ireland. * Mark T A Donoghue, * Reetu Tuteja & * Charles Spillane * University of California, Davis, California, USA. * R Varma Penmetsa & * Douglas R Cook * Monsanto Company, Creve Coeur, Missouri, USA. * Wei Wu, * Shiaw-Pyng Yang & * Todd Michael * Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA. * W Richard McCombie * Department of Biology, University of Copenhagen, Denmark. * Jun Wang * BGI-Americas, Cambridge, Massachusetts, USA. * Xun Xu Contributions R.K.V., W.C., R.K.S., G.F., R.V.P., H.D.U., K.B.S., W.R.McC., B.Y., G.Z., D.R.C., G.D.M., X.X., contributed to generation of genome sequence, transcriptome sequence and genetic mapping data; W.C., G.F., R.T., W.W., S.-P.Y., T.M., W.R.McC., G.Z., H.Y., J.W., X.X., worked on genome assembly; W.C., Y.L., A.K.B., R.K.S., S.A., A.D.F., H.Y., J.W., X.X., contributed to genome annotation and gene function; R.K.V., W.C., Y.L., A.K.B., R.K.S., J.A.S., J.S., A.I., M.T.A.D., A.M.W., A.D.F., J.S., R.T., T.S., C.S., D.R.C., G.D.M., X.X., S.A.J., worked on genome analysis and comparative genomics and R.K.V., together with S.A.J., D.R.C., C.S., W.C., A.K.B., R.K.S., S.A., J.A.S., wrote and finalized the manuscript. R.K.V. conceived and directed the project. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Rajeev K Varshney Author Details * Rajeev K Varshney Contact Rajeev K Varshney Search for this author in: * NPG journals * PubMed * Google Scholar * Wenbin Chen Search for this author in: * NPG journals * PubMed * Google Scholar * Yupeng Li Search for this author in: * NPG journals * PubMed * Google Scholar * Arvind K Bharti Search for this author in: * NPG journals * PubMed * Google Scholar * Rachit K Saxena Search for this author in: * NPG journals * PubMed * Google Scholar * Jessica A Schlueter Search for this author in: * NPG journals * PubMed * Google Scholar * Mark T A Donoghue Search for this author in: * NPG journals * PubMed * Google Scholar * Sarwar Azam Search for this author in: * NPG journals * PubMed * Google Scholar * Guangyi Fan Search for this author in: * NPG journals * PubMed * Google Scholar * Adam M Whaley Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew D Farmer Search for this author in: * NPG journals * PubMed * Google Scholar * Jaime Sheridan Search for this author in: * NPG journals * PubMed * Google Scholar * Aiko Iwata Search for this author in: * NPG journals * PubMed * Google Scholar * Reetu Tuteja Search for this author in: * NPG journals * PubMed * Google Scholar * R Varma Penmetsa Search for this author in: * NPG journals * PubMed * Google Scholar * Wei Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Hari D Upadhyaya Search for this author in: * NPG journals * PubMed * Google Scholar * Shiaw-Pyng Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Trushar Shah Search for this author in: * NPG journals * PubMed * Google Scholar * K B Saxena Search for this author in: * NPG journals * PubMed * Google Scholar * Todd Michael Search for this author in: * NPG journals * PubMed * Google Scholar * W Richard McCombie Search for this author in: * NPG journals * PubMed * Google Scholar * Bicheng Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Gengyun Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Huanming Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Jun Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Charles Spillane Search for this author in: * NPG journals * PubMed * Google Scholar * Douglas R Cook Search for this author in: * NPG journals * PubMed * Google Scholar * Gregory D May Search for this author in: * NPG journals * PubMed * Google Scholar * Xun Xu Search for this author in: * NPG journals * PubMed * Google Scholar * Scott A Jackson Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Tables 1–14,16,18,19 and Supplementary Figures 1–12 Excel files * Supplementary Table 15 (2M) Primer sequences for the SSR markers * Supplementary Table 17 (2M) SNP information across 12 pigeonpea genotypes Creative Commons Attribution-Non-Commercial-Share Alike license This article is distributed under the terms of the Creative Commons Attribution-Non-Commercial-Share Alike license (http://creativecommons.org/licenses/by-nc-sa/3.0/), which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation, and derivative works must be licensed under the same or similar license. Additional data
• Genome architectures revealed by tethered chromosome conformation capture and population-based modeling
  - Nat Biotechnol 30(1):90-98 (2012)
  Nature Biotechnology | Research | Article Genome architectures revealed by tethered chromosome conformation capture and population-based modeling * Reza Kalhor1, 2 * Harianto Tjong1 * Nimanthi Jayathilaka1, 2 * Frank Alber1 * Lin Chen1, 3, 4 * Affiliations * Contributions * Corresponding authorsJournal name:Nature BiotechnologyVolume: 30,Pages:90–98Year published:(2012)DOI:doi:10.1038/nbt.2057Received22 February 2011Accepted02 November 2011Published online25 December 2011 Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We describe tethered conformation capture (TCC), a method for genome-wide mapping of chromatin interactions. By performing ligations on solid substrates rather than in solution, TCC substantially enhances the signal-to-noise ratio, thereby facilitating a detailed analysis of interactions within and between chromosomes. We identified a group of regions in each chromosome in human cells that account for the majority of interchromosomal interactions. These regions are marked by high transcriptional activity, suggesting that their interactions are mediated by transcriptional machinery. Each of these regions interacts with numerous other such regions throughout the genome in an indiscriminate fashion, partly driven by the accessibility of the partners. As a different combination of interactions is likely present in different cells, we developed a computational method to translate the TCC data into physical chromatin contacts in a population of three-dimensional genome structures.! Statistical analysis of the resulting population demonstrates that the indiscriminate properties of interchromosomal interactions are consistent with the well-known architectural features of the human genome. View full text Figures at a glance * Figure 1: Overview of TCC. Cells are treated with formaldehyde, which covalently crosslinks proteins (purple ellipses) to each other and to DNA (orange and blue strings). (1) The chromatin is solubilized and its proteins are biotinylated (purple ball and stick). DNA is digested with a restriction enzyme that generates 5′ overhangs. (2) Crosslinked complexes are immobilized at a very low density on the surface of streptavidin-coated magnetic beads (gray arc) through the