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1. Lineage-specific biology revealed by a finished genome assembly of the mouse.

Author

Deanna M Church, Leo Goodstadt, Ladeana W Hillier, Michael C Zody, Steve Goldstein, Xinwe She, Carol J Bult, Richa Agarwala, Joshua L Cherry, Michael DiCuccio, Wratko Hlavina, Yuri Kapustin, Peter Meric, Donna Maglott, Zoë Birtle, Ana C Marques, Tina Graves, Shiguo Zhou, Brian Teague, Konstantinos Potamousis, Christopher Churas, Michael Place, Jill Herschleb, Ron Runnheim, Daniel Forrest, James Amos-Landgraf, David C Schwartz, Ze Cheng, Kerstin Lindblad-Toh, Evan E Eichler, Chris P Ponting, and Mouse Genome Sequencing Consortium

Subjects
Biology (General), QH301-705.5
Abstract

The mouse (Mus musculus) is the premier animal model for understanding human disease and development. Here we show that a comprehensive understanding of mouse biology is only possible with the availability of a finished, high-quality genome assembly. The finished clone-based assembly of the mouse strain C57BL/6J reported here has over 175,000 fewer gaps and over 139 Mb more of novel sequence, compared with the earlier MGSCv3 draft genome assembly. In a comprehensive analysis of this revised genome sequence, we are now able to define 20,210 protein-coding genes, over a thousand more than predicted in the human genome (19,042 genes). In addition, we identified 439 long, non-protein-coding RNAs with evidence for transcribed orthologs in human. We analyzed the complex and repetitive landscape of 267 Mb of sequence that was missing or misassembled in the previously published assembly, and we provide insights into the reasons for its resistance to sequencing and assembly by whole-genome shotgun approaches. Duplicated regions within newly assembled sequence tend to be of more recent ancestry than duplicates in the published draft, correcting our initial understanding of recent evolution on the mouse lineage. These duplicates appear to be largely composed of sequence regions containing transposable elements and duplicated protein-coding genes; of these, some may be fixed in the mouse population, but at least 40% of segmentally duplicated sequences are copy number variable even among laboratory mouse strains. Mouse lineage-specific regions contain 3,767 genes drawn mainly from rapidly-changing gene families associated with reproductive functions. The finished mouse genome assembly, therefore, greatly improves our understanding of rodent-specific biology and allows the delineation of ancestral biological functions that are shared with human from derived functions that are not.

Published
2009
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2. eP369: Increased automation reduces turnaround time for submissions to ClinVar

Author

Melissa Landrum, Shanmuga Chitipiralla, Garth Brown, Chao Chen, Baoshan Gu, Jennifer Hart, Douglas Hoffman, Wonhee Jang, Chunlei Liu, Zenith Maddipatla, Donna Maglott, Rama Maiti, Joseph Mitchell, Tayebeh Rezaie, George Riley, David Shao, George Zhou, Vitaly Lyoshin, Chris Swallow, Kuljeet Kaur, Andrew Russette, and Brandi Kattman

Subjects
Genetics (clinical)
Published
2022

3. ClinVar: improvements to accessing data

Author

Rama Maiti, Chao Chen, Douglas W. Hoffman, J. Bradley Holmes, Donna Maglott, Joseph Mitchell, Baoshan Gu, Garth Brown, Melissa J. Landrum, Brandi L. Kattman, Vitaly Lyoshin, Kuljeet Kaur, George Zhou, George R. Riley, Wonhee Jang, Jennifer Hart, Nuala A. O'Leary, Chunlei Liu, Valerie A. Schneider, Shanmuga Chitipiralla, Wenyao Shi, and Zenith Maddipatla

Subjects
Internet, 0303 health sciences, Information retrieval, National Library of Medicine (U.S.), Genome, Human, computer.internet_protocol, Summary data, Genetic variants, Genetic Variation, Genomics, Biology, United States, Search Engine, 03 medical and health sciences, 0302 clinical medicine, Haplotypes, Databases, Genetic, Genetics, Database Issue, Humans, Disease, computer, 030217 neurology & neurosurgery, XML, 030304 developmental biology
Abstract

ClinVar is a freely available, public archive of human genetic variants and interpretations of their relationships to diseases and other conditions, maintained at the National Institutes of Health (NIH). Submitted interpretations of variants are aggregated and made available on the ClinVar website (https://www.ncbi.nlm.nih.gov/clinvar/), and as downloadable files via FTP and through programmatic tools such as NCBI’s E-utilities. The default view on the ClinVar website, the Variation page, was recently redesigned. The new layout includes several new sections that make it easier to find submitted data as well as summary data such as all diseases and citations reported for the variant. The new design also better represents more complex data such as haplotypes and genotypes, as well as variants that are in ClinVar as part of a haplotype or genotype but have no interpretation for the single variant. ClinVar's variant-centric XML had its production release in April 2019. The ClinVar website and E-utilities both have been updated to support the VCV (variation in ClinVar) accession numbers found in the variant-centric XML file. ClinVar's search engine has been fine-tuned for improved retrieval of search results.

Published
2019

4. SPDI: data model for variants and applications at NCBI

Author

Donna Maglott, J. Bradley Holmes, Lon Phan, Eric Moyer, and Brandi L. Kattman

Subjects
Statistics and Probability, dbSNP, Computer science, Computational biology, Biochemistry, Set (abstract data type), 03 medical and health sciences, 0302 clinical medicine, Databases, Genetic, Subsequence, Genetic variation, RefSeq, Nucleotide, Allele, Molecular Biology, 030304 developmental biology, Sequence (medicine), chemistry.chemical_classification, 0303 health sciences, Genome, Genomics, Original Papers, Computer Science Applications, Computational Mathematics, Projection (relational algebra), Vocabulary, Controlled, Computational Theory and Mathematics, Data model, chemistry, Algorithms, 030217 neurology & neurosurgery, Function (biology), Reference genome
Abstract

MotivationNormalizing diverse representations of sequence variants is critical to the elucidation of the genetic basis of disease and biological function. NCBI has long wrestled with integrating data from multiple submitters to build databases such as dbSNP and ClinVar. Inconsistent representation of variants among variant callers, local databases, and tools results in discrepancies and duplications that complicate analysis. Current tools are not robust enough to manage variants in different formats and different reference sequence coordinates.ResultsThe SPDI (pronounced “speedy”) data model defines variants as a sequence of 4 operations: start at the boundary before the first position in the sequence S, advance P positions, delete D positions, then insert the sequence in the string I, giving the data model its name, SPDI. The SPDI model can thus be applied to both nucleotide and protein variants, but the services discussed here are limited to nucleotide. Current services convert representations between HGVS, VCF, and SPDI and provide two forms of normalization. The first, based on the NCBI Variant Overprecision Correction Algorithm, returns a unique, normalized representation termed the “Contextual Allele” for any input. The SPDI name, with its four operations, defines exactly the reference subsequence potentially affected by the variant, even in low complexity regions such as homopolymer and dinucleotide sequence repeats. The second level of normalization depends on an alignment dataset (ADS). SPDI services perform remapping (AKA lift-over) of variants from the input reference sequence to return a list of all equivalent Contextual Alleles based on the transcript or genomic sequences that were aligned. One of these contextual alleles is selected to represent all, usually that based on the latest genomic assembly such as GRCh38 and is designated as the unique “Canonical Allele”. ADS includes alignments between non-assembly RefSeq sequences (prefixed NM, NR, NG), as well inter- and intra-assembly-associated genomic sequences (NCs, NTs and NWs) and this allow for robust remapping and normalization of variants across sequences and assembly versions.Availability and implementationThe SPDI services are available for open access at: https://api.ncbi.nlm.nih.gov/variation/v0/Contactholmesbr@ncbi.nlm.nih.gov

Published
2019

6. ClinVar: improving access to variant interpretations and supporting evidence

Author

Jennifer M. Lee, George R. Riley, Chen Chao, Kurt McDaniel, Brandi L. Kattman, Donna Maglott, Zenith Maddipatla, Melissa J. Landrum, Baoshan Gu, Douglas W. Hoffman, Kenneth S. Katz, Garth Brown, J. Bradley Holmes, Mark L. Benson, George Zhou, Karen Karapetyan, Chunlei Liu, Shanmuga Chitipiralla, Michael Ovetsky, Malheiro Aj, Wonhee Jang, and Jennifer Hart

Subjects
0301 basic medicine, Variant Call Format, Information retrieval, medicine.diagnostic_test, computer.internet_protocol, MEDLINE, Genetic variants, Genetic Variation, Biology, Set (abstract data type), 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, Phenotype, 030220 oncology & carcinogenesis, Genetics, medicine, Database Issue, Humans, Disease, Databases, Nucleic Acid, computer, XML, Genetic testing
Abstract

ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/) is a freely available, public archive of human genetic variants and interpretations of their significance to disease, maintained at the National Institutes of Health. Interpretations of the clinical significance of variants are submitted by clinical testing laboratories, research laboratories, expert panels and other groups. ClinVar aggregates data by variant-disease pairs, and by variant (or set of variants). Data aggregated by variant are accessible on the website, in an improved set of variant call format files and as a new comprehensive XML report. ClinVar recently started accepting submissions that are focused primarily on providing phenotypic information for individuals who have had genetic testing. Submissions may come from clinical providers providing their own interpretation of the variant (‘provider interpretation’) or from groups such as patient registries that primarily provide phenotypic information from patients (‘phenotyping only’). ClinVar continues to make improvements to its search and retrieval functions. Several new fields are now indexed for more precise searching, and filters allow the user to narrow down a large set of search results.

Published
2017

7. Integrating Genomic Resources with Electronic Health Records using the HL7 Infobutton Standard

Author

Guilherme Del Fiol, Marc S. Williams, Donna Maglott, Casey Lynnette Overby, Tristan Nelson, Bret S. E. Heale, Robert R. Freimuth, Christa Lese Martin, Wendy S. Rubinstein, Scott R. Goehringer, and Aleksandar Milosavljevic

Subjects
Knowledge management, 020205 medical informatics, Interoperability, Health Informatics, 02 engineering and technology, Certification, Health records, Clinical decision support system, User-Computer Interface, 03 medical and health sciences, 0302 clinical medicine, Resource (project management), Health Information Management, 0202 electrical engineering, electronic engineering, information engineering, Data Mining, Electronic Health Records, Medicine, 030212 general & internal medicine, Reference implementation, Workgroup, business.industry, Genomics, Reference Standards, Computer Science Applications, Search Engine, Open source, business, Research Article
Abstract

SummaryThe Clinical Genome Resource (ClinGen) Electronic Health Record (EHR) Workgroup aims to integrate ClinGen resources with EHRs. A promising option to enable this integration is through the Health Level Seven (HL7) Infobutton Standard. EHR systems that are certified according to the US Meaningful Use program provide HL7-compliant infobutton capabilities, which can be leveraged to support clinical decision-making in genomics.To integrate genomic knowledge resources using the HL7 infobutton standard. Two tactics to achieve this objective were: (1) creating an HL7-compliant search interface for ClinGen, and (2) proposing guidance for genomic resources on achieving HL7 Infobutton standard accessibility and compliance.We built a search interface utilizing OpenInfobutton, an open source reference implementation of the HL7 Infobutton standard. ClinGen resources were assessed for readiness towards HL7 compliance. Finally, based upon our experiences we provide recommendations for publishers seeking to achieve HL7 compliance.Eight genomic resources and two sub-resources were integrated with the ClinGen search engine via OpenInfobutton and the HL7 infobutton standard. Resources we assessed have varying levels of readiness towards HL7-compliance. Furthermore, we found that adoption of standard terminologies used by EHR systems is the main gap to achieve compliance.Genomic resources can be integrated with EHR systems via the HL7 Infobutton standard using OpenInfobutton. Full compliance of genomic resources with the Infobutton standard would further enhance interoperability with EHR systems. Citation: Heale BSE, Overby CL, Del Fiol G, Rubinstein WS, Maglott DR, Nelson TH, Milosavljevic A, Martin CL, Goehringer SR, Freimuth RR, Williams MS. Integrating genomic resources with electronic health records using the HL7 Infobutton standard.

