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Featured researches published by Laurie Goodman.


Nature | 2008

The diploid genome sequence of an Asian individual

Jun Wang; Wei Wang; Ruiqiang Li; Yingrui Li; Geng Tian; Laurie Goodman; Wei Fan; Junqing Zhang; Jun Li; Juanbin Zhang; Yiran Guo; Binxiao Feng; Heng Li; Yao Lu; Xiaodong Fang; Huiqing Liang; Z. Du; Dong Li; Yiqing Zhao; Yujie Hu; Zhenzhen Yang; Hancheng Zheng; Ines Hellmann; Michael Inouye; John E. Pool; Xin Yi; Jing Zhao; Jinjie Duan; Yan Zhou; Junjie Qin

Here we present the first diploid genome sequence of an Asian individual. The genome was sequenced to 36-fold average coverage using massively parallel sequencing technology. We aligned the short reads onto the NCBI human reference genome to 99.97% coverage, and guided by the reference genome, we used uniquely mapped reads to assemble a high-quality consensus sequence for 92% of the Asian individual’s genome. We identified approximately 3 million single-nucleotide polymorphisms (SNPs) inside this region, of which 13.6% were not in the dbSNP database. Genotyping analysis showed that SNP identification had high accuracy and consistency, indicating the high sequence quality of this assembly. We also carried out heterozygote phasing and haplotype prediction against HapMap CHB and JPT haplotypes (Chinese and Japanese, respectively), sequence comparison with the two available individual genomes (J. D. Watson and J. C. Venter), and structural variation identification. These variations were considered for their potential biological impact. Our sequence data and analyses demonstrate the potential usefulness of next-generation sequencing technologies for personal genomics.


Cell | 2012

Single-Cell Exome Sequencing Reveals Single-Nucleotide Mutation Characteristics of a Kidney Tumor

Xun Xu; Yong Hou; Xuyang Yin; Li Bao; Aifa Tang; Luting Song; Fuqiang Li; Shirley Tsang; Kui Wu; Hanjie Wu; Weiming He; Liang Zeng; Manjie Xing; Renhua Wu; Hui Jiang; Xiao Liu; Dandan Cao; Guangwu Guo; Xueda Hu; Yaoting Gui; Zesong Li; Wenyue Xie; Xiaojuan Sun; Min Shi; Zhiming Cai; Bin Wang; Meiming Zhong; Jingxiang Li; Zuhong Lu; Ning Gu

Clear cell renal cell carcinoma (ccRCC) is the most common kidney cancer and has very few mutations that are shared between different patients. To better understand the intratumoral genetics underlying mutations of ccRCC, we carried out single-cell exome sequencing on a ccRCC tumor and its adjacent kidney tissue. Our data indicate that this tumor was unlikely to have resulted from mutations in VHL and PBRM1. Quantitative population genetic analysis indicates that the tumor did not contain any significant clonal subpopulations and also showed that mutations that had different allele frequencies within the population also had different mutation spectrums. Analyses of these data allowed us to delineate a detailed intratumoral genetic landscape at a single-cell level. Our pilot study demonstrates that ccRCC may be more genetically complex than previously thought and provides information that can lead to new ways to investigate individual tumors, with the aim of developing more effective cellular targeted therapies.


Nature Genetics | 2013

The duck genome and transcriptome provide insight into an avian influenza virus reservoir species

Yinhua Huang; Yingrui Li; David W. Burt; Hualan Chen; Yong Zhang; Wubin Qian; Heebal Kim; Shangquan Gan; Yiqiang Zhao; Jianwen Li; Kang Yi; Huapeng Feng; Pengyang Zhu; Bo Li; Qiuyue Liu; Suan Fairley; Katharine E. Magor; Zhenlin Du; Xiaoxiang Hu; Laurie Goodman; Hakim Tafer; Alain Vignal; Taeheon Lee; Kyu-Won Kim; Zheya Sheng; Yang An; Steve Searle; Javier Herrero; M.A.M. Groenen; Richard P.M.A. Crooijmans

The duck (Anas platyrhynchos) is one of the principal natural hosts of influenza A viruses. We present the duck genome sequence and perform deep transcriptome analyses to investigate immune-related genes. Our data indicate that the duck possesses a contractive immune gene repertoire, as in chicken and zebra finch, and this repertoire has been shaped through lineage-specific duplications. We identify genes that are responsive to influenza A viruses using the lung transcriptomes of control ducks and ones that were infected with either a highly pathogenic (A/duck/Hubei/49/05) or a weakly pathogenic (A/goose/Hubei/65/05) H5N1 virus. Further, we show how the ducks defense mechanisms against influenza infection have been optimized through the diversification of its β-defensin and butyrophilin-like repertoires. These analyses, in combination with the genomic and transcriptomic data, provide a resource for characterizing the interaction between host and influenza viruses.