biotinylated proteins; noncrosslinked DNA fragments are removed. (3) The 5′ overhangs are filled in with an α-thio-triphosphate–containing nucleotide analog (the yellow nucleotide in the inset), which is resistant to exonuclease digestion, and a biotinylated nucleotide analog (the red nucleotide with the purple ball and stick in the inset) to generate blunt ends. (4) Blunt DNA ends are ligated. (5) Crosslinking is reversed and DNA is purified. The biotinylated nucleotide is removed from nonligated DNA ends using Escherichia coli ! exonuclease III whereas the phosphorothioate bond protects DNA fragments from complete degradation. (6) The DNA is sheared and fragments that include a ligation junction are isolated on streptavidin-coated magnetic beads, but this time through the biotinylated nucleotides. (7) Sequencing adaptors are added to all DNA molecules to generate a library. (8) Ligation events are identified using paired-end sequencing. * Figure 2: Tethering improves the signal-to-noise ratio of conformation capture. (,) TCC can reproduce the results obtained by Hi-C10. A genome-wide contact frequency map is compiled from the ligation frequency data generated by tethered (TCC) () and nontethered (Hi-C) () conformation capture. The portion of each map that corresponds to the intrachromosomal contacts of chromosome 2 is shown. The intensity of the red color in each position of the map represents the observed frequency of contact between corresponding segments of the chromosome, which are shown on the top and to the left of the map. In these maps, chromosome 2 is divided into segments that span 277 HindIII sites each, resulting in 258 segments of ~1 Mb. A pair of tick marks on the ideogram encompasses 4,986 HindIII sites. In this and other figures, the white lines in the heat maps mark the unalignable region of the centromeres. () The observed fractions of intra- and interchromosomal ligations in tethered (T) and nontethered (NT) libraries produced using HindIII or MboI. The random ligation! (RL) bar represents the expected fractions if all ligations occurred between noncrosslinked DNA fragments. For the nontethered MboI library only, these fractions were determined by sequencing 160 individual DNA molecules from three replicates of the experiment. (,) The genome-wide enrichment map for chromosome 2, compiled from the tethered () and nontethered () HindIII libraries. Enrichment is calculated as the ratio of the observed frequency in each position to its expected value; expected values were obtained assuming completely random ligations (Online Methods). Red and light blue, respectively, indicate enrichment and depletion of a contact. Chromosome 2 (left) extends along the y axis whereas all 23 chromosomes (top) extend along the x axis. The zoomed panel to the right of each map magnifies the section that corresponds to contacts between the small arm of chromosome 2 and chromosomes 20, 21, 22 and X. For maps in and , each chromosome is divided into segments that s! pan 558 HindIII sites, leading to respectively 116 and 1,384 s! egments of ~1.5 Mb for chromosome 2 and all other chromosomes. A pair of tick marks on chromosome 2 spans 5,022 HindIII sites. * Figure 3: Intrachromosomal interactions. () Correlation map and class assignment for chromosome 2. The color of each position in the map represents the Pearson's correlation between the intrachromosomal contact profiles of the corresponding two segments of the chromosome to the left and on top (the ideogram of the chromosome has only been shown to the left, but the x axis of the map also represents the chromosome). To assign each segment to the active (orange blocks on top of the map) or the inactive (purple blocks on top of the map) class, principal component analysis was used to calculate EIG (value of the first principal component, plotted on top of the assignment blocks) for each segment (Online Methods). Segments with a positive EIG are assigned to the active class, whereas those with a negative EIG are assigned to the inactive class. Segments with EIG values close to zero have not been assigned to either class. The size of each chromosome band is based on the number of HindIII sites it contains. () The genome! -wide average Pearson's correlation between intrachromosomal contact profiles of two active segments (orange), two inactive segments (dark purple), and an active and an inactive segment (gray) plotted against their genomic distance. () Active-active (left) and inactive-inactive (right) correlation maps for chromosome 2. Each correlation map is calculated following the procedure in , except contacts between only the active segments (left) or only the inactive segments (right) are considered. The ideogram of chromosome 2 in the middle shows the active (orange bars on the left) and inactive (purple bars on the right) segments. The arrows mark the positions of these segments in the corresponding maps. In this figure, the tethered HindIII library is used and each chromosome is divided into segments of 138 HindIII sites, resulting in, respectively, 517 and 6,000 segments of ~0.5 Mb for chromosome 2 and the entire genome. * Figure 4: Interchromosomal interactions. () For all segments of chromosome 2, ICP is plotted against EIG. The blue dashed line separates high-ICP segments: values above the line are significantly larger than the average ICP for inactive segments (P < 0.05, nonparametric). The open red dots mark those inactive segments with a large ICP that also flank the centromere. Chromosome 2 is divided into 517 segments of ~0.5 Mb, each spanning 138 HindIII sites. Data from the tethered HindIII library are used in all panels. () For all active segments in the genome, ICP is plotted against the binding of RNA polymerase II (pol II). Pol II binding values are reproduced from a ChIP-Seq study39 on the GM12878 cells and are in arbitrary units based on alignment frequency. P < 10−16. Each point represents a segment of the genome that spans 138 HindIII sites. The x axis is plotted in a logarithmic scale. () For seven loci on the small arm of chromosome 11, the ICP value is plotted against their average distance from the edge of chr! omosome 11 territory as measured by FISH27. Positive distance values denote localization within the bulk territory, whereas negative values denote localization away from the bulk territory. Error bars, ± 95% confidence interval27. () Plotted are the frequencies of all contacts between high-ICP active segments on chromosome 19 and all the segments on chromosome 11. Purple dots represent contacts involving high-ICP active segments on chromosome 11, and gray triangles represent contacts involving all the other segments of chromosome 11. Contacts