Published
2016

8. How the NIH Genetic Testing Registry and Medical Genetics Summaries can help oncologists adopt pharmacogenetics guidelines

Author

Douglas W. Hoffman, Marilu A. Hoeppner, Zenith Maddipatla, Malheiro Aj, Donna Maglott, Wonhee Jang, Chunlei Liu, Joe Mitchell, Baoshan Gu, Shanmuga Chitipiralla, Brandi L. Kattman, George Zhou, Dean Lc, Wenyao Shi, Chao Chen, and Vitaliy Lyoshin

Subjects
Cancer Research, medicine.medical_specialty, medicine.diagnostic_test, business.industry, Patient management, Oncology, medicine, Medical genetics, Medical physics, Dosing, Oncology drugs, business, Pharmacogenetics, Genetic testing
Abstract

e14141 Background: Oncology is one of the few medical specialties where pharmacogenetic dosing is becoming a routine part of patient management. In the US, oncology drugs account for one-third of all drugs with pharmacogenetic data in their labeling. The NIH Genetic Testing Registry (GTR, www.ncbi.nlm.nih.gov/gtr ) is a freely available database of orderable genetic tests voluntarily provided by laboratories, and supports easy identification of both diagnostic and therapy-based tests. Medical Genetics Summaries (MGS, www.ncbi.nlm.nih.gov/books/NBK61999 ) presents actionable pharmacogenetic information by collating guidelines from authoritative professional (e.g. FDA, CPIC) and medical (e.g. ASCO) societies. MGS is regularly updated and can be found by searching PubMed. To increase oncologist adoption of pharmacogenetic recommendations, we analyzed the relationship between genetic tests in GTR and practice guidelines in MGS. Methods: The GTR database was queried to extract content, and we consulted the FDA Table of Pharmacogenomic Biomarkers in Drug Labeling. Results: Of the 90 oncology drugs that have pharmacogenetic information in their labeling, 29 drugs have at least one test for drug response in GTR. The oncology drugs most tested are mercaptopurine and irinotecan with 13 tests each, followed by thioguanine (12) and tamoxifen (9). For the remaining 61 drugs, only one drug (Pertuzumab) has a guideline, which is summarized in MGS. In total, MGS offers 10 summaries focused on oncology drugs. The 90 drug labels mention 49 distinct biomarkers (e.g. genes and variants). All 49 biomarkers can be interrogated using tests via the GTR. In total, GTR has approximately 3,500 tests for cancer diagnostics, 500 tests for cancer management, and 37 pharmacogenetic tests for cancer drugs. Conclusions: Searching for a genetic test in GTR by biomarker (100% coverage) is more efficient than by drug name (32% coverage) because of how laboratories describe their tests. Clinicians need to quickly find the best pharmacogenetic test and how tests are represented in the community affect the ability to search and find them. This presentation will show busy clinicians how to optimally use GTR and MGS. This work was supported by the Intramural Research Program of the National Library of Medicine, National Institutes of Health.

Published
2020

9. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation

Author

Barbara Robbertse, Patrick Masterson, Jinna Choi, Sanjida H. Rangwala, Vyacheslav Brover, Olga Blinkova, Stacy Ciufo, Kim D. Pruitt, Conrad L. Schoch, Daniel H. Haft, Kelly M. McGarvey, Richard McVeigh, Raymond E. Tully, Bhanu Rajput, Alexander Astashyn, Vinita Joardar, Wratko Hlavina, Vamsi K. Kodali, Kathleen O'Neill, Danso Ako-adjei, Hanzhen Sun, Craig Wallin, Mathew W. Wright, Michael DiCuccio, Daniel Rausch, Catherine M. Farrell, Susan S. Storz, Avi Kimchi, Terence Murphy, Tripti Gupta, Eneida L. Hatcher, Donna Maglott, Shashikant Pujar, Brian Smith-White, David Webb, Nuala A. O'Leary, Wenjun Li, Michael R. Murphy, Igor Tolstoy, Françoise Thibaud-Nissen, Diana Haddad, Olga Ermolaeva, Azat Badretdin, Andrei Shkeda, Lillian D. Riddick, Tatiana Tatusova, Wendy Wu, Melissa J. Landrum, Vyacheslav Chetvernin, Tamara Goldfarb, Anjana R. Vatsan, Paul Kitts, J. Rodney Brister, Yiming Bao, and Eric Cox

Subjects
0301 basic medicine, Nematoda, Genomics, Genome, Viral, Biology, computer.software_genre, Mice, 03 medical and health sciences, Sequence Analysis, Protein, Databases, Genetic, Genetics, RefSeq, Animals, Humans, Database Issue, Phylogeny, Comparative genomics, Database, Genome, Human, Sequence Analysis, RNA, GENCODE, Gene Expression Profiling, Molecular Sequence Annotation, Genome project, Reference Standards, Invertebrates, Rats, Genome, Microbial, 030104 developmental biology, Vertebrates, Cattle, RNA, Long Noncoding, Human genome, Genome, Fungal, computer, Genome, Plant, Reference genome
Abstract

The RefSeq project at the National Center for Biotechnology Information (NCBI) maintains and curates a publicly available database of annotated genomic, transcript, and protein sequence records (http://www.ncbi.nlm.nih.gov/refseq/). The RefSeq project leverages the data submitted to the International Nucleotide Sequence Database Collaboration (INSDC) against a combination of computation, manual curation, and collaboration to produce a standard set of stable, non-redundant reference sequences. The RefSeq project augments these reference sequences with current knowledge including publications, functional features and informative nomenclature. The database currently represents sequences from more than 55 000 organisms (>4800 viruses, >40 000 prokaryotes and >10 000 eukaryotes; RefSeq release 71), ranging from a single record to complete genomes. This paper summarizes the current status of the viral, prokaryotic, and eukaryotic branches of the RefSeq project, reports on improvements to data access and details efforts to further expand the taxonomic representation of the collection. We also highlight diverse functional curation initiatives that support multiple uses of RefSeq data including taxonomic validation, genome annotation, comparative genomics, and clinical testing. We summarize our approach to utilizing available RNA-Seq and other data types in our manual curation process for vertebrate, plant, and other species, and describe a new direction for prokaryotic genomes and protein name management.

Published
2015
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10. Principles and Recommendations for Standardizing the Use of the Next-Generation Sequencing Variant File in Clinical Settings

Author

Fiona Hyland, Elizabeth A. Worthey, Mollie Ullman-Cullere, Ira M. Lubin, John D. Pfeifer, Karl V. Voelkerding, Edward R. Lockhart, Gabor T. Marth, Shaun Cordes, Alexander Wait Zaranek, Deanna M. Church, Dennis G. Ballinger, Himani Bisht, Karen Eilbeck, R Truty, Nazneen Aziz, Lisa V. Kalman, Zivana Tezak, Heidi L. Rehm, Lawrence J. Babb, Donna Maglott, Justin M. Zook, Melissa J. Landrum, and Somak Roy

Subjects
0301 basic medicine, Flexibility (engineering), Information retrieval, Sequence analysis, Computer science, media_common.quotation_subject, Genetic Variation, High-Throughput Nucleotide Sequencing, Regular Article, Variation (game tree), Ambiguity, Sequence Analysis, DNA, Bioinformatics, Pathology and Forensic Medicine, Variety (cybernetics), 03 medical and health sciences, 030104 developmental biology, Databases, Genetic, Molecular Medicine, Humans, Workgroup, Software, Sequence (medicine), media_common, Reference genome
Abstract

A national workgroup convened by the Centers for Disease Control and Prevention identified principles and made recommendations for standardizing the description of sequence data contained within the variant file generated during the course of clinical next-generation sequence analysis for diagnosing human heritable conditions. The specifications for variant files were initially developed to be flexible with regard to content representation to support a variety of research applications. This flexibility permits variation with regard to how sequence findings are described and this depends, in part, on the conventions used. For clinical laboratory testing, this poses a problem because these differences can compromise the capability to compare sequence findings among laboratories to confirm results and to query databases to identify clinically relevant variants. To provide for a more consistent representation of sequence findings described within variant files, the workgroup made several recommendations that considered alignment to a common reference sequence, variant caller settings, use of genomic coordinates, and gene and variant naming conventions. These recommendations were considered with regard to the existing variant file specifications presently used in the clinical setting. Adoption of these recommendations is anticipated to reduce the potential for ambiguity in describing sequence findings and facilitate the sharing of genomic data among clinical laboratories and other entities.

Published
2017

11. Characterizing genetic variants for clinical action

Author

Ned Calonge, Erynn S. Gordon, Robert S. Epstein, Howard P. Levy, Michael Dunn, Jonathan S. Berg, Naomi Aronson, Teri A. Manolio, Peter Good, David H. Ledbetter, Marc S. Williams, Leslie G. Biesecker, Gail P. Jarvik, Paul Flicek, Kensaku Kawamoto, Robert C. Green, Catherine Wicklund, Donna Maglott, Helen M. Colhoun, Lisa D. Brooks, Erin M. Ramos, Nazneen Rahman, Gurvaneet Randhawa, William A. Knaus, Christopher J. O'Donnell, Elaine Lyon, Eric D. Green, Audrey Duncanson, Rex L. Chisholm, Stephen T. Sherry, Tim Hubbard, Bruce Blumberg, Howard L. McLeod, Corina Din-Lovinescu, and Matthew E. Hurles

Subjects
business.industry, Genome-wide association study, Computational biology, Bioinformatics, Precision medicine, Health informatics, Knowledge base, Pharmacogenomics, Genetics, Genetic predisposition, Medicine, Human genome, business, Genetics (clinical), Genetic association
Abstract

Genome-wide association studies, DNA sequencing studies, and other genomic studies are finding an increasing number of genetic variants associated with clinical phenotypes that may be useful in developing diagnostic, preventive, and treatment strategies for individual patients. However, few common variants have been integrated into routine clinical practice. The reasons for this are several, but two of the most significant are limited evidence about the clinical implications of the variants and a lack of a comprehensive knowledge base that captures genetic variants, their phenotypic associations, and other pertinent phenotypic information that is openly accessible to clinical groups attempting to interpret sequencing data. As the field of medicine begins to incorporate genome-scale analysis into clinical care, approaches need to be developed for collecting and characterizing data on the clinical implications of variants, developing consensus on their actionability, and making this information available for clinical use. The National Human Genome Research Institute (NHGRI) and the Wellcome Trust thus convened a workshop to consider the processes and resources needed to: 1) identify clinically valid genetic variants; 2) decide whether they are actionable and what the action should be; and 3) provide this information for clinical use. This commentary outlines the key discussion points and recommendations from the workshop.