GigaScience | 2012

Large and linked in scientific publishing.

Laurie Goodman; Scott C Edmunds; Alexandra T Basford

We are delighted to announce the launch of GigaScience, an online open-access journal that focuses on research using or producing large datasets in all areas of biological and biomedical sciences. GigaScience is a new type of journal that provides standard scientific publishing linked directly to a database that hosts all the relevant data. The primary goals for the journal, detailed in this editorial, are to promote more rapid data release, broader use and reuse of data, improved reproducibility of results, and direct, easy access between analyses and their data. Direct and permanent connections of scientific analyses and their data (achieved by assigning all hosted data a citable DOI) will enable better analysis and deeper interpretation of the data in the future.


Database | 2014

GigaDB: promoting data dissemination and reproducibility

Tam P. Sneddon; Xiao Si Zhe; Scott C Edmunds; Peter Li; Laurie Goodman; Christopher I. Hunter

Often papers are published where the underlying data supporting the research are not made available because of the limitations of making such large data sets publicly and permanently accessible. Even if the raw data are deposited in public archives, the essential analysis intermediaries, scripts or software are frequently not made available, meaning the science is not reproducible. The GigaScience journal is attempting to address this issue with the associated data storage and dissemination portal, the GigaScience database (GigaDB). Here we present the current version of GigaDB and reveal plans for the next generation of improvements. However, most importantly, we are soliciting responses from you, the users, to ensure that future developments are focused on the data storage and dissemination issues that still need resolving. Database URL: http://www.gigadb.org


Journal of Clinical Investigation | 2004

Persistence--luck--Avastin.

Laurie Goodman

“If I say I was delighted, it will be an understatement,” said Napoleone Ferrara of Genentech to the JCI when asked how he felt on February 26, 2004, when the FDA approved Avastin for colorectal cancer treatment. This culminated Ferrara’s more than 15-year involvement in its development. FDA approval came after of one of the most successful phase III anticancer drug trials in history. The trial consisted of 925 patients diagnosed with previously untreated metastatic colon cancer. Comparison of the median survival time of patients treated with irinotecan/5-fluorouracil/leucovorin (IFL) chemotherapy and Avastin to that of patients treated with IFL and a placebo showed that those receiving Avastin had a median end-point survival time of 5 months longer than those without. This met the primary efficacy endpoint, and was also the greatest difference in median survival time ever seen in a phase III trial. Avastin is the first antiangiogenesis drug to receive FDA approval. It inhibits vascular endothelial growth factor (VEGF), a main protein involved in inducing angiogenesis. In the last several years, interest in finding antiangiogenesis drugs for anticancer therapy has peaked. The idea, though, of inhibiting new blood vessel growth as a means to block tumorigenesis is not really a new one. It has been around since the early 1900s: in a seminal study, Gordon Ide and colleagues were among the first to suggest that new blood vessel development was essential to provide oxygen and nutrients for tumor growth (1). While FDA approval of Avastin is only the beginning for a new line of anticancer treatments, it marks the end of Napoleone Ferrara’s long road from the identification of an intriguing biological molecule to the development of a viable drug. Ferrara’s history with Avastin began in 1989, when he was working on another project — one focused on Genentech’s then main research interest: cardiovascular disorders. He identified and purified a pituitary gland protein that stimulated vascular endothelial cell growth — that protein was VEGF. At that time, “no one really thought this would be therapeutic,” Ferrara said. “But [Genentech] has this great policy that allows people to pursue their own interests.” So, Ferrara, thinking it might be useful for anticancer therapy, continued to work on VEGF. A breakthrough came in 1993, when Ferrara and colleagues developed a mouse antibody that blocked VEGF function and inhibited tumor growth in mice (2). At that time, Ferrara said, these results “were really surprising. It was thought that one would need to block many factors to inhibit angiogenesis.” Ferrara said they then “had to convince management [to pursue this] — but overall they were very supportive.” More difficult was the creation of a humanized form of the mouse anti-VEGF antibody. “Ultimately,” Ferrara said, “we obtained what is now called Avastin, which is extremely effective and not immunogenic.” Ferrara told the JCI that when Avastin entered clinical trials, one very encouraging aspect was that the side effects seen in the trials were “very mild.” “What you see mostly,” he said, “is modest hypertension. In phase II there was an indication of increased thrombosis, but [we] didn’t really see this in the phase III trials.” The resulting hypertension is not surprising, as VEGF induces nitric oxide, which is involved in blood pressure regulation. “The beauty of a monoclonal antibody [as a treatment] is its specificity,” Ferrara added. “Small molecule therapies can sometimes [have interactions] with other molecules, especially at higher doses, and cause side effects from activity unrelated to the targeted molecule.” A monoclonal antibody specifically interacts with only one protein and therefore only affects the pathways in which that protein is involved. Everything for Avastin looked incredibly good — until September 2002, when Avastin failed to meet its primary efficacy endpoint of progression-free survival in a phase III breast cancer trial. “This was really disappointing,” Ferrara said, but noted he still had some hope. “It did not increase the survival, but there was some evidence that the treatment shrank some tumors in the trial. Also, these patients were in third-line therapy [meaning they had already been treated by two other methods that had failed]. This is a very high bar for a trial. The patients were in a much more advanced stage and very sick.” Another positive sign was the preliminary results from a phase II renal cell carcinoma trial, which did meet its primary efficacy endpoint (3). That trial was the first step toward FDA drug approval. Most important, however, was the successful completion of the phase III colorectal cancer trial described above. Avastin is currently in several other phase III anticancer clinical trials, as well as trials for other disorders where angiogenesis is involved; for example, a shorter form of Avastin, called Lucentis, is already in phase III trials for age-related macular degeneration. When the JCI asked Ferrara how best to travel the entire road from isolated protein to approved drug, he answered with a laugh, “You need to be persistent — or lucky — or a combination of those.”