plotted between vertical dotted lines involve the same high-ICP active segment on chromosome 19 and all the segments of chromosome 11. The locations of the high-ICP active segments in chromosome 19 are marked by an orange bar on the ideogram of the chromosome on the bottom of the panel. The different shades of orange are used only to differentiate the adjacent segments. Frequencies above the dashed blue line are significantly higher t! han the average frequency of contacts between high-ICP active ! segments on chromosome 19 and inactive segments on chromosome 11 (P < 0.04, nonparametric). These frequencies can be considered significantly larger than the noise level, defined as the false-positive contact frequencies due to random intermolecular ligations. Each chromosome was divided into ~1 Mb segments that span 277 HindIII sites, resulting in a total of 143 segments for chromosomes 11 and 43 segments for chromosome 19. Among those, 14 segments on chromosome 19 and 28 segments on chromosome 11 were classified as high-ICP active. () For all possible pairs of high-ICP active segments from chromosomes 11 and 19, their contact frequency has been plotted against the product of their ICPs. Same interactions are marked with purple color in . The P-value of the correlation is nominal. Other parameters are the same as in . () The layout of 3D-FISH experiments where the localization of a high-ICP active locus on chromosome 19 (H0) relative to four loci on chromosome 11 (H1, H2, ! L1 and L2) was analyzed in about 1,000 cells per pair of loci. H1 and H2 are high-ICP active, whereas the L1 and L2 are inactive. The blocks on the chromosomes' ideograms mark the position of each locus (orange for high-ICP active and brown for inactive), and the arrows mark the pair combinations that are analyzed (purple for active-active and gray for active-inactive). () An example nucleus from each pair of loci analyzed in 3D-FISH. Nuclei are counterstained with DAPI (blue). In all four nuclei, the hybridization signal of H0 is shown in red and that of the other locus is shown in green. () Cumulative percentage of nuclei that show a pair of hybridization signals closer than a given distance is plotted. Only the closest pair of signals for each nucleus is considered. Distances smaller than 0.65 μm (dashed blue line, arbitrarily selected for visualization purposes) represent colocalizations in a close vicinity where a direct interaction between loci is possible. * Figure 5: Coarse-graining of the contact frequency maps and structural representation of the genome. () The contact frequency map of chromosome 11 from the tethered HindIII library. The chromosome has been divided into 237 segments each of which covers 166 HindIII sites. Hierarchical constrained clustering was applied using the Pearson's correlation between the segments' contact profiles as the similarity measure (Methods). The dendrogram of constrained clustering is shown to the left and on top of the map. The intensity of the red color in the map represents the observed frequency of contact between corresponding chromosome segments. () Coarse-grained block matrix of chromosome 11. To identify the blocks, we determined a clustering cutoff following a previously described procedure40. In the block map, the value of an element is the average contact frequency of all the corresponding elements in the contact frequency map. The dimension of the initial contact frequency map is reduced to 15 elements for the block matrix of chromosome 11 and a total of 428 for the block matrice! s of all 23 chromosomes. Spearman's rank correlation coefficient between this block matrix and the contact frequency map in is 0.78. Assignment of segments to the active (orange blocks) and inactive (dark brown blocks) classes are shown to the left and on top of the matrix. () Sphere representation for chromatin regions in a block. The sphere for each block is defined by two different radii. First, its hard radius (solid sphere), which is estimated from the block sequence length and nuclear occupancy of the genome; the sphere cannot be penetrated within this radius (Online Methods). Second, its soft radius (dotted line), which is twice that of the hard sphere radius. A contact between two spheres is defined as an overlap between the spheres' respective soft radii. Also shown is a schematic view of the 30 nm chromatin fiber. () Population of 10,000 genome structures. A schematic view of the calculated structure population is shown on top. A randomly selected sample from the ! population is magnified at the bottom. All 46 chromosome terri! tories are shown. Homologous pairs share the same color. The nuclear envelope is displayed in gray. For visualization purposes, the spheres are blurred in the magnified structure because the use of 2 × 428 spheres to represent the genome makes the territories appear more discrete than they actually are. * Figure 6: Population-based analysis of chromosome territory localizations in the nucleus. () The distribution of the radial positions for chromosomes 18 (red dashed line) and 19 (blue solid line), calculated from the genome structure population. Radial positions are calculated for the center of mass of each chromosome and are given as a fraction of the nuclear radius. () The average radial position of all chromosomes plotted against their size. Error bars, s.d. () Clustering of chromosomes with respect to the average distance between the center of mass of each chromosome pair in the genome structure population. The clustering dendrogram, which identifies two dominant clusters is shown on top. The matrix of average distances between pairs of chromosomes is shown at the bottom. The intensity of blue color increases with decreasing distance. () (Left panels) The density contour plot of the combined localization probability for all the chromosomes in cluster 1 (top panel) and cluster 2 (bottom panel) calculated from all the structures in the genome structure populati! on. The rainbow color-coding on the central nuclear plane ranges from blue (minimum value) to red (maximum value). (Right panels) A representative genome structure from the genome structure population. Chromosome territories are shown for all the chromosomes in cluster 1 (top) and all the chromosomes in clusters 2 (bottom). The localization probabilities are calculated following a previously described procedure28. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Sequence Read Archive * SRA025848 Author information * Abstract * Accession codes * Author information * Supplementary information Affiliations * Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, California, USA. * Reza Kalhor, * Harianto Tjong, * Nimanthi Jayathilaka, * Frank Alber & * Lin Chen * Program in Genetic, Molecular and Cellular Biology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA. * Reza Kalhor & * Nimanthi Jayathilaka * Department of Chemistry, University of Southern California, Los Angeles, California, USA. * Lin Chen * USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California, USA. * Lin Chen Contributions R.K. and L.C. conceived the TCC technique and R.K. performed the experiments and analyzed the contact data. R.K. and N.J. performed the FISH experiments and analyzed the results. H.T. and F.A. conceived the modeling strategy, and R.K. and L.C. provided input and discussions. H.T. performed the modeling experiments and analysis. R.K., F.A., H.T. and L.C. wrote the manuscript. All authors commented on and revised the manuscript. F.A. and L.C. supervised the project. Competing financial interests A provisional patent for TCC is under review. Corresponding authors Correspondence to: * Frank Alber or * Lin Chen Author Details * Reza Kalhor Search for this author in: * NPG journals * PubMed * Google Scholar * Harianto Tjong Search for this author in: * NPG journals * PubMed * Google Scholar * Nimanthi Jayathilaka Search for this author in: * NPG journals * PubMed * Google Scholar * Frank Alber Contact Frank Alber Search for this author in: * NPG journals * PubMed * Google Scholar * Lin Chen Contact Lin Chen Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (31M) Supplementary Tables 1, 3 and 4, Supplementary Methods and Supplementary Figures 1–10 Excel files * Supplementary Table 2 (643K) No title Additional data
• Targeted RNA sequencing reveals the deep complexity of the human transcriptome
  - Nat Biotechnol 30(1):99-104 (2012)
  Nature Biotechnology | Research | Letter Targeted RNA sequencing reveals the deep complexity of the human transcriptome * Tim R Mercer1 * Daniel J Gerhardt2 * Marcel E Dinger1 * Joanna Crawford1 * Cole Trapnell3 * Jeffrey A Jeddeloh2 * John S Mattick1 * John L Rinn3 * Affiliations * Contributions * Corresponding authorsJournal name:Nature BiotechnologyVolume: 30,Pages:99–104Year published:(2012)DOI:doi:10.1038/nbt.2024Received05 August 2011Accepted04 October 2011Published online13 November 2011 Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Transcriptomic analyses have revealed an unexpected complexity to the human transcriptome, whose breadth and depth exceeds current RNA sequencing capability1, 2, 3, 4. Using tiling arrays to target and sequence select portions of the transcriptome, we identify and characterize unannotated transcripts whose rare or transient expression is below the detection limits of conventional sequencing approaches. We use the unprecedented depth of coverage afforded by this technique to reach the deepest limits of the human transcriptome, exposing widespread, regulated and remarkably complex noncoding transcription in intergenic regions, as well as unannotated exons and splicing patterns in even intensively studied protein-coding loci such as p53 and HOX. The data also show that intermittent sequenced reads observed in conventional RNA sequencing data sets, previously dismissed as noise, are in fact indicative of unassembled rare transcripts. Collectively, these results reveal the range,! depth and complexity of a human transcriptome that is far from fully characterized. View full text Figures at a glance * Figure 1: Circle plots illustrating the prevalence and complexity of captured transcripts at genic (a) and intergenic (b) loci. Successive tracks from outer edge indicate the following features: (i) genomic position (colored bars indicate different chromosomes and black ticks demarcate 5 kb); (ii) previous gene annotations (black bars on green background); (iii) frequency distribution of sequenced read alignments from precapture library (green histogram on gray background); (iv) assembled transcript structures from precapture library (green bars indicate exons and links indicate splice junctions); (v) probed regions represented on capture array (black bars on blue background); (vi) frequency distribution of sequenced read alignments from CaptureSeq library (blue histogram on gray background); and (vii) assembled transcript structures from CaptureSeq library (green bars and links correspond to exons and splice junctions identified in both pre- and CaptureSeq libraries, blue bars and links correspond to exons and splice junctions exclusively identified in CaptureSeq libraries). Inset shows detail of se! lected regions. Plot generated using Circos software (http://www.circos.ca/). * Figure 2: Resolution of unannotated p53 isoforms. () Genome-browser view of the p53 gene. The coverage and relative expression as determined by conventional RNA-Seq is indicated by upper red histogram. () Genome-browser view showing unannotated alternative splicing (blue; i–iv) identified using RNA CaptureSeq. The relative coverage and expression as determined by RNA CaptureSeq are also indicated by upper histogram (blue). () Relative expression of alternative unannotated p53 isoforms. The annotated (known, red) and unannotated (novel, blue) isoforms of p53, along with expected modifications to characterized protein domains are indicated in left panel. The relative expression of annotated and unannotated isoforms is indicated in right panel (error bars indicate upper and lower bound of 95% confidence interval). * Figure 3: Identification of unannotated exon variants and rare intergenic noncoding RNAs by targeted RNA capture and sequencing. () Genome-browser view of HOTAIR showing six unannotated isoforms (i), including fine-scale alternate splicing events (ii; zoom detail) that generate 16 additional unannotated isoforms. Relative abundance and coverage in RNA-Seq (upper blue histogram) and CaptureSeq (upper red histogram) libraries from foot fibroblast cell line indicated. (iii) Relative abundance of exon variants. () Differential expression across HOXA loci (black bars show gene annotations) between lung and foot fibroblasts, reflecting the different anatomical origin of each cell line. Coverage and relative abundance by RNA CaptureSeq (histograms) is indicated for each cell line. () Relative enrichment of HOXA genes and lncRNAs (1–7) between foot (F) and lung (L) fibroblasts as determined by CaptureSeq (dark gray) or qRT-PCR using precapture (light gray) or postcapture (medium gray) RNA samples. () Cumulative frequency distribution showing codon substitution frequency of full-length transcripts assembled ! from captured libraries (blue), coding genes (green) and known noncoding RNAs (red) for reference. () Cumulative frequency distribution indicates the normalized expression of full-length unannotated intergenic