Published
2014

12. Systematic documentation and analysis of human genetic variation in hemoglobinopathies using the microattribution approach

Author

Petros Papadopoulos, Swee Lay Thein, Kenneth R. Peterson, Iris Schrijver, Kamran Moradkhani, Sonja Pavlovic, Barnaby Clark, James D. Hoyer, Raymond E. Tully, Philippe Joly, Alain Francina, D J Anstee, Lucia Perseu, Ross C. Hardison, Emmanuel Kanavakis, Halyna Fedosyuk, Stephan Menzel, Douglas R. Higgs, Maja Stojiljkovic, Henri Wajcman, Belinda Giardine, Stefania Satta, Belinda K. Singleton, Marianthi Georgitsi, John S. Waye, Webb Miller, George P. Patrinos, Branka Zukic, Sjaak Philipsen, Milena Radmilovic, J. Traeger-Synodinos, Flávia C. Costa, Donna Maglott, David H.K. Chui, Monica V.E. Gallivan, Panagoula Kollia, Martin Jarvis, A. Nazli Basak, Cornelis L. Harteveld, Adamantia Papachatzopoulou, Richard J. Gibbons, Joseph Borg, John Old, Manoussos N. Papadakis, Claudia Wiemann, Renzo Galanello, Piero C. Giordano, Paula Faustino, Cathy Riemer, Alex E. Felice, and Takahito Wada

Subjects
thalassemia, Molecular Sequence Data, Human genetic variation, Computational biology, Biology, Genome, Article, Hemoglobins, 03 medical and health sciences, 0302 clinical medicine, Documentation, Databases, Genetic, Human Genome Project, Genetic variation, Genetics, medicine, locus-specific databases, Data Mining, Humans, microattribution, Genetic variability, Promoter Regions, Genetic, hemoglobinopathies, 030304 developmental biology, Publishing, 0303 health sciences, Base Sequence, Genome, Human, Genetic Variation, DNA, medicine.disease, Doenças Genéticas, Hemoglobinopathies, Hemoglobinopathy, 030220 oncology & carcinogenesis, Mutation, Mutation (genetic algorithm), Human genome
Abstract

We developed a series of interrelated locus-specific databases to store all published and unpublished genetic variation related to these disorders, and then implemented microattribution to encourage submission of unpublished observations of genetic variation to these public repositories 1. A total of 1,941 unique genetic variants in 37 genes, encoding globins (HBA2, HBA1, HBG2, HBG1, HBD, HBB) and other erythroid proteins (ALOX5AP, AQP9, ARG2, ASS1, ATRX, BCL11A, CNTNAP2, CSNK2A1, EPAS1, ERCC2, FLT1, GATA1, GPM6B, HAO2, HBS1L, KDR, KL, KLF1, MAP2K1, MAP3K5, MAP3K7, MYB, NOS1, NOS2, NOS3, NOX3, NUP133, PDE7B, SMAD3, SMAD6, and TOX) are currently documented in these databases with reciprocal attribution of microcitations to data contributors. Our project provides the first example of implementing microattribution to incentivise submission of all known genetic variation in a defined system. It has demonstrably increased the reporting of human variants and now provides a comprehensive online resource for systematically describing human genetic variation in the globin genes and other genes contributing to hemoglobinopathies and thalassemias. The large repository of previously reported data, together with more recent data, acquired by microattribution, demonstrates how the comprehensive documentation of human variation will provide key insights into normal biological processes and how these are perturbed in human genetic disease. Using the microattribution process set out here, datasets which took decades to accumulate for the globin genes could be assembled rapidly for other genes and disease systems. The principles established here for the globin gene system will serve as a model for other systems and the analysis of other common and/or complex human genetic diseases.

Published
2016

13. Using ClinVar as a Resource to Support Variant Interpretation

Author

Danielle R. Azzariti, Steven M. Harrison, Jennifer M. Lee, Erin Rooney Riggs, Erin M. Ramos, Donna Maglott, Annie Niehaus, Melissa J. Landrum, Heidi L. Rehm, and Christa Lese Martin

Subjects
0301 basic medicine, Information retrieval, Genome, Human, Computer science, Interpretation (philosophy), Genetic Variation, computer.software_genre, Article, Data sharing, 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, Resource (project management), 030220 oncology & carcinogenesis, Databases, Genetic, Genetics, Humans, Data mining, computer, Genetics (clinical)
Abstract

ClinVar is a freely accessible, public archive of reports of the relationships among genomic variants and phenotypes. To facilitate evaluation of the clinical significance of each variant, ClinVar aggregates submissions of the same variant, displays supporting data from each submission, and determines if the submitted clinical interpretations are conflicting or concordant. The unit describes how to (1) identify sequence and structural variants of interest in ClinVar with by multiple searching approaches, including Variation Viewer and (2) understand the display of submissions to ClinVar and the evidence supporting each interpretation. By following this protocol, ClinVar users will be able to learn how to incorporate the wealth of resources and knowledge in ClinVar into variant curation and interpretation.

Published
2016

14. Clone DB: an integrated NCBI resource for clone-associated data

Author

Hsiu-Chuan Chen, Peter Meric, Nora Husain, Cliff Clausen, Zhigang Zhou, Valerie A. Schneider, Deanna M. Church, Nathan Bouk, and Donna Maglott

Subjects
Genetics, Internet, Clone (cell biology), Chromosome Mapping, Sequence Analysis, DNA, Articles, Resource (Windows), Biology, Genome, DNA sequencing, Systems Integration, Entrez, Identifier, Mice, Important research, Animals, Humans, Cloning, Molecular, Databases, Nucleic Acid, Gene, Gene Library
Abstract

The National Center for Biotechnology Information (NCBI) Clone DB (http://www.ncbi.nlm.nih.gov/clone/) is an integrated resource providing information about and facilitating access to clones, which serve as valuable research reagents in many fields, including genome sequencing and variation analysis. Clone DB represents an expansion and replacement of the former NCBI Clone Registry and has records for genomic and cell-based libraries and clones representing more than 100 different eukaryotic taxa. Records provide details of library construction, associated sequences, map positions and information about resource distribution. Clone DB is indexed in the NCBI Entrez system and can be queried by fields that include organism, clone name, gene name and sequence identifier. Whenever possible, genomic clones are mapped to reference assemblies and their map positions provided in clone records. Clones mapping to specific genomic regions can also be searched for using the NCBI Clone Finder tool, which accepts queries based on sequence coordinates or features such as gene or transcript names. Clone DB makes reports of library, clone and placement data on its FTP site available for download. With Clone DB, users now have available to them a centralized resource that provides them with the tools they will need to make use of these important research reagents.

Published
2012

15. The NIH genetic testing registry: a new, centralized database of genetic tests to enable access to comprehensive information and improve transparency

Author

Michael Ovetsky, Brandi L. Kattman, Kathy L. Hudson, Malheiro Aj, Viatcheslav Gorelenkov, Guangfeng Song, Wonhee Jang, Kenneth S. Katz, Mikhail Denisenko, Wendy S. Rubinstein, Craig Wallin, Mark D. Johnson, Fedor Karmanov, Shanmuga Chitipiralla, Nora Husain, James Ostell, Douglas W. Hoffman, Jennifer M. Lee, Vichet Hem, Alexander Ukrainchik, Donna Maglott, and Cathy Fomous

Subjects
Internet, File Transfer Protocol, medicine.diagnostic_test, business.industry, MEDLINE, Genetic Variation, Articles, Certification, Biology, Test (assessment), World Wide Web, Identifier, Centralized database, Phenotype, Genes, Databases, Genetic, Genetics, medicine, Humans, The Internet, Genetic Testing, Registries, business, Genetic testing
Abstract

The National Institutes of Health Genetic Testing Registry (GTR; available online at http://www.ncbi.nlm.nih.gov/gtr/) maintains comprehensive information about testing offered worldwide for disorders with a genetic basis. Information is voluntarily submitted by test providers. The database provides details of each test (e.g. its purpose, target populations, methods, what it measures, analytical validity, clinical validity, clinical utility, ordering information) and laboratory (e.g. location, contact information, certifications and licenses). Each test is assigned a stable identifier of the format GTR000000000, which is versioned when the submitter updates information. Data submitted by test providers are integrated with basic information maintained in National Center for Biotechnology Information's databases and presented on the web and through FTP (ftp.ncbi.nih.gov/pub/GTR/_README.html).

Published
2012

16. NCBI Reference Sequences (RefSeq): current status, new features and genome annotation policy

Author

Garth Brown, Donna Maglott, Tatiana Tatusova, and Kim D. Pruitt

Subjects
Sequence profiling tool, GENCODE, Sequence Analysis, RNA, Molecular Sequence Annotation, Genome project, Computational biology, Articles, Genomics, Sequence Analysis, DNA, Biology, Reference Standards, Bioinformatics, Genome, Annotation, Sequence Analysis, Protein, Databases, Genetic, Genetics, RefSeq, Humans, Sequence Analysis, Reference genome
Abstract

The National Center for Biotechnology Information (NCBI) Reference Sequence (RefSeq) database is a collection of genomic, transcript and protein sequence records. These records are selected and curated from public sequence archives and represent a significant reduction in redundancy compared to the volume of data archived by the International Nucleotide Sequence Database Collaboration. The database includes over 16,00 organisms, 2.4 × 0(6) genomic records, 13 × 10(6) proteins and 2 × 10(6) RNA records spanning prokaryotes, eukaryotes and viruses (RefSeq release 49, September 2011). The RefSeq database is maintained by a combined approach of automated analyses, collaboration and manual curation to generate an up-to-date representation of the sequence, its features, names and cross-links to related sources of information. We report here on recent growth, the status of curating the human RefSeq data set, more extensive feature annotation and current policy for eukaryotic genome annotation via the NCBI annotation pipeline. More information about the resource is available online (see http://www.ncbi.nlm.nih.gov/RefSeq/).

Published
2011

17. Entrez Gene: gene-centered information at NCBI

Author

Tatiana Tatusova, Kim D. Pruitt, Donna Maglott, and James Ostell

Subjects
Sequence profiling tool, Internet, National Library of Medicine (U.S.), Sequence analysis, Entrez Gene, HUGO Gene Nomenclature Committee, food and beverages, Computational biology, Articles, Genomics, Biology, Bioinformatics, United States, Entrez, User-Computer Interface, Genes, Databases, Genetic, RefSeq, Genetics, natural sciences, GeneRIF, Reference genome
Abstract

Entrez Gene (www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene) is NCBI's database for gene-specific information. It does not include all known or predicted genes; instead Entrez Gene focuses on the genomes that have been completely sequenced, that have an active research community to contribute gene-specific information, or that are scheduled for intense sequence analysis. The content of Entrez Gene represents the result of curation and automated integration of data from NCBI's Reference Sequence project (RefSeq), from collaborating model organism databases, and from many other databases available from NCBI. Records are assigned unique, stable and tracked integers as identifiers. The content (nomenclature, map location, gene products and their attributes, markers, phenotypes, and links to citations, sequences, variation details, maps, expression, homologs, protein domains and external databases) is updated as new information becomes available. Entrez Gene is a step forward from NCBI's LocusLink, with both a major increase in taxonomic scope and improved access through the many tools associated with NCBI Entrez.

Published
2010

18. NCBI Reference Sequences: current status, policy and new initiatives

Author

Kim D. Pruitt, Donna Maglott, William Klimke, and Tatiana Tatusova

Subjects
Genetics, RNA, Untranslated, Sequence analysis, Pseudogene, Proteins, Genomics, Articles, Exons, Computational biology, Genome project, Reference Standards, Biology, Genome, Mice, Annotation, Databases, Genetic, RefSeq, Animals, Humans, Sequence Analysis, Pseudogenes, Reference genome
Abstract

NCBI's Reference Sequence (RefSeq) database (http://www.ncbi.nlm.nih.gov/RefSeq/) is a curated non-redundant collection of sequences representing genomes, transcripts and proteins. RefSeq records integrate information from multiple sources and represent a current description of the sequence, the gene and sequence features. The database includes over 5300 organisms spanning prokaryotes, eukaryotes and viruses, with records for more than 5.5 x 10(6) proteins (RefSeq release 30). Feature annotation is applied by a combination of curation, collaboration, propagation from other sources and computation. We report here on the recent growth of the database, recent changes to feature annotations and record types for eukaryotic (primarily vertebrate) species and policies regarding species inclusion and genome annotation. In addition, we introduce RefSeqGene, a new initiative to support reporting variation data on a stable genomic coordinate system.

Published
2009

19. Human immunodeficiency virus type 1, human protein interaction database at NCBI

Author

Donna Maglott, Roger G. Ptak, Kenneth S. Katz, Kim D. Pruitt, William Fu, and Brigitte E. Sanders-Beer

Subjects
MEDLINE, Biology, computer.software_genre, gag Gene Products, Human Immunodeficiency Virus, Protein–protein interaction, Viral Proteins, 03 medical and health sciences, Protein Interaction Mapping, Computer Graphics, Genetics, RefSeq, Humans, HIV vaccine, Databases, Protein, GeneRIF, 030304 developmental biology, Acquired Immunodeficiency Syndrome, 0303 health sciences, Database, Entrez Gene, 030302 biochemistry & molecular biology, Proteins, Articles, 3. Good health, HIV-1, Identification (biology), computer, Reference genome
Abstract

The 'Human Immunodeficiency Virus Type 1 (HIV-1), Human Protein Interaction Database', available through the National Library of Medicine at www.ncbi.nlm.nih.gov/RefSeq/HIVInteractions, was created to catalog all interactions between HIV-1 and human proteins published in the peer-reviewed literature. The database serves the scientific community exploring the discovery of novel HIV vaccine candidates and therapeutic targets. To facilitate this discovery approach, the following information for each HIV-1 human protein interaction is provided and can be retrieved without restriction by web-based downloads and ftp protocols: Reference Sequence (RefSeq) protein accession numbers, Entrez Gene identification numbers, brief descriptions of the interactions, searchable keywords for interactions and PubMed identification numbers (PMIDs) of journal articles describing the interactions. Currently, 2589 unique HIV-1 to human protein interactions and 5135 brief descriptions of the interactions, with a total of 14,312 PMID references to the original articles reporting the interactions, are stored in this growing database. In addition, all protein-protein interactions documented in the database are integrated into Entrez Gene records and listed in the 'HIV-1 protein interactions' section of Entrez Gene reports. The database is also tightly linked to other databases through Entrez Gene, enabling users to search for an abundance of information related to HIV pathogenesis and replication.