International Journal on Digital Libraries | 2017

Experiences in integrated data and research object publishing using GigaDB

Scott C Edmunds; Peter Li; Christopher I. Hunter; Si Zhe Xiao; Rob Davidson; Nicole Nogoy; Laurie Goodman

In the era of computation and data-driven research, traditional methods of disseminating research are no longer fit-for-purpose. New approaches for disseminating data, methods and results are required to maximize knowledge discovery. The “long tail” of small, unstructured datasets is well catered for by a number of general-purpose repositories, but there has been less support for “big data”. Outlined here are our experiences in attempting to tackle the gaps in publishing large-scale, computationally intensive research. GigaScience is an open-access, open-data journal aiming to revolutionize large-scale biological data dissemination, organization and re-use. Through use of the data handling infrastructure of the genomics centre BGI, GigaScience links standard manuscript publication with an integrated database (GigaDB) that hosts all associated data, and provides additional data analysis tools and computing resources. Furthermore, the supporting workflows and methods are also integrated to make published articles more transparent and open. GigaDB has released many new and previously unpublished datasets and data types, including as urgently needed data to tackle infectious disease outbreaks, cancer and the growing food crisis. Other “executable” research objects, such as workflows, virtual machines and software from several GigaScience articles have been archived and shared in reproducible, transparent and usable formats. With data citation producing evidence of, and credit for, its use in the wider research community, GigaScience demonstrates a move towards more executable publications. Here data analyses can be reproduced and built upon by users without coding backgrounds or heavy computational infrastructure in a more democratized manner.