ncRNAs (red) relative to subset of genes captured on array (blue; captured) or genes identified by conventional RNA-Seq (green; all). () Cumulative frequency distribution showing the raw sequenced read frequency aligning to captured intergenic transcripts from both RNA-Seq (dashed red) and CaptureSeq (blue) and all assembled transcripts from RNA-Seq (solid red). The large difference in raw alignment frequency suggests saturated coverage achieved by CaptureSeq. () Pie chart indicating the proportion of RNA-Seq reads assigned to assembled transcripts, previous gene annotations, or unassignable reads occurring in intronic or intergenic regions. Bar indicates the proportion of unassigned intronic or intergenic reads 'rescued' by incorporation into rare transcript exons. Accession codes * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE29041 Author information * Accession codes * Author information * Supplementary information Affiliations * Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia. * Tim R Mercer, * Marcel E Dinger, * Joanna Crawford & * John S Mattick * Roche NimbleGen Inc., Research and Development, Madison, Wisconsin, USA. * Daniel J Gerhardt & * Jeffrey A Jeddeloh * Department of Stem Cell & Regenerative Biology, Harvard University, Cambridge, Massachusetts, USA. * Cole Trapnell & * John L Rinn Contributions T.R.M., J.A.J., J.S.M. and J.L.R. designed the experiments. D.J.G. performed array capture, quality assessments and supported the sequencing teams. J.C. performed RT-PCR. M.E.D., T.R.M. and C.T. performed alignment, transcript assembly and analysis. T.R.M., M.E.D., J.A.J., J.S.M. and J.L.R. wrote the manuscript. Competing financial interests T.R.M., M.E.D., C.T., J.C., J.S.M. and J.L.R. declare no competing financial interests. D.J.G. and J.A.J are employees of Roche NimbleGen, Inc. Corresponding authors Correspondence to: * Jeffrey A Jeddeloh or * John S Mattick or * John L Rinn Author Details * Tim R Mercer Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel J Gerhardt Search for this author in: * NPG journals * PubMed * Google Scholar * Marcel E Dinger Search for this author in: * NPG journals * PubMed * Google Scholar * Joanna Crawford Search for this author in: * NPG journals * PubMed * Google Scholar * Cole Trapnell Search for this author in: * NPG journals * PubMed * Google Scholar * Jeffrey A Jeddeloh Contact Jeffrey A Jeddeloh Search for this author in: * NPG journals * PubMed * Google Scholar * John S Mattick Contact John S Mattick Search for this author in: * NPG journals * PubMed * Google Scholar * John L Rinn Contact John L Rinn Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–8 and Supplementary Results Excel files * Supplementary Tables 1-4 (225K) Summary of library sequencing and alignment employed within study. Zip files * Supplementary Data (3M) Size and genome corrdinates of probed regions Additional data
• Resequencing 50 accessions of cultivated and wild rice yields markers for identifying agronomically important genes
  - Nat Biotechnol 30(1):105-111 (2012)
  Nature Biotechnology | Research | Resources Resequencing 50 accessions of cultivated and wild rice yields markers for identifying agronomically important genes * Xun Xu1, 2, 3, 12 * Xin Liu2, 12 * Song Ge4, 12 * Jeffrey D Jensen5, 12 * Fengyi Hu6, 12 * Xin Li1, 12 * Yang Dong1, 12 * Ryan N Gutenkunst7 * Lin Fang2 * Lei Huang3, 4 * Jingxiang Li2 * Weiming He2, 8 * Guojie Zhang1, 2, 4 * Xiaoming Zheng3, 4 * Fumin Zhang3 * Yingrui Li2 * Chang Yu2 * Karsten Kristiansen2, 9 * Xiuqing Zhang2 * Jian Wang2 * Mark Wright10 * Susan McCouch10 * Rasmus Nielsen1, 9, 11 * Jun Wang2, 9 * Wen Wang1 * Affiliations * Contributions * Corresponding authorsJournal name:Nature BiotechnologyVolume: 30,Pages:105–111Year published:(2012)DOI:doi:10.1038/nbt.2050Received03 June 2011Accepted25 October 2011Published online11 December 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Rice is a staple crop that has undergone substantial phenotypic and physiological changes during domestication. Here we resequenced the genomes of 40 cultivated accessions selected from the major groups of rice and 10 accessions of their wild progenitors (Oryza rufipogon and Oryza nivara) to >15 × raw data coverage. We investigated genome-wide variation patterns in rice and obtained 6.5 million high-quality single nucleotide polymorphisms (SNPs) after excluding sites with missing data in any accession. Using these population SNP data, we identified thousands of genes with significantly lower diversity in cultivated but not wild rice, which represent candidate regions selected during domestication. Some of these variants are associated with important biological features, whereas others have yet to be functionally characterized. The molecular markers we have identified should be valuable for breeding and for identifying agronomically important genes in rice. View full text Figures at a glance * Figure 1: Population structure of Asian rice. () PCA using all identified SNPs as markers. Most indica, japonica, O. nivara and O. rufipogon accessions cluster together, whereas four accessions (IRGC 12883, 8555, 43397 and 60542, marked as 1, 4, 28 and 39) that are located between groups can be explained by admixture and are marked as gray dots. The numbers by each dots are index numbers to the International Rice Research Institute (IRRI) accession numbers in Supplementary Table 1. () Neighbor-joining phylogenetic tree based on all SNPs, with the evolutionary distances measured by p-distance with PHYLIP51. () Population structure analysis using FRAPPE35. Each color represents one population. Each accession is represented by a vertical bar, and the length of each colored segment in each vertical bar represents the proportion contributed by ancestral populations. * Figure 2: Linkage disequilibrium differences between wild and cultivated rice groups. () Linkage disequilibrium (LD) decays quickly within 10 kb for indica, O. nivara and O. rufipogon, whereas it extends to 50 kb in japonica. () Different japonica subgroups have similar linkage disequilibrium decay patterns, indicating that the overall long linkage disequilibrium in japonica is not caused by population substructure. Four accessions with mixed genomic backgrounds are removed from the cultivated population for all analyses in this figure. * Figure 3: Significant outlier regions (genes) in ROD distribution. () ROD for japonica relative to O. rufipogon across chromosome 1. For other chromosomes see Supplementary Figure 13. Regions with a 2.5% significance level of ROD are shown in blue; regions with a 0.25% level are shown in red. () ROD for indica relative to O. nivara across chromosome 1. For other chromosomes see Supplementary Figure 14. Regions with a 2.5% significance level of ROD are shown in blue; regions with a 0.25% level are shown in red. (,) Gene trees for prog1 and Os09g0547100, respectively. The tree topologies depart dramatically from the whole genome phylogenetic tree (Fig. 1b), and the cultivated rice accessions almost share a single allele, probably because of a selective sweep. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Xun Xu, * Xin Liu, * Song Ge, * Jeffrey D Jensen, * Fengyi Hu, * Xin Li & * Yang Dong Affiliations * CAS-Max Planck Junior Research Group on Evolutionary Genomics, State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences (CAS), Kunming, China. * Xun Xu, * Xin Li, * Yang Dong, * Guojie Zhang, * Rasmus Nielsen & * Wen Wang * BGI-Shenzhen, Shenzhen, China. * Xun Xu, * Xin Liu, * Lin Fang, * Jingxiang Li, * Weiming He, * Guojie Zhang, * Yingrui Li, * Chang Yu, * Karsten Kristiansen, * Xiuqing Zhang, * Jian Wang & * Jun Wang * Graduate University of Chinese Academy Sciences, Beijing, China. * Xun Xu, * Lei Huang, * Xiaoming Zheng & * Fumin Zhang * State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, Beijing, China. * Song Ge, * Lei Huang, * Guojie Zhang & * Xiaoming Zheng * School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland. * Jeffrey D Jensen * Food Crops Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China. * Fengyi Hu * Department of Molecular and Cellular Biology, University of Arizona, Tucson, Arizona, USA. * Ryan N Gutenkunst * South China University of Technology, Guangdong, China. * Weiming He * Department of Biology, University of Copenhagen, Copenhagen, Denmark. * Karsten Kristiansen, * Rasmus Nielsen & * Jun Wang * Department of Plant Breeding & Genetics, Cornell University, Ithaca, New York, USA. * Mark Wright & * Susan McCouch * Departments of Integrative Biology and Statistics, University of California, Berkeley, USA. * Rasmus Nielsen Contributions W.W., Jun Wang, S.G., X.X., R.N. and F.H. designed the project. X.X., X. Liu, X. Li, J.D.J., M.W., L.F., G.Z., W.H., X. Zheng., Y.L. and R.N. analyzed the data. W.W., X.X., R.N., R.N.G., X. Liu, S.M., K.K. and Jun Wang wrote the manuscript. S.G., F.H., L.H. and F.Z. prepared the samples. X. Zhang, Jian Wang, C.Y., J.L. and Y.D. conducted the experiments. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Wen Wang or * Jun Wang or * Rasmus Nielsen Author Details * Xun Xu Search for this author in: * NPG journals * PubMed * Google Scholar * Xin Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Song Ge Search for this author in: * NPG journals * PubMed * Google Scholar * Jeffrey D Jensen Search for this author in: * NPG journals * PubMed * Google Scholar * Fengyi Hu Search for this author in: * NPG journals * PubMed * Google Scholar * Xin Li Search for this author in: * NPG journals * PubMed * Google Scholar * Yang Dong Search for this author in: * NPG journals * PubMed * Google Scholar * Ryan N Gutenkunst Search for this author in: * NPG journals * PubMed * Google Scholar * Lin Fang Search for this author in: * NPG journals * PubMed * Google Scholar * Lei Huang Search for this author in: * NPG journals * PubMed * Google Scholar * Jingxiang Li Search for this author in: * NPG journals * PubMed * Google Scholar * Weiming He Search for this author in: * NPG journals * PubMed * Google Scholar * Guojie Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Xiaoming Zheng Search for this author in: * NPG journals * PubMed * Google Scholar * Fumin Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Yingrui Li Search for this author in: * NPG journals * PubMed * Google Scholar * Chang Yu Search for this author in: * NPG journals * PubMed * Google Scholar * Karsten Kristiansen Search for this author in: * NPG journals * PubMed * Google Scholar * Xiuqing Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Jian Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Mark Wright Search for this author in: * NPG journals * PubMed * Google Scholar * Susan McCouch Search for this author in: * NPG journals * PubMed * Google Scholar * Rasmus Nielsen Contact Rasmus Nielsen Search for this author in: * NPG journals * PubMed * Google Scholar * Jun Wang Contact Jun Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Wen Wang Contact Wen Wang Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (5M) Supplementary Tables 1,2,5,6,8–16, Supplmentary Notes and Supplementary Figures 1–23 Other * Supplementary Table 3 (164K) Annotation information of novel genes identified from unmapped contigs. * Supplementary Table 4 (209K) Information of lost genes. (+ means not lost in that individual, - mean lost supported by both pairs and coverage information, P means only supported by pair-end information, C means only supported by coverage information.) * Supplementary Table 7 (78K) CNV regions. Zip files * Supplementary Data Set 1 (8M) * Supplementary Data Set 2 (21M) * Supplementary Data Set 3 (18M) * Supplementary Data Set 4 (27M) Additional data
• Erratum: In defense of life sciences venture investing
  - Nat Biotechnol 30(1):112 (2012)
  Nature Biotechnology | Erratum Erratum: In defense of life sciences venture investing * Bruce L Booth * Bijan SalehizadehJournal name:Nature BiotechnologyVolume: 30,Page:112Year published:(2012)DOI:doi:10.1038/nbt0112-112aPublished online09 January 2012 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Biotechnol.29, 579–583 (2011); published online 11 July 2011; corrected after print 11 July 2011 In the version of this article initially published, the x axis in Figure 1 should not have contained the category "Technology." In addition, on p. 579, col. 3, paragraph 1, the last sentence should have read "comparative realized performance" instead of "comparative performance." The errors have been corrected in the HTML and PDF versions of the article. Author information Author Details * Bruce L Booth Search for this author in: * NPG journals * PubMed * Google Scholar * Bijan Salehizadeh Search for this author in: * NPG journals * PubMed * Google Scholar Additional data Author Details * Bruce L Booth Search for this author in: * NPG journals * PubMed * Google Scholar * Bijan Salehizadeh Search for this author in: * NPG journals * PubMed * Google Scholar
• Erratum: Donor cell type can influence the epigenome and differentiation potential of human induced pluripotent stem cells
  - Nat Biotechnol 30(1):112 (2012)