Published
2009

20. Short Communication: Cataloguing the HIV Type 1 Human Protein Interaction Network

Author

Mikhail N. Rozanov, Donna Maglott, Carl W. Dieffenbach, Jonathan E. Dickerson, John W. Pinney, Brigitte E Sanders-Beer, Kenneth S. Katz, Roger G. Ptak, Kim D. Pruitt, David Robertson, and William Fu

Subjects
Regulation of gene expression, Entrez Gene, Immunology, Computational biology, Biology, Bioinformatics, Protein–protein interaction, Entrez, Infectious Diseases, Interaction network, Virology, RefSeq, Identification (biology), Protein kinase A
Abstract

Although many interactions between HIV-1 and human proteins have been reported in the scientific literature, no publicly accessible source for efficiently reviewing this information was available. Therefore, a project was initiated in an attempt to catalogue all published interactions between HIV-1 and human proteins. HIV-related articles in PubMed were used to develop a database containing names, Entrez GeneIDs, and RefSeq protein accession numbers of interacting proteins. Furthermore, brief descriptions of the interactions, PubMed identification numbers of articles describing the interactions, and keywords for searching the interactions were incorporated. Over 100,000 articles were reviewed, resulting in the identification of 1448 human proteins that interact with HIV-1 comprising 2589 unique HIV-1-to-human protein interactions. Preliminary analysis of the extracted data indicates 32% were direct physical interactions (e.g., binding) and 68% were indirect interactions (e.g., upregulation through activation of signaling pathways). Interestingly, 37% of human proteins in the database were found to interact with more than one HIV-1 protein. For example, the signaling protein mitogen-activated protein kinase 1 has a surprising range of interactions with 10 different HIV-1 proteins. Moreover, large numbers of interactions were published for the HIV-1 regulatory protein Tat and envelope proteins: 30% and 33% of total interactions identified, respectively. The database is accessible at http://www.ncbi.nlm.nih.gov/RefSeq/HIVInteractions/ and is cross-linked to other National Center for Biotechnology Information databases and programs via Entrez Gene. This database represents a unique and continuously updated scientific resource for understanding HIV-1 replication and pathogenesis to assist in accelerating the development of effective therapeutic and vaccine interventions.

Published
2008

21. What everybody should know about the rat genome and its online resources

Author

Howard J. Jacob, Jim Kent, Xosé M. Fernández-Suárez, Ewan Birney, George M. Weinstock, Kim D. Pruitt, Richard A. Gibbs, Kim C. Worley, Donna Maglott, Donna Karolchik, Garth Brown, and Simon N. Twigger

Subjects
Complex disease, Genome browser, Computational biology, Biology, Polymorphism, Single Nucleotide, Genome, Rats, Mutant Strains, Article, Rat Genome Database, Databases, Genetic, Genetics, RefSeq, Animals, Humans, Ensembl, Gene, Whole genome sequencing, Internet, Genetic Diseases, Inborn, Computational Biology, Genetic Variation, Genomics, Sequence Analysis, DNA, Rats, Disease Models, Animal, Haplotypes
Abstract

It has been four years since the original publication of the draft sequence of the rat genome. Five groups are now working together to assemble, annotate and release an updated version of the rat genome. As the prevailing model for physiology, complex disease and pharmacological studies, there is an acute need for the rat's genomic resources to keep pace with the rat's prominence in the laboratory. In this commentary, we describe the current status of the rat genome sequence and the plans for its impending 'upgrade'. We then cover the key online resources providing access to the rat genome, including the new SNP views at Ensembl, the RefSeq and Genes databases at the US National Center for Biotechnology Information, Genome Browser at the University of California Santa Cruz and the disease portals for cardiovascular disease and obesity at the Rat Genome Database.

Published
2008

22. Reference Databases for Disease Associations

Author

Donna Maglott, Deanna M. Church, and Wendy S. Rubinstein

Subjects
medicine.medical_specialty, dbSNP, Database, Genome-wide association study, Context (language use), Disease, Biology, computer.software_genre, Data sharing, Variation (linguistics), medicine, Medical genetics, Baseline (configuration management), computer
Abstract

Making sense of the human variation in the context of human disease has many challenges. A medical genetics professional faced with a list of identified variants must be able to evaluate, and reevaluate, whether there are prior reports about any variant, what the current interpretation of that variant might be, and/or if there are predicted consequences. The sources of publicly available information are diverse, so the genetics professional needs to know not only how to find information, but also how large centralized databases and datasets are maintained in case a current interpretation is not highly supported. This chapter provides an overview of how human genetic information is ascertained—both the common variation to provide the baseline of information about variation unlikely to be the primary cause of a disorder, and the rarer variation that may contribute to disease. It summarizes the features of major databases that archive submissions about variation (dbSNP, dbVar), about variation–disease associations (dbGaP), and clinical interpretations of variation (ClinVar). It also summarizes major tools that support access to these data by several paths, e.g., by location on the genome, by gene, and by disease or measured phenotype. The chapter also describes community efforts to encourage data sharing and to develop standards that promote the development of clinical grade databases.

Published
2015

23. Mitochondrial Disease Sequence Data Resource (MSeqDR): a global grass-roots consortium to facilitate deposition, curation, annotation, and integrated analysis of genomic data for the mitochondrial disease clinical and research communities

Author

Xiaowu Gai, Jeremy Leipzig, Aurora Pujol, Richard G. Boles, Deanna M. Church, Finley Macrae, Erin Rooney Riggs, Michio Hirano, Mariangela Santorsola, Yaffa R. Rubinstein, Rosanna Clima, Marie T. Lott, Penelope E. Bonnen, Christine M. Stanley, David Dimmock, Heidi L. Rehm, Marni J. Falk, Giuseppe Gasparre, Holger Prokisch, Shamima Rahman, Claire A. Sheldon, Anu Suomalainen, Hakon Hakonarson, Sarah E. Calvo, Melissa A. Parisi, Alphons P. M. Stassen, Zhe Zhang, Katrina Gwinn, Johan L.K. Van Hove, Lee-Jun C. Wong, Lisa D. Brooks, Virginia Brilhante, William C. Copeland, Jeana T. DaRe, Curt Scharfe, Michael A. Gonzalez, Johan T. den Dunnen, I.F.M. de Coo, Iris L. Gonzalez, Claudia Calabrese, Yasushi Okazaki, Vamsi K. Mootha, Lynne A. Wolfe, Douglas S. Kerr, Doron M. Behar, Bert Smeets, John Christodoulou, Juan C. Perin, Stephan Züchner, Lishuang Shen, Eric A. Shoubridge, Sihoun Hahn, Danuta Krotoski, Sharon F. Terry, Domenico Simone, David Ralph, Renkui Bai, Olga Derbenevoa, Honey V. Reddi, Eric A. Pierce, Daniel Navarro-Gomez, Gregory M. Enns, Vincent Procaccio, Russell P. Saneto, Mannis van Oven, Philip E. Yeske, David R. Thorburn, Bruce H. Cohen, Maria Lvova, Robert Shelton, Douglas C. Wallace, Maria Angela Diroma, Marcella Attimonelli, Dong Li, Elizabeth M. McCormick, Amy Goldstein, Mark A. Tarnopolsky, Patrick F. Chinnery, Donna Maglott, Richard J. Rodenburg, Jirair K. Bedoyan, Richard G.H. Cotton, Richard H. Haas, Jan A.M. Smeitink, Grant A. Mitchell, Isabelle Thiffault, Sherri J. Bale, RS: CARIM - R2 - Cardiac function and failure, RS: GROW - Developmental Biology, Genetica & Celbiologie, Klinische Genetica, RS: GROW - R4 - Reproductive and Perinatal Medicine, Falk M.J., Shen L., Gonzalez M., Leipzig J., Lott M.T., Stassen A.P.M., Diroma M.A., Navarro-Gomez D., Yeske P., Bai R., Boles R.G., Brilhante V., Ralph D., DaRe J.T., Shelton R., Terry S.F., Zhang Z., Copeland W.C., van Oven M., Prokisch H., Wallace D.C., Attimonelli M., Krotoski D., Zuchner S., Gai X., Bale S., Bedoyan J., Behar D., Bonnen P., Brooks L., Calabrese C., Calvo S., Chinnery P., Christodoulou J., Church D.t, Clima R., Cohen B.H., Cotton R.G., de Coo I.F.M., Derbenevoa O., den Dunnen J.T., Dimmock D., Enns G., Gasparre G., Goldstein A., Gonzalez I., Gwinn K., Hahn S., Haas R.H., Hakonarson H., Hirano M., Kerr D., Li D., Lvova M., Macrae F., Maglott D., McCormick E., Mitchell G., Mootha V.K., Okazaki Y., Pujol A., Parisi M., Perin J.C., Pierce E.A., Procaccio V., Rahman S., Reddi H., Rehm H., Riggs E., Rodenburg R., Rubinstein Y., Saneto R., Santorsola M., Scharfe C., Sheldon C., Shoubridge E.A., Simone D., Smeets B., Smeitink J.A., Stanley C., Suomalainen A., Tarnopolsky M., Thiffault I., Thorburn D.R., Hove J.V., Wolfe L., Wong L.J., and Genetic Identification

Subjects
Male, Mitochondrial DNA, Mitochondrial Diseases, DATABASE, Endocrinology, Diabetes and Metabolism, Mitochondrial disease, Genomics, mitochondrial DNA, Computational biology, Biology, Biochemistry, Genome, Article, 03 medical and health sciences, Annotation, User-Computer Interface, 0302 clinical medicine, Endocrinology, Data visualization, Human Phenotype Ontology, Databases, Genetic, Genetics, medicine, Humans, Exome, Molecular Biology, 030304 developmental biology, 0303 health sciences, Internet, business.industry, Information Dissemination, Computational Biology, Metabolic Disorders Radboud Institute for Molecular Life Sciences [Radboudumc 6], medicine.disease, 3. Good health, Data sharing, Phenotype, Genome, Mitochondrial, Female, business, 030217 neurology & neurosurgery, Software
Abstract

Success rates for genomic analyses of highly heterogeneous disorders can be greatly improved if a large cohort of patient data is assembled to enhance collective capabilities for accurate sequence variant annotation, analysis, and interpretation. Indeed, molecular diagnostics requires the establishment of robust data resources to enable data sharing that informs accurate understanding of genes, variants, and phenotypes. The "Mitochondrial Disease Sequence Data Resource (MSeqDR) Consortium" is a grass-roots effort facilitated by the United Mitochondria] Disease Foundation to identify and prioritize specific genomic data analysis needs of the global mitochondrial disease clinical and research community. A central Web portal (https://mseqdr.org) facilitates the coherent compilation, organization, annotation, and analysis of sequence data from both nuclear and mitochondrial genomes of individuals and families with suspected mitochondria! disease. This Web portal provides users with a flexible and expandable suite of resources to enable variant-, gene-, and exome-level sequence analysis in a secure, Web-based, and user-friendly fashion. Users can also elect to share data with other MSeqDR Consortium members, or even the general public, either by custom annotation tracks or through the use of a convenient distributed annotation system (DAS) mechanism. A range of data visualization and analysis tools are provided to facilitate user interrogation and understanding of genomic, and ultimately phenotypic, data of relevance to mitochondrial biology and disease. Currently available tools for nuclear and mitochondrial gene analyses include an MSeqDR GBrowse instance that hosts optimized mitochondrial disease and mitochondrial DNA (mtDNA) specific annotation tracks, as well as an MSeqDR locus-specific database (LSDB) that curates variant data on more than 1300 genes that have been implicated in mitochondrial disease and/or encode mitochondria-localized proteins. MSeqDR is integrated with a diverse array of mtDNA data analysis tools that are both freestanding and incorporated into an online exome-level dataset curation and analysis resource (GEM.app) that is being optimized to support needs of the MSeqDR community. In addition, MSeqDR supports mitochondrial disease phenotyping and ontology tools, and provides variant pathogenicity assessment features that enable community review, feedback, and integration with the public ClinVar variant annotation resource. A centralized Web-based informed consent process is being developed, with implementation of a Global Unique Identifier (GUID) system to integrate data deposited on a given individual from different sources. Community-based data deposition into MSeqDR has already begun. Future efforts will enhance capabilities to incorporate phenotypic data that enhance genomic data analyses. MSeqDR will fill the existing void in bioinformatics tools and centralized knowledge that are necessary to enable efficient nuclear and mtDNA genomic data interpretation by a range of shareholders across both clinical diagnostic and research settings. Ultimately, MSeqDR is focused on empowering the global mitochondrial disease community to better define and explore mitochondrial diseases. (C) 2014 Elsevier Inc. All rights reserved.