Journal of Clinical Investigation | 2004

Profits of public-private partnerships

Laurie Goodman

Long-term, large-scale, scientific endeavors can generate considerable resource pools that can be used by an enormous number of people; they also require a generous amount of capital. One way to fulfill such financial needs is to develop partnerships between the public and private sectors, and the NIH Roadmap includes a section specificially aimed at doing just that. Such undertakings, however, were going on before the creation of this initiative and were part of the reason for its inclusion in the Roadmap. The Mouse Sequencing Consortium is one project completed with money from both the NIH and industry. Another such effort is the Osteoarthritis Initiative, which has been in development since 1999. This program brings together public and private funding to build and carry out a seven-year project that will recruit 5,000 men and women over age 50 who are at high risk for developing osteoarthritis. The goal is to identify biological and structural markers for the progression and development of this disease. Stephen Katz, Director of the National Institute of Arthritis and Musculoskeletal and Skin Disease, one of the leading institutes involved in this initiative, told the JCI, “This is a long-term project where we are going to follow individuals at high risk for developing some symptomatic knee osteoarthritis.” Researchers will take yearly biological samples, images, and clinical data from individuals enrolled in the study. Four sites — the University of Maryland School of Medicine, Baltimore; The Ohio State University, Columbus; the University of Pittsburgh; and the Memorial Hospital of Rhode Island, Pawtucket — were selected in August of 2002 as the data collection centers, and the University of California, San Francisco, was designated the data coordination center. Each site will enroll 1250 adults; the first patients were recruited in February of this year. “The beauty of this [program],” Katz said, “is that — like the genome or other projects that the NIH is looking to support — it is something that is going to be accessible to the world community as quickly as possible.” Data will be released to the public 3 years, 4.5 years, and 7 years into the study. The reason the data are released in chunks, Katz explained, is that “when you are developing data like this, it takes a while to validate it. We decided to start unfolding the information when we have 2500 individuals already registered for the study . . . If we do it on an individual basis, where [the data for] every individual is validated, then put out on the web, it wouldn’t do anybody any good until you get a couple thousand people enrolled, and it would have been much, much more expensive.” Realizing a project of this type, however, requires more than just public money. To aid in developing public and private interactions, the Foundation for the National Institutes of Health, a not-for-profit organization, was established in 1996. Charles Pucie, Senior Advisor for Communications of the Foundation, told the JCI that the organization “was focused much more narrowly in its early conception, with fellowships and so forth. That changed dramatically in [the] middle 90s with the notion that we would work to support all sorts of activities of NIH across the board.” Pucie added, “For each of these major programs . . . a model is developed for the administration and movement of information and for fiscal responsibilities that is pretty much unique to the particular program. This is because most of the partnerships that we have been involved in have been sui generis. In other words, the model for each of the partnerships has been [developed] to best suit the circumstances of the task at hand . . . And that certainly is true in the case of the Osteoarthritis Initiative.” Katz also noted that private institutions provide more than additional funding: “There is a tremendous amount of intellectual capital in these pharmaceutical companies, and they really helped us in terms of identifying what some of the issues were.” Even with the aid of the Foundation, combining the needs of public and private institutions can be tricky. “It is a very delicate matter for the NIH to work with industry,” Katz said. “We have to make sure that our goals are clearly defined . . . if they are in alignment with the goals of industry, that’s great. In this particular endeavor there is no advantage that these contributing companies have in terms of access to data.” Currently three companies, Merck Research Laboratories, Novartis Pharmaceuticals, and Pfizer Inc., support the initiative, contributing


Journal of Clinical Investigation | 2004

Taking the sting out of the anthrax vaccine

Laurie Goodman

800,000 each annually. The total project cost is estimated to be


Journal of Clinical Investigation | 2004

Hard-hearted CRP

Laurie Goodman

55-60 million. But without early access to data or special rights to use any of the samples generated, why would any company get involved? Katz believes there are a number of reasons. “They end up knowing what is going on to a much greater extent if they are involved. But I think they also realize that [it is] important . . . for everyone to have some research resource that they can all tap into to validate what they themselves are doing on their own. It is a resource that is not going to be patentable, but if people utilize material from the resource and make discoveries, their discoveries are patentable.” Pucie added that “the broad thrust of the private funds is that it both accelerates a research project [and] allows for the adding of elements that might not otherwise have been feasible.” Because these companies contribute to government-funded projects, the resultant resources will be more substantial and complete than the private or public sectors can develop working alone. The current work on the Osteoarthritis Initiative and the past success of such joint ventures in the genome project are bolstering support for additional public-private partnerships, including one through the National Institute of Aging focusing on Alzheimer disease. While the future of these efforts remains unknown, these interactions appear to be giving everyone a leg up and will, it is hoped, ultimately benefit those suffering from these diseases.

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Peter Li

Salk Institute for Biological Studies

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Marjorie H. Barnes

University of Massachusetts Medical School

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