  Nature Biotechnology | Erratum Erratum: Donor cell type can influence the epigenome and differentiation potential of human induced pluripotent stem cells * Kitai Kim * Rui Zhao * Akiko Doi * Kitwa Ng * Juli Unternaehrer * Patrick Cahan * Huo Hongguang * Yuin-Han Loh * Martin J Aryee * M William Lensch * Hu Li * James J Collins * Andrew P Feinberg * George Q DaleyJournal name:Nature BiotechnologyVolume: 30,Page:112Year published:(2012)DOI:doi:10.1038/nbt0112-112bPublished online09 January 2012 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Biotechnol.29, 1117–1119 (2011); published online 27 November 2011; corrected after print 9 January 2012 In the version of this article initially published, the received date was incorrect. The correct received date is 17 February 2011. The error has been corrected in the HTML and PDF versions of the article. Author information Author Details * Kitai Kim Search for this author in: * NPG journals * PubMed * Google Scholar * Rui Zhao Search for this author in: * NPG journals * PubMed * Google Scholar * Akiko Doi Search for this author in: * NPG journals * PubMed * Google Scholar * Kitwa Ng Search for this author in: * NPG journals * PubMed * Google Scholar * Juli Unternaehrer Search for this author in: * NPG journals * PubMed * Google Scholar * Patrick Cahan Search for this author in: * NPG journals * PubMed * Google Scholar * Huo Hongguang Search for this author in: * NPG journals * PubMed * Google Scholar * Yuin-Han Loh Search for this author in: * NPG journals * PubMed * Google Scholar * Martin J Aryee Search for this author in: * NPG journals * PubMed * Google Scholar * M William Lensch Search for this author in: * NPG journals * PubMed * Google Scholar * Hu Li Search for this author in: * NPG journals * PubMed * Google Scholar * James J Collins Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew P Feinberg Search for this author in: * NPG journals * PubMed * Google Scholar * George Q Daley Search for this author in: * NPG journals * PubMed * Google Scholar Additional data Author Details * Kitai Kim Search for this author in: * NPG journals * PubMed * Google Scholar * Rui Zhao Search for this author in: * NPG journals * PubMed * Google Scholar * Akiko Doi Search for this author in: * NPG journals * PubMed * Google Scholar * Kitwa Ng Search for this author in: * NPG journals * PubMed * Google Scholar * Juli Unternaehrer Search for this author in: * NPG journals * PubMed * Google Scholar * Patrick Cahan Search for this author in: * NPG journals * PubMed * Google Scholar * Huo Hongguang Search for this author in: * NPG journals * PubMed * Google Scholar * Yuin-Han Loh Search for this author in: * NPG journals * PubMed * Google Scholar * Martin J Aryee Search for this author in: * NPG journals * PubMed * Google Scholar * M William Lensch Search for this author in: * NPG journals * PubMed * Google Scholar * Hu Li Search for this author in: * NPG journals * PubMed * Google Scholar * James J Collins Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew P Feinberg Search for this author in: * NPG journals * PubMed * Google Scholar * George Q Daley Search for this author in: * NPG journals * PubMed * Google Scholar
• Erratum: Move over ZFNs
  - Nat Biotechnol 30(1):112 (2012)
  Nature Biotechnology | Erratum Erratum: Move over ZFNs * Laura DeFrancescoJournal name:Nature BiotechnologyVolume: 30,Page:112Year published:(2012)DOI:doi:10.1038/nbt0112-112cPublished online09 January 2012 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Biotechnol.29, 681–684 (2011); published online 5 August 2011; corrected after print 9 January 2012 In the version of this article initially published, Figure 1 incorrectly cites Romer et al., Plant Physiol., 2009, as the article that demonstrates "Promoter customized to bind 3 TAL effectors." It should have cited Romer et al., Proc. Nat. Acad. Sci. USA (2009). The error has been corrected in the HTML and PDF versions of the article. Author information Author Details * Laura DeFrancesco Search for this author in: * NPG journals * PubMed * Google Scholar Additional data Author Details * Laura DeFrancesco Search for this author in: * NPG journals * PubMed * Google Scholar
• Corrigendum: Biotechs follow big pharma lead back into academia
  - Nat Biotechnol 30(1):112 (2012)
  Nature Biotechnology | Corrigendum Corrigendum: Biotechs follow big pharma lead back into academia * Jim KlingJournal name:Nature BiotechnologyVolume: 30,Page:112Year published:(2012)DOI:doi:10.1038/nbt0112-112dPublished online09 January 2012 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Biotechnol.29, 555–556 (2011); published online 8 July 2011; corrected after print 9 January 2012 In the version of this article initially published, Gilead Biosciences was incorrectly named. The correct name is Gilead Sciences. The error has been corrected in the HTML and PDF versions of the article. Author information Author Details * Jim Kling Search for this author in: * NPG journals * PubMed * Google Scholar Additional data Author Details * Jim Kling Search for this author in: * NPG journals * PubMed * Google Scholar
• Corrigendum: New models emerge for commercializing university assets
  - Nat Biotechnol 30(1):112 (2012)
  Nature Biotechnology | Corrigendum Corrigendum: New models emerge for commercializing university assets * Nuala MoranJournal name:Nature BiotechnologyVolume: 30,Page:112Year published:(2012)DOI:doi:10.1038/nbt0112-112ePublished online09 January 2012 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Biotechnol.29, 774–775 (2011); published online 8 September 2011; corrected after print 9 January 2011 In the version of this article initially published, in Box 1, the CEO at VPD is David Collier, not Michael Collier. On p. 775, second column, the University of Pennsylvania has 400 invention disclosures a year, and not, as stated, Penn State. The error has been corrected in the HTML and PDF versions of the article. Author information Author Details * Nuala Moran Search for this author in: * NPG journals * PubMed * Google Scholar Additional data Author Details * Nuala Moran Search for this author in: * NPG journals * PubMed * Google Scholar
• Corrigendum: Drugmakers use real-world patient data to calibrate product development
  - Nat Biotechnol 30(1):112 (2012)
  Nature Biotechnology | Corrigendum Corrigendum: Drugmakers use real-world patient data to calibrate product development * Cormac SheridanJournal name:Nature BiotechnologyVolume: 30,Page:112Year published:(2012)DOI:doi:10.1038/nbt0112-112fPublished online09 January 2012 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Biotechnol., 778–779 (2011); published online 8 September 2011; corrected after print 9 January 2012 In the version of this article initially published, Medco Health Solutions' location was given as Wilmington, Delaware; it should have been Franklin Lakes, New Jersey. On p. 779, in the first paragraph, left column, UBC is said to be HealthCore's parent company. In fact, Wellpoint is HealthCore's parent company. The errors have been corrected in the HTML and PDF versions of the article. Author information Author Details * Cormac Sheridan Search for this author in: * NPG journals * PubMed * Google Scholar Additional data Author Details * Cormac Sheridan Search for this author in: * NPG journals * PubMed * Google Scholar