Published
2015

24. Connecting Sequence and Biology in the Laboratory Mouse

Author

Joel E. Richardson, Lois J. Maltais, Donna Maglott, Richard M. Baldarelli, Martin Ringwald, Benjamin L. King, Deanna M. Church, James A. Kadin, Lynn M. Schriml, Lori E. Corbani, Janan T. Eppig, Jun Adachi, Takeya Kasukawa, Louise M. McKenzie, Kim D. Pruitt, Longlong Yang, Yunxia Zhu, David P. Hill, Sharon Cousins, Deborah J Reed, Masaaki Furuno, Dong Qi, Sridhar Ramachandran, Judith A. Blake, Kenneth S. Frazer, Dirck W. Bradt, and Carol J. Bult

Subjects
DNA, Complementary, ved/biology.organism_classification_rank.species, Context (language use), Computational biology, Mouse Genome Informatics, Biology, Genome, DNA sequencing, Transcriptome, Mice, Databases, Genetic, Computer Graphics, Genetics, Animals, natural sciences, Model organism, Gene, Genetics (clinical), Internet, Base Sequence, ved/biology, Computational Biology, Resources, Genes, Informatics
Abstract

The Mouse Genome Sequencing Consortium and the RIKEN Genome Exploration Research grouphave generated large sets of sequence data representing the mouse genome and transcriptome, respectively. These data provide a valuable foundation for genomic research. The challenges for the informatics community are how to integrate these data with the ever-expanding knowledge about the roles of genes and gene products in biological processes, and how to provide useful views to the scientific community. Public resources, such as the National Center for Biotechnology Information (NCBI; http://www.ncbi.nih.gov), and model organism databases, such as the Mouse Genome Informatics database (MGI; http://www.informatics.jax.org), maintain the primary data and provide connections between sequence and biology. In this paper, we describe how the partnership of MGI and NCBI LocusLink contributes to the integration of sequence and biology, especially in the context of the large-scale genome and transcriptome data now available for the laboratory mouse. In particular, we describe the methods and results of integration of 60,770 FANTOM2 mouse cDNAs with gene records in the databases of MGI and LocusLink.

Published
2003

25. NCBI Reference Sequence Project: update and current status

Author

Donna Maglott, Kim D. Pruitt, and Tatiana Tatusova

Subjects
Genetics, Whole genome sequencing, Pseudogene, Proteins, Genomics, Articles, Computational biology, Biology, Genome, United States, Rats, Alternative Splicing, Mice, Databases, Genetic, RefSeq, Animals, Humans, RNA, Pseudogenes, Biotechnology, Reference genome
Abstract

The goal of the NCBI Reference Sequence (RefSeq) project is to provide the single best non-redundant and comprehensive collection of naturally occurring biological molecules, representing the central dogma. Nucleotide and protein sequences are explicitly linked on a residue-by-residue basis in this collection. Ideally all molecule types will be available for each well-studied organism, but the initial database collection pragmatically includes only those molecules and organisms that are most readily identified. Thus different amounts of information are available for different organisms at any given time. Furthermore, for some organisms additional intermediate records are provided when the genome sequence is not yet finished. The collection is supplied by NCBI through three distinct pipelines in addition to collaborations with community groups. The collection is curated on an ongoing basis. Additional information about the NCBI RefSeq project is available at http://www.ncbi.nih.gov/RefSeq/.

Published
2003

26. Gene: a gene-centered information resource at NCBI

Author

Kim D. Pruitt, Terence Murphy, Donna Maglott, Kenneth S. Katz, Igor Tolstoy, Michael Ovetsky, Vichet Hem, Tatiana Tatusova, Garth Brown, Olga Ermolaeva, and Craig Wallin

Subjects
Genetics, Sequence profiling tool, Internet, National Library of Medicine (U.S.), Genetic Variation, Genomics, Computational biology, Biology, Genome, United States, Entrez, Phenotype, Genes, Databases, Genetic, RefSeq, Database Issue, GeneRIF, Gene, Reference genome
Abstract

The National Center for Biotechnology Information's (NCBI) Gene database (www.ncbi.nlm.nih.gov/gene) integrates gene-specific information from multiple data sources. NCBI Reference Sequence (RefSeq) genomes for viruses, prokaryotes and eukaryotes are the primary foundation for Gene records in that they form the critical association between sequence and a tracked gene upon which additional functional and descriptive content is anchored. Additional content is integrated based on the genomic location and RefSeq transcript and protein sequence data. The content of a Gene record represents the integration of curation and automated processing from RefSeq, collaborating model organism databases, consortia such as Gene Ontology, and other databases within NCBI. Records in Gene are assigned unique, tracked integers as identifiers. The content (citations, nomenclature, genomic location, gene products and their attributes, phenotypes, sequences, interactions, variation details, maps, expression, homologs, protein domains and external databases) is available via interactive browsing through NCBI's Entrez system, via NCBI's Entrez programming utilities (E-Utilities and Entrez Direct) and for bulk transfer by FTP.

Published
2014

27. Introducing RefSeq and LocusLink: curated human genome resources at the NCBI

Author

Hugues Sicotte, Kenneth S. Katz, Donna Maglott, and Kim D. Pruitt

Subjects
Genetics, Internet, Databases, Factual, Genome, Human, GENCODE, Genomics, Computational biology, Biology, Entrez, Data sequences, RefSeq, Humans, Coding region, Human genome
Abstract

RefSeq records are included in the Entrez retrieval system (http://www.ncbi.nlm.nih.gov/entrez/). This allows a third query pathway, namely directly by Entrez nucleotide or protein text queries or indirectly by neighboring strategies. LocusLink and RefSeq data are also provided without restriction for ftp transfer (ftp://ncbi.nlm.nih.gov/refseq). Therefore, the combination of LocusLink and RefSeq resources provides a powerful approach to answering such questions as: •Is my sequence from a known gene? (Try blast and look for a RefSeq result.)•What sequence can I use as a standard for gene A? (Try blast or LocusLink.)•Where can I get more information about gene B? (Start at LocusLink.)•What genes are related to disorder Z? (Start at OMIM or LocusLink.)The goal of LocusLink and RefSeq is to include all known genes and their major products. As of the end of August 1999, ∼10 690 loci have been included, 7500 with at least some sequence data and 5985 reference mRNAs. Expanding the LocusLink and RefSeq datasets is an ongoing effort – LocusIDs are established as additional genes are identified, and RefSeq records are added as new links between genes and sequences with complete coding regions are made. The public sites are refreshed weekly. We welcome collaborations with the scientific community to ensure that these resources are as comprehensive and accurate as possible.

Published
2000

28. ClinVar: public archive of relationships among sequence variation and human phenotype

Author

George R. Riley, Donna Maglott, Wonhee Jang, Melissa J. Landrum, Wendy S. Rubinstein, Deanna M. Church, and Jennifer M. Lee

Subjects
Genetics, Internet, dbSNP, computer.internet_protocol, Genome, Human, Genetic Variation, Genomics, Computational biology, Biology, Annotation, Phenotype, VI. Genomic variation, diseases and drugs, Genetic variation, Databases, Genetic, Humans, Human genome, natural sciences, Sequence variation, Human phenotype, computer, XML
Abstract

ClinVar (http://www.ncbi.nlm.nih.gov/clinvar/) provides a freely available archive of reports of relationships among medically important variants and phenotypes. ClinVar accessions submissions reporting human variation, interpretations of the relationship of that variation to human health and the evidence supporting each interpretation. The database is tightly coupled with dbSNP and dbVar, which maintain information about the location of variation on human assemblies. ClinVar is also based on the phenotypic descriptions maintained in MedGen (http://www.ncbi.nlm.nih.gov/medgen). Each ClinVar record represents the submitter, the variation and the phenotype, i.e. the unit that is assigned an accession of the format SCV000000000.0. The submitter can update the submission at any time, in which case a new version is assigned. To facilitate evaluation of the medical importance of each variant, ClinVar aggregates submissions with the same variation/phenotype combination, adds value from other NCBI databases, assigns a distinct accession of the format RCV000000000.0 and reports if there are conflicting clinical interpretations. Data in ClinVar are available in multiple formats, including html, download as XML, VCF or tab-delimited subsets. Data from ClinVar are provided as annotation tracks on genomic RefSeqs and are used in tools such as Variation Reporter (http://www.ncbi.nlm.nih.gov/variation/tools/reporter), which reports what is known about variation based on user-supplied locations.

Published
2013

29. Phenotype–Genotype Integrator (PheGenI): synthesizing genome-wide association study (GWAS) data with existing genomic resources

Author

Erin M. Ramos, Douglas W. Hoffman, Heather Junkins, Lon Phan, Donna Maglott, Mike Feolo, Lucia A. Hindorff, and Stephen T. Sherry

Subjects
Genetics, Internet, Genotype, Genome, Human, Short Report, Computational Biology, Genomics, Single-nucleotide polymorphism, Genome-wide association study, Computational biology, Tag SNP, Biology, Genome, Polymorphism, Single Nucleotide, Phenotype, Expression quantitative trait loci, Databases, Genetic, Humans, Human genome, Genetics (clinical), Software, Genetic association, Genome-Wide Association Study
Abstract

Rapidly accumulating data from genome-wide association studies (GWASs) and other large-scale studies are most useful when synthesized with existing databases. To address this opportunity, we developed the Phenotype–Genotype Integrator (PheGenI), a user-friendly web interface that integrates various National Center for Biotechnology Information (NCBI) genomic databases with association data from the National Human Genome Research Institute GWAS Catalog and supports downloads of search results. Here, we describe the rationale for and development of this resource. Integrating over 66 000 association records with extensive single nucleotide polymorphism (SNP), gene, and expression quantitative trait loci data already available from the NCBI, PheGenI enables deeper investigation and interrogation of SNPs associated with a wide range of traits, facilitating the examination of the relationships between genetic variation and human diseases.

Published
2013

30. Getting Ready for the Human Phenome Project: The 2012 Forum of the Human Variome Project

Author

Eric Y. Zhao, Paul N. Schofield, Andreas Zankl, Carol Hamilton, Jacqueline A. L. MacArthur, Marta Girdea, John C. Carey, Sandra C. Doelken, Tudor Groza, Marc S. Greenblatt, Damian Smedley, Donna Maglott, Beverly Searle, Richard G.H. Cotton, Cynthia L. Smith, Ada Hamosh, Heidi L. Rehm, William S. Oetting, Tim Beck, Berit Kerner, Peter N. Robinson, Inge Bernstein, and Barend Mons

Subjects
Disease gene, databases, phenotype, Interoperability, database interoperability, Genetic variants, Human Variome Project, Disease, Biology, Ontology (information science), Phenome, Bioinformatics, Data science, Article, Human genetics, Genetics, Genetics (clinical), meeting report
Abstract

A forum of the Human Variome Project (HVP) was held as a satellite to the 2012 Annual Meeting of the American Society of Human Genetics in San Francisco, California. The theme of this meeting was “Getting Ready for the Human Phenome Project.” Understanding the genetic contribution to both rare single-gene “Mendelian” disorders and more complex common diseases will require integration of research efforts among many fields and better defined phenotypes. The HVP is dedicated to bringing together researchers and research populations throughout the world to provide the resources to investigate the impact of genetic variation on disease. To this end, there needs to be a greater sharing of phenotype and genotype data. For this to occur, many databases that currently exist will need to become interoperable to allow for the combining of cohorts with similar phenotypes to increase statistical power for studies attempting to identify novel disease genes or causative genetic variants. Improved systems and tools that enhance the collection of phenotype data from clinicians are urgently needed. This meeting begins the HVP’s effort toward this important goal.

Published
2013

31. Landscape scanning of cancer gene panels: A report from the NIH Genetic Testing Registry (GTR)

Author

Malheiro Aj, Brandi L. Kattman, Kenneth S. Katz, Vichet Hem, Guangfeng Song, Craig Wallin, Wendy S. Rubinstein, Baoshan Gu, Michael Ovetsky, Jennifer M. Lee, Donna Maglott, and Ricardo Villamarin-Salomon

Subjects
Oncology, Cancer Research, medicine.medical_specialty, medicine.diagnostic_test, business.industry, Bioinformatics, 03 medical and health sciences, 0302 clinical medicine, 030220 oncology & carcinogenesis, Internal medicine, Medicine, Cancer gene, business, 030217 neurology & neurosurgery, Medical literature, Genetic testing
Abstract

e13120Background: Genetic testing of tumors and germlines has proliferated in oncology, as gauged by ASCO’s meeting topics and the medical literature. A quantitative appraisal of orderable genetic ...

Published
2016

32. Databases in Human and Medical Genetics

Author

Peter D. Stenson, David Neil Cooper, Donna Maglott, Johan T. den Dunnen, Helen V. Firth, Stephen T. Sherry, Roberta A Pagon, Michael Feolo, and Ada Hamosh

Subjects
medicine.medical_specialty, Database, business.industry, Entrez Gene, Genetic counseling, information science, Locus (genetics), Computational biology, Gene mutation, computer.software_genre, OMIM : Online Mendelian Inheritance in Man, Medicine, Medical genetics, DECIPHER, Ensembl, natural sciences, business, computer, health care economics and organizations
Abstract

This chapter provides an introduction to the major, freely available, Internet-accessible databases in human and medical genetics used by healthcare providers in the diagnosis, management, and genetic counseling of persons with inherited disorders and their families, as well as by researchers for gene discovery, recording allelic variants, and cataloging genotype-phenotype relationships. Databases discussed include: GeneTests (view: www.genetests.org); Online Mendelian Inheritance in Man (view: www.ncbi.nlm.nih.gov/Omim); locus specific databases (LSDBs) identified at the Human Genome Variation Society (HGVS) web site (http://www.HGVS.org/dblist.html); DatabasE of Chromosome Imbalance and Phenotype in Humans using Ensembl Resources (view: http://decipher.sanger.ac.uk); Entrez Gene (view: ncbi.nlm.nih.gov/gene); dbGap: Database of Genotype and Phenotype (view: ncbi.nlm.nih.gov/dbgap); and the Human Gene Mutation Database HGMD ® (view: http://www.hgmd.org).

Published
2010

33. Locus Reference Genomic sequences: an improved basis for describing human DNA variants

Author

Paul Flicek, Peter E.M. Taschner, William M. McLaren, Donna Maglott, Alex Astashyn, Ewan Birney, Raymond E. Tully, Fiona Cunningham, Andrew Devereau, Anthony J. Brookes, Christophe Béroud, Pontus Larsson, Glenn Proctor, Glen Dobson, Brendan Vaughan, Raymond Dalgleish, Johan T. den Dunnen, Heikki Lehväslaiho, Yuan Chen, Department of Genetics [Leicester], University of Leicester, European Bioinformatics Institute [Hinxton] (EMBL-EBI), EMBL Heidelberg, National Center for Biotechnology Information (NCBI), Laboratoire de génétique des maladies rares. Pathologie moleculaire, etudes fonctionnelles et banque de données génétiques (LGMR), IFR3, Université Montpellier 1 (UM1)-Université Montpellier 1 (UM1)-Université de Montpellier (UM)-Institut National de la Santé et de la Recherche Médicale (INSERM), Genetic Medicine, NGRL Manchester, Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Department of Human Genetics, Leiden University Medical Center (LUMC), European Project: 200754,EC:FP7:HEALTH,FP7-HEALTH-2007-A,GEN2PHEN(2008), Université Montpellier 1 (UM1)-IFR3, Université Montpellier 1 (UM1)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), BMC, Ed., Genotype-To-Phenotype Databases: A Holistic Solution - GEN2PHEN - - EC:FP7:HEALTH2008-01-01 - 2013-06-30 - 200754 - VALID, and Universiteit Leiden-Universiteit Leiden

Subjects
Locus (genetics), Genomics, Computational biology, [SDV.GEN] Life Sciences [q-bio]/Genetics, Biology, DNA sequencing, 03 medical and health sciences, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Correspondence, Genetics, Genetics(clinical), Gene, Molecular Biology, Genetics (clinical), 030304 developmental biology, 0303 health sciences, [SDV.GEN]Life Sciences [q-bio]/Genetics, 030305 genetics & heredity, Nucleic acid sequence, Genetic code, Human genetics, 3. Good health, [SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Molecular Medicine, Human genome
Abstract

International audience; As our knowledge of the complexity of gene architecture grows, and we increase our understanding of the subtleties of gene expression, the process of accurately describing disease-causing gene variants has become increasingly problematic. In part, this is due to current reference DNA sequence formats that do not fully meet present needs. Here we present the Locus Reference Genomic (LRG) sequence format, which has been designed for the specific purpose of gene variant reporting. The format builds on the successful National Center for Biotechnology Information (NCBI) RefSeqGene project and provides a single-file record containing a uniquely stable reference DNA sequence along with all relevant transcript and protein sequences essential to the description of gene variants. In principle, LRGs can be created for any organism, not just human. In addition, we recognize the need to respect legacy numbering systems for exons and amino acids and the LRG format takes account of these. We hope that widespread adoption of LRGs - which will be created and maintained by the NCBI and the European Bioinformatics Institute (EBI) - along with consistent use of the Human Genome Variation Society (HGVS)-approved variant nomenclature will reduce errors in the reporting of variants in the literature and improve communication about variants affecting human health. Further information can be found on the LRG web site: http://www.lrg-sequence.org.

Published
2010

34. The completion of the Mammalian Gene Collection (MGC)

Author

Michelle Moksa, Ralf Wagner, David Haussler, Michael Stevens, Marco A. Marra, Chenwei Lin, Bonnie L. Maidak, Laura Langton, Janet Weber, Michael R. Brent, Sun Lu, Howard J. Jacob, Charles L.G. Comstock, Terence Murphy, Elise A. Feingold, Brona Brejova, Diana Mah, Dominic Esposito, Chaochun Wei, Carolyn M. Shenmen, Kevin Ma, Stephanie Lee, Richard M. Myers, Francis S. Collins, Rachel A. Harte, Catherine M. Farrell, Jeremy Schmutz, Keith Fichter, Richard A. Gibbs, Preethi H. Gunaratne, Cristen Robinson, Lon Phan, Rebekah S. Rasooly, Jacqueline C. Pulido, Alice C. Young, Shuwei Yang, Jane Grimwood, Peter J. Good, Robert Baertsch, Michael Mayo, Troy Moore, Elizabeth Pardes, Michael J. Brownstein, Wonhee Jang, Linda Yankie, Lila Ghamsari, Gary F. Temple, Praveen Sethupathy, Steven J.M. Jones, Martin Hirst, Wendy Wu, Athena Deng, Adam Siepel, Christa Pennacchio, Mark Dickson, Mark R. Smith, Alex Astashyn, Kourosh Salehi-Ashtiani, Jeanne Lewis, Sarah Munro, Kenneth H. Buetow, Nicole Shapiro, Carl F. Schaefer, Elizabeth Mello, Merinda Deng, Chris E. Gruber, Mark Diekhans, Anita Lebeau, Stephanie Sieja, Stefan Wiemann, Donna Maglott, Ming Ward, Jim Kent, Susan Wagner, Narayan K. Bhat, Leonie Misquitta, Tom I. Bonner, Eric D. Green, Robert A. Holt, Michael R. Murphy, Greg Taylor, Angela M. Garcia, Joseph Lazar, James Hartigan, Garth Brown, Johnson Pang, Jeffrey G. Derge, Mike Muratet, Lukas Wagner, Kane Tse, Allison Mandich, Robert W. Blakesley, Osamu Ohara, Jennifer Hart, Daniela S. Gerhard, Richard A. Moore, John Douglas Mcpherson, Jing Wang, Debonny Shoaf, Bhanu Rajput, Kim D. Pruitt, Jia Qian Wu, Stanley Letovksy, Anne Go, Lillian D. Riddick, James R. Hudson, Ryan R. Murray, Donna M. Muzny, Vanessa Wall, Gerard G. Bouffard, Thomas Zeng, Alex Rodriguez, Melissa J. Landrum, Keith Robison, Eric Chuah, Jim C. Mullikin, Kirsten Schreiber, James L. Hartley, Ralph F. Hopkins, Marcus Graf, Ryan D. Morin, Amber Marsh, David Webb, Marijke J. van Baren, Blake A. Simmons, Malachi Griffith, and David Kloske

Subjects
Resource, DNA, Complementary, Biology, chemistry.chemical_compound, Mice, Genetics, RefSeq, Animals, Humans, Genomic library, Cloning, Molecular, Gene, Genetics (clinical), Gene Library, Cloning, Mammals, Expressed sequence tag, DNA synthesis, cDNA library, Reverse Transcriptase Polymerase Chain Reaction, Computational Biology, DNA, United States, Rats, chemistry, Genes, National Institutes of Health (U.S.)
Abstract

Since its start, the Mammalian Gene Collection (MGC) has sought to provide at least one full-protein-coding sequence cDNA clone for every human and mouse gene with a RefSeq transcript, and at least 6200 rat genes. The MGC cloning effort initially relied on random expressed sequence tag screening of cDNA libraries. Here, we summarize our recent progress using directed RT-PCR cloning and DNA synthesis. The MGC now contains clones with the entire protein-coding sequence for 92% of human and 89% of mouse genes with curated RefSeq (NM-accession) transcripts, and for 97% of human and 96% of mouse genes with curated RefSeq transcripts that have one or more PubMed publications, in addition to clones for more than 6300 rat genes. These high-quality MGC clones and their sequences are accessible without restriction to researchers worldwide.