• Broadening PhD curricula
  - Nat Biotechnol 30(1):113-114 (2012)
  Nature Biotechnology | Careers and Recruitment Broadening PhD curricula * Nathan L Vanderford1Journal name:Nature BiotechnologyVolume: 30,Pages:113–114Year published:(2012)DOI:doi:10.1038/nbt.2091Published online09 January 2012 To provide formal education and training required for PhDs to perform their complex, multidisciplinary job functions, traditional PhD curricula should be restructured to include mandatory professional development course work. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Nathan L. Vanderford is at the University of Kentucky, Markey Cancer Center, Lexington, Kentucky, USA. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Nathan L Vanderford Author Details * Nathan L Vanderford Contact Nathan L Vanderford Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• People
  - Nat Biotechnol 30(1):116 (2012)
  Article preview View full access options Nature Biotechnology | Careers and Recruitment | People People Journal name:Nature BiotechnologyVolume: 30,Page:116Year published:(2012)DOI:doi:10.1038/nbt.2097Published online09 January 2012 Aegerion Pharmaceuticals (Cambridge, MA, USA) has announced the appointment of as global president. Rothera was most recently vice president and general manager of commercial operations for Europe, the Middle East & Africa at Shire Human Genetic Therapies, the division of Shire established after the acquisition of TransKaryotic Therapeutics. Prior to Shire, he held key commercial positions with early stage, growth companies at ADL Healthcare, Chiron Biopharmaceuticals Europe, PathoGenesis Europe and Amylin Pharmaceuticals Europe. Aegerion CEO Marc D. Beer remarks: "Mark's knowledge of the international orphan drug markets and his experience leading commercial operations globally will contribute to a successful commercial launch of lomitapide. He has played a pivotal role building several biotech companies into commercial organizations, and has an acknowledged record of successful orphan drug product launches, revenue growth and profitability. We look forward to his leaders! hip within Aegerion and welcome him to the management team." As part of a corporate reorganization, Stem Cell Therapeutics (Calgary, Alberta, Canada) has named as chief medical officer, as vice president of development and as director of intellectual property. has resigned as CSO to pursue other opportunities. Agro most recently served as senior vice president, clinical development at TransTech Pharma. Incledon has over 16 years of pharma industry experience, including serving as director of R&D at Eli Lilly Canada. Duncan previously built and managed Canada's largest biotech patent estate during his tenure with Allelix. ShangPharma (Shanghai) has announced the resignation of its president and CEO, Chen will continue to serve on ShangPharma's board of directors. has announced he is stepping down as CEO of Amgen (Thousand Oaks, CA, USA) in May 2012 and as chairman of the board at year's end. His successor is , who joined the company in 2006 as vice president of operations and currently serves as president and COO. In addition, is stepping down as Amgen's executive vice president for R&D in February. Chief medical officer will succeed him. has been appointed vice president of development at Interleukin Genetics (Waltham, MA, USA). She has nearly 20 years of experience in the genomics and biotech industry, most recently serving as vice president of business development at Agencourt Bioscience. GenVec (Gaithersburg, MD, USA) has announced the retirement plans of president and CEO He will continue to serve until a successor is named and the CEO transition is complete. He has served as a director, president and CEO since 1996. The European Commission (Brussels) has appointed University of Aberdeen microbiologist as its first chief scientific advisor, in a bid to improve policy advice to the bloc's executive when it drafts EU law. Glover previously served as Scotland's chief scientist, a post she had held since 2006. She will report directly to Commission president Jose Manuel Barroso. , formerly president and US site head of Renovis, has been named CSO of CureDuchenne (Newport Beach, CA, USA), a national nonprofit that funds research to find a cure for Duchenne muscular dystrophy. In addition, he will be responsible for CureDuchenne's Drug Discovery and Development Program to help drive research to market for treatments and ultimately a cure for Duchenne. Vertex Pharmaceuticals (Cambridge, MA, USA) has named CEO, effective February 1. Leiden is a managing director at venture capital firm Clarus Ventures and has been a member of Vertex's board of directors since July 2009. He is a former COO and president at Abbott Laboratories. He succeeds , who retires as CEO but will remain a member of the board. In related news, Emmens was recently named the Best Biotech CEO of 2011 in a readers' poll published by TheStreet. Emmens this year oversaw the successful approval and launch of the hepatitis C drug Incivek and the submission of a groundbreaking cystic fibrosis drug for FDA approval. The Oxbridge-London Biotech Roundtable, a 2,000-student-led forum, honored , president of The Scripps Research Institute in San Diego, and , member of the Medical Research Council's Laboratory of Molecular Biology in Cambridge and a Fellow of Trinity College Cambridge, with the Courage in Innovation Award at a ceremony in London on December 8, 2011. The students celebrated Lerner's and Winter's pioneering work on monoclonal antibodies that led to today's best-selling biologic therapeutics, including Humira (adalimumab). Amarin (Dublin) has announced that has joined the company's board of directors as an independent director. O'Sullivan has more than 40 years of pharma industry experience, including more than 30 years as CEO and member of the board of directors of the LEO Pharma companies in Ireland and more than 10 years as a member of the board of directors of the parent company of the LEO Pharma Group in Denmark. He currently serves as a member of the board of Merrion Pharmaceuticals and Warner Chilcott. Acetylon Pharmaceuticals (Boston) has named as vice president, clinical development. She joins the company after serving in senior clinical development roles with Roche and AstraZeneca. 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