Published
2009

35. Curation at the NCBI: Genomes, Genes, & Sequence Standards

Author

Kim Pruitt, Garth Brown, Catherine Farrell, Jennifer Hart, Melissa Landrum, Donna Maglott, Bonnie Maidak, Michael Murphy, Terence Murphy, Bhanu Rajput, Lillian Riddick, David Webb, Janet Weber, and Wendy Wu

Subjects
General Materials Science
Published
2009

36. Lineage-specific biology revealed by a finished genome assembly of the mouse

Author

Kerstin Lindblad-Toh, Dan Forrest, David C. Schwartz, Ana C. Marques, Chris P. Ponting, Donna Maglott, Shiguo Zhou, Carol J. Bult, James M. Amos-Landgraf, Christopher Churas, Tina Graves, Deanna M. Church, Ron Runnheim, Yuri Kapustin, Joshua L. Cherry, Zoë Birtle, LaDeana W. Hillier, Michael C. Zody, Michael Place, Ze Cheng, Wratko Hlavina, Steve Goldstein, Richa Agarwala, Konstantinos Potamousis, Jill Herschleb, Evan E. Eichler, Xinwe She, Leo Goodstadt, Brian Teague, Peter Meric, and Michael DiCuccio

Subjects
QH301-705.5, Population, Sequence assembly, Computational biology, Biology, Genome, General Biochemistry, Genetics and Molecular Biology, Mice, 03 medical and health sciences, 0302 clinical medicine, Gene Duplication, Databases, Genetic, Animals, Humans, Gene family, Genetics and Genomics/Genomics, Biology (General), education, Gene, Genetics and Genomics/Plant Genomes and Evolution, 030304 developmental biology, Whole genome sequencing, Comparative genomics, Genetics, 0303 health sciences, education.field_of_study, General Immunology and Microbiology, General Neuroscience, Computational Biology, Genetics and Genomics/Bioinformatics, Genetics and Genomics/Genome Projects, Human genome, General Agricultural and Biological Sciences, 030217 neurology & neurosurgery, Research Article
Abstract

A finished clone-based assembly of the mouse genome reveals extensive recent sequence duplication during recent evolution and rodent-specific expansion of certain gene families. Newly assembled duplications contain protein-coding genes that are mostly involved in reproductive function., The mouse (Mus musculus) is the premier animal model for understanding human disease and development. Here we show that a comprehensive understanding of mouse biology is only possible with the availability of a finished, high-quality genome assembly. The finished clone-based assembly of the mouse strain C57BL/6J reported here has over 175,000 fewer gaps and over 139 Mb more of novel sequence, compared with the earlier MGSCv3 draft genome assembly. In a comprehensive analysis of this revised genome sequence, we are now able to define 20,210 protein-coding genes, over a thousand more than predicted in the human genome (19,042 genes). In addition, we identified 439 long, non–protein-coding RNAs with evidence for transcribed orthologs in human. We analyzed the complex and repetitive landscape of 267 Mb of sequence that was missing or misassembled in the previously published assembly, and we provide insights into the reasons for its resistance to sequencing and assembly by whole-genome shotgun approaches. Duplicated regions within newly assembled sequence tend to be of more recent ancestry than duplicates in the published draft, correcting our initial understanding of recent evolution on the mouse lineage. These duplicates appear to be largely composed of sequence regions containing transposable elements and duplicated protein-coding genes; of these, some may be fixed in the mouse population, but at least 40% of segmentally duplicated sequences are copy number variable even among laboratory mouse strains. Mouse lineage-specific regions contain 3,767 genes drawn mainly from rapidly-changing gene families associated with reproductive functions. The finished mouse genome assembly, therefore, greatly improves our understanding of rodent-specific biology and allows the delineation of ancestral biological functions that are shared with human from derived functions that are not., Author Summary The availability of an accurate genome sequence provides the bedrock upon which modern biomedical research is based. Here we describe a high-quality assembly, Build 36, of the mouse genome. This assembly was put together by aligning overlapping individual clones representing parts of the genome, and it provides a more complete picture than previous assemblies, because it adds much rodent-specific sequence that was previously unavailable. The addition of these sequences provides insight into both the genomic architecture and the gene complement of the mouse. In particular, it highlights recent gene duplications and the expansion of certain gene families during rodent evolution. An improved understanding of the mouse genome and thus mouse biology will enhance the utility of the mouse as a model for human disease.

Published
2009

37. Planning the human variome project: the Spain report

Author

Peter J. Oefner, M. V. Golubenko, Jim Kaput, Judith Savige, Helmut Blöcer, Maurizio Genuardi, Toshio Kojima, Ming Qi, Paul Flicek, Vera Lúcia Gil-da-Silva-Lopes, Rodney J. Scott, Sandrine Laradi, Jon W. Teague, Ada Hamosh, María Jesús Sobrido, Mollie Ullman-Cullere, Joji Utsunomiya, Robert Hoffmann, Simon B. Flanagan, Lawrence Cavedon, Steven F. Dobrowolski, Ludwine Messiaen, Elizabeth A. Shephard, Giuditta Perozzi, Mireille Claustres, Sue Povey, Elizabeth Webb, Rolf H. Sijmons, Anne Cambon-Thomsen, Hyang Sook Yoo, Donna Maglott, Gabriela Möslein, Jongsun Jung, Toshihiro Tanaka, Jong Bhak, Yoichi Furukawa, Stacey L. Bleoo, Aida I. Al Aqeel, Mark H. Paalman, Timothy D. Smith, Makia J. Marafie, Jillian S. Parboosingh, Garry R. Cutting, M. Rosário N.Dos Santos, Ho Ghang, Daniela Seminara, Ana María Oller de Ramirez, Mariona Bustamante, Yeun Jun Chung, Carlos Díaz, Fahd Al-Mulla, Terence M. Harrison, John M. Hancock, Michael S. Watson, Rajkumar Ramesar, D. Scheible, Heather J. Howard, Mihai G. Netea, Marc S. Greenblatt, Ian Phillips, John Burn, Santos Alonso, Henk J van Kranen, George P. Patrinos, Carol Isaacson Barash, Suyash Prasad, Neskuts Izagirre, Ross C. Hardison, Inge Bernstein, Thomas K. Weber, Sean V. Tavtigian, Melissa L. Norton, Seon Hee Yim, Mauno Vihinen, Finlay A. Macrae, Rita Calzone, Richard G.H. Cotton, Meredith Yeager, C. Sue Richards, Yoichi Matsubara, Yoon Shin Cho, Arleen D. Auerbach, Johan T. den Dunnen, Young Ii Yeom, Jumana Y. Al-Aama, Raymond Dalgleish, Graham R. Taylor, Yeon Su Lee, Steven G.E. Marsh, Steven E. Brenner, James O'Leary, Rania Horaitis, Stefan Aretz, Bharati Bapat, David J. Quin, Lauren Hardman, Rosemary Ekong, William S. Oetting, Paola Carrera, Michele Cargill, Jong-Young Lee, Thoralf Töpel, and Division of Personalised Nutrition and Medicine, FDA/National Center for Toxicological Research, Jefferson, Arkansas 72079, USA. James.kaput@fda.hhs.gov

Subjects
Human genetic variation, Settore MED/03 - GENETICA MEDICA, GLOBAL HEALTH, Genome, MUTATION DATABASE, 0302 clinical medicine, Databases, Genetic, HUMAN GENOME, GRAND CHALLENGES, DEVELOPING-COUNTRIES, International HapMap Project, database, Genetics (clinical), Genetics, 0303 health sciences, GENETIC-VARIATION, HUMAN-DISEASE, 3. Good health, Pathogenesis and modulation of inflammation [N4i 1], Phenotype, LOCUS-SPECIFIC DATABASES, 030220 oncology & carcinogenesis, Infection and autoimmunity [NCMLS 1], Genotype, Human Variome Project, Biology, Article, 03 medical and health sciences, variome, genetic disease, Humans, Genetic Predisposition to Disease, Genetic variability, INTERNATIONAL HAPMAP PROJECT, MISSENSE VARIANTS, genome, 030304 developmental biology, Polymorphism, Genetic, Genome, Human, Information Dissemination, Computational Biology, Genetic Variation, Data science, Human genetics, Variome, Spain, Mutation, Human genome, mutation
Abstract

Contains fulltext : 81952.pdf (Publisher’s version ) (Closed access) The remarkable progress in characterizing the human genome sequence, exemplified by the Human Genome Project and the HapMap Consortium, has led to the perception that knowledge and the tools (e.g., microarrays) are sufficient for many if not most biomedical research efforts. A large amount of data from diverse studies proves this perception inaccurate at best, and at worst, an impediment for further efforts to characterize the variation in the human genome. Because variation in genotype and environment are the fundamental basis to understand phenotypic variability and heritability at the population level, identifying the range of human genetic variation is crucial to the development of personalized nutrition and medicine. The Human Variome Project (HVP; http://www.humanvariomeproject.org/) was proposed initially to systematically collect mutations that cause human disease and create a cyber infrastructure to link locus specific databases (LSDB). We report here the discussions and recommendations from the 2008 HVP planning meeting held in San Feliu de Guixols, Spain, in May 2008.

Published
2009

38. The genome sequence of taurine cattle: A window to ruminant biology and evolution

Author

Heather M Deobald, Gerald R. Fowler, Clay Davis, Judith Herdandez, Donna Maglott, Lin Chen, Gonzalo Rincon, Darren E. Hagen, James T. Warren, Evgenia V. Kriventseva, Ingrid Olsaker, Debora L. Hamernik, Charles Moen, Oliver C. Jann, Yuri Kapustin, Erdogan Memili, Timothy Connelley, Ling Ling Pu, Terhi Iso-Touru, Gemma Marie Payne, Ye Cheng, Amy Egan, Alexandre Reymond, Aniko Sabo, J. Bruce German, Jason R. Grant, Joseph Chacko, Ronnie D. Green, Isabel Kinney Ferreira de Miranda Santos, Raffaele Mazza, A.J. Molenaar, Richard A. Moore, Christian J. Buhay, Henry Song, Cham G. Kumar, Marion L. Greaser, Hasan Khatib, Harris A. Lewin, Olga Ermolaeva, Jonathan V. Sweedler, Steven J.M. Jones, Rosemeire Conceição Parra Pastor, Paul Stothard, Adam J. Colley, Antti Livanainen, Francesca Panzitta, Dan Graur, Aaron Ingham, David L. Adelson, Timothy P. L. Smith, Shirley A. Ellis, Andy Cree, Jingkun Zhang, Carolyn T.A. Herzig, Jason Goodell, Colette A. Abbey, Feng-Qi Zhao, Mimi M. Chandrabose, Ross L. Tellam, Alex Astashyn, Yanru Ren, Laura Elnitski, Bella Mayurkumar Patel, Sem Genini, Lassudara G. Almeida, Jacqueline E. Schein, Theresa Casey, Hanni Salih, José Fernando Garcia, Zhiquan Wang, Carolyn Fitzsimmons, Evan E. Eichler, Ngoc Nguyen, Kaitlin E. Donohue, Ariel Fernando Amadio, Clayton R. Boldt, John C. McEwan, Juan Manuel Anzola, Francisco Câmara, Shoba Ranganathan, Eran Elhaik, Stefan Hiendleder, George M. Weinstock, Lora Lewis, Jeremy F. Taylor, Dimos Kapetis, Andrew J. Roberts, Lee Alexander, Nelida Rodriguez-Osorio, Alexandre Souvorov, Justin C. Lee, Bruce R. Southey, Boris Kiryutin, Michael Holder, Xiang Qin, Warren M. Snelling, Abhirami Ratnakumar, Marcelo Fábio Gouveia Nogueira, Angela K. Walker, Hatam A. Hakimov, Fernando H. Biase, Roderic Guigó, Shannon Dugan-Rocha, Sean McWilliam, Rex Lee Williams, Jacqueline Chrast, Huyen Dinh, Robert C. Edgar, Huaiyang Jiang, Justin T. Reese, John W. Keele, George E. Liu, Yufeng Shen, Jireh Santibanez, Kim C. Worley, Sandra L. Lee, Sari S. Khalil, Marta Hernández, Stephen N. White, Suria M. Bahadue, Changxi Li, Kim D. Pruitt, Kirsty Jensen, C. Michael Dickens, Jung-Woo Choi, Jennifer Harrow, Tatiana A. DeCampos, Richard A. Gibbs, Ryan J. Lozado, Yoshikazu Sugimoto, Sigbjam Lien, Anna K. Bennett, Curtis P. Van Tassell, Eve Devinoy, Gustavo Garcia, R. Baxter, Satyanarayana Rachagani, Kevin K. Lahmers, Stylianos E. Antonarakis, D. Kolbehdari, Cynthia L. Baldwin, Lillian Sando, Darryl L. Hadsell, Elen Anatriello, Ze Cheng, Richard C. Waterman, Paul Havlak, Peter Dove, Laura Sherman, Wes Barris, Imke Tammen, Geoffrey Okwuonu, Jennifer Hume, Denis M. Larkin, Robert D. Schnabel, Zhi-Liang Hu, Evgeny M. Zdobnov, Danielle G. Lemay, Stephanie Bell, Roberto Malinverni, Jiuzhou Song, David Steffen, James M. Reecy, Lynne V. Nazareth, Carlo José Freire de Oliveira, E. Marques, Cody J. Gladney, Donna M. Muzny, Candice L. Brinkmeyer-Larigford, Lakshmi K. Matukumalli, Jan Aerts, Stephen S. Moore, Margaret Morgan, Kim L. McLean, Juan F. Medrano, Felix Kokocinski, Marco A. Marra, Gregory P. Harhay, Frank W. Nicholas, Loren C. Skow, Fiona S. L. Brinkman, Tovah Kerr, Krista L. Fritz, Stacey M. Curry, Charlotte N. Henrichsen, Catherine Ucla, David J. Lynn, Victor V. Solovyev, Natasha E. Romero, Sandra Hines, Joy M. Raison, Alessandra Mara Franzin, Selina Vattathil, Jeffery A. Carroll, Brian P. Dalrymple, Katarzyna Wilczek-Boney, Seongwon Seo, Richard J. Leach, Mireya Plass, Paul Kitts, Kris R. Wunderlich, Bhanu Prakash V.L. Telugu, Gary L. Bennett, Ramatu Ayiesha Gabisi, Ravikiran Donthu, Shalini N. Jhangiani, Rita A. Wright, Mary Qu Yang, Nauman J. Maqbool, W. A. Carvalho, Monique Rijnkels, Yuri Tani Utsunomiya, Charles E. Chappie, John L. Williams, Rob Halgren, Stephen M. J. Searle, A.R.R. Abatepaulo, Thomas Junier, Stephanie D. McKay, Anne G. Rosenwald, David A. Wheeler, Rosemarie Weikard, N. Hastings, Roger T. Stone, Eduardo Eyras, Cerissa Hamilton, Wendy C. Brown, Yan Ding, Ylva Strandberg Lutzow, Matthew Hobbs, Annett Eberlein, Carine Wyss, Jennifer M. Urbanski, Matthew Peter Kent, Lilian P.L. Lau, Dinesh Kumar, Penny K. Riggs, Lawrence B. Schook, Matthew Hitchens, Vandita Joshi, Melissa J. Landrum, Tyler Alioto, Nathan Poslusny, Thomas T. Wheeler, Victor Sapojnikov, Natália F. Martins, San Juana Ruiz, Michael D. MacNeil, Alexandre Rodrigues Caetano, Mario Andres Poli, Catherine Jamis, Masaaki Taniguchi, James E. Womack, William F. Martin, Andrej Razpet, James G. R. Gilbert, Daniel G. Bradley, Readman Chiu, Thomas H. Welsh, Clare A. Gill, Erica Sodergren, Carol G. Chitko-McKown, Hari Prasad Nandakumar, Virpi Ahola, Steve M. Kappes, Jennifer E. Chapin, Sandra Regina Maruyama, John Lopez, Krystin M. Logan, Jonathan A. Green, Laurens G. Wilming, Yue Liu, Antti Iivanainen, Robert A. Holt, Barbara T. Moreno, Marcos De Donato, Christie Kovar, Angela Jolivet Johnson, Carl T. Muntean, Robert Ward, K. James Durbin, Matthew D. Whiteside, Christopher P. Childers, Tad S. Sonstegard, Yin Shin Liu, Bin Zhu, Sameer D. Pant, Ashley J. Waardenberg, André Eggen, D.M. Spurlock, Hsiu Chuan Chen, Le Luo Guan, Sandra L. Rodriguez-Zas, Akiko Takasuga, Daniela D. Moré, Jianqi Yang, Wratko Hlavina, Sheila M. Schmutz, Michael J. Brownstein, Christine G. Elsik, Marvin Diep Dao, Daniel Gerlach, E. Hart, Elsa Chacko, Elizabeth Glass, Libing Shen, Chris P. Verschoor, Eliane P. Cervelatti, Department of Biology, Georgetown University, Department of Animal Science, Texas A&M University [College Station], Livestock Industries, Baylor College of Medicine (BCM), Reymond, Alexandre, Zdobnov, Evgeny, Antonarakis, Stylianos, Ucla, Catherine, Gerlach, Daniel, and Junier, Thomas

Subjects
Male, genome sequence, [SDV]Life Sciences [q-bio], ved/biology.organism_classification_rank.species, Genome, Genética y Herencia, Segmental duplication, 2. Zero hunger, Genetics, ddc:616, 0303 health sciences, Multidisciplinary, 04 agricultural and veterinary sciences, Bovine genome, Animals, Domestic, Proteins/genetics, Female, CIENCIAS NATURALES Y EXACTAS, Sequence analysis, Evolution, Biotecnología Agropecuaria, Molecular Sequence Data, Tecnología GM, clonación de ganado, selección asistida, diagnósticos, tecnología de producción de biomasa, etc, Biology, Synteny, Article, Ciencias Biológicas, Evolution, Molecular, 03 medical and health sciences, Species Specificity, Animals, Humans, General, Gene, 030304 developmental biology, Whole genome sequencing, ved/biology, Taurine cattle, 0402 animal and dairy science, Genetic Variation, Sequence Analysis, DNA, 040201 dairy & animal science, Bos taurus, Alternative Splicing, MicroRNAs/genetics, CIENCIAS AGRÍCOLAS, cattle, Cattle, genetic
Abstract

To understand the biology and evolution of ruminants, the cattle genome was sequenced to about sevenfold coverage. The cattle genome contains a minimum of 22,000 genes, with a core set of 14,345 orthologs shared among seven mammalian species of which 1217 are absent or undetected in noneutherian (marsupial or monotreme) genomes. Cattle-specific evolutionary breakpoint regions in chromosomes have a higher density of segmental duplications, enrichment of repetitive elements, and species-specific variations in genes associated with lactation and immune responsiveness. Genes involved in metabolism are generally highly conserved, although five metabolic genes are deleted or extensively diverged from their human orthologs. The cattle genome sequence thus provides a resource for understanding mammalian evolution and accelerating livestock genetic improvement for milk and meat production. Fil: Bovine Genome Sequencing and Analysis Consortium. Bovine Genome Sequencing And Analysis Consortium; Estados Unidos Fil: Amadio, Ariel Fernando. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Santa Fe. Estación Experimental Agropecuaria Rafaela; Argentina Fil: Poli, Mario Andres. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Genética; Argentina

Published
2009

39. The Consensus Coding Sequence (Ccds) Project: Identifying a Common Protein-Coding Gene Set for the Human and Mouse Genomes

Author

Oksana Dukhanina, Barbara J. Ruef, Janet Weber, Craig Wallin, Michael DiCuccio, Robert Baertsch, Manolis Kellis, Bronwen Aken, Joshua L. Cherry, Clara Amid, Marie-Marthe Suner, Andrew Shkeda, Julio Fernandez-Banet, Sarah Ayling, Donna Maglott, Elizabeth M. Hart, Steve Searle, Michael R. Murphy, Ewan Birney, James Ostell, Laurens G. Wilming, Bhanu Rajput, Jennifer Harrow, Jeena Rajan, Mark Diekhans, Catherine M. Farrell, Richard Durbin, David J. Lipman, Adam Frankish, Jennifer Hart, Bonnie L. Maidak, Terence Murphy, Jonathan M. Mudge, Kim D. Pruitt, Michael Schuster, Melissa J. Landrum, Wenyu Wu, Catherine E. Snow, Garth Brown, Tim Hubbard, Rachel A. Harte, Jennifer M. Lee, Jane E. Loveland, Michael F. Lin, Charles A. Steward, David Haussler, David Webb, Lillian D. Riddick, Val Curwen, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Kellis, Manolis, and Lin, Michael F.

Subjects
Resource, Genetics, Genome, Sequence alignment, Computational biology, Biology, Mice, Open Reading Frames, Annotation, Protein Annotation, Consensus Sequence, Consensus sequence, Animals, Humans, Coding region, Ensembl, Human genome, Sequence Alignment, Genetics (clinical)
Abstract

Effective use of the human and mouse genomes requires reliable identification of genes and their products. Although multiple public resources provide annotation, different methods are used that can result in similar but not identical representation of genes, transcripts, and proteins. The collaborative consensus coding sequence (CCDS) project tracks identical protein annotations on the reference mouse and human genomes with a stable identifier (CCDS ID), and ensures that they are consistently represented on the NCBI, Ensembl, and UCSC Genome Browsers. Importantly, the project coordinates on manually reviewing inconsistent protein annotations between sites, as well as annotations for which new evidence suggests a revision is needed, to progressively converge on a complete protein-coding set for the human and mouse reference genomes, while maintaining a high standard of reliability and biological accuracy. To date, the project has identified 20,159 human and 17,707 mouse consensus coding regions from 17,052 human and 16,893 mouse genes. Three evaluation methods indicate that the entries in the CCDS set are highly likely to represent real proteins, more so than annotations from contributing groups not included in CCDS. The CCDS database thus centralizes the function of identifying well-supported, identically-annotated, protein-coding regions., National Human Genome Research Institute (U.S.) (Grant number 1U54HG004555-01), Wellcome Trust (London, England) (Grant number WT062023), Wellcome Trust (London, England) (Grant number WT077198)

Published
2008

40. GENETICS. The Human Variome Project

Author

Richard G H, Cotton, Arleen D, Auerbach, Myles, Axton, Carol Isaacson, Barash, Samuel F, Berkovic, Anthony J, Brookes, John, Burn, Garry, Cutting, Johan T, den Dunnen, Paul, Flicek, Nelson, Freimer, Marc S, Greenblatt, Heather J, Howard, Michael, Katz, Finlay A, Macrae, Donna, Maglott, Gabriela, Möslein, Sue, Povey, Rajkumar S, Ramesar, Carolyn S, Richards, Daniela, Seminara, Timothy D, Smith, María-Jesús, Sobrido, Jan Helge, Solbakk, Rudolph E, Tanzi, Sean V, Tavtigian, Graham R, Taylor, Joji, Utsunomiya, and Michael, Watson

Subjects
Databases, Factual, Genome, Human, Data_GENERAL, International Cooperation, Databases, Genetic, Human Genome Project, Mutation, Genetic Variation, Humans, Interdisciplinary Communication, ComputingMethodologies_GENERAL, Nervous System Diseases, Article
Abstract

An ambitious plan to collect, curate, and make accessible information on genetic variations affecting human health is beginning to be realized.

Published
2008

41. The NIH Genetic Testing Registry and content tailored for oncologists

Author

Jeffrey Hoover, Donna Maglott, Shanmuga Chitipiralla, Mark L. Benson, Brandi L. Kattman, Malheiro Aj, Wendy S. Rubinstein, Kenneth S. Katz, Jennifer M. Lee, and Radhika Tekumalla

Subjects
Set (abstract data type), Cancer Research, Knowledge management, Resource (project management), Oncology, medicine.diagnostic_test, business.industry, medicine, business, health care economics and organizations, Word (computer architecture), Genetic testing
Abstract

e12543 Background: Oncology professionals have no centralized resource to locate the specific set of genetic tests relevant to their practice. Instead they rely on education, marketing, and word of...

Published
2015

42. The Genome of the Sea Urchin Strongylocentrotus purpuratus

Author

Amro Hamdoun, Virginia Brockton, Huyen Dinh, Qiang Tu, Richard O. Hynes, Maria Ina Arnone, Wratko Hlavina, L. Courtney Smith, Mariano A. Loza, David R. Burgess, Matthew P. Hoffman, Florian Raible, Qiu Autumn Yuan, Geoffrey Okwuonu, Mark Y. Tong, Jennifer Hume, Donna Maglott, Manisha Goel, Olivier Fedrigo, Manuel L. Gonzalez-Garay, Celina E. Juliano, Judith Hernandez, Gary M. Wessel, William F. Marzluff, Audrey J. Majeske, Christian Gache, Louise Duloquin, Xingzhi Song, François Lapraz, Fowler J, Alexandre Souvorov, Jared V. Goldstone, Georgia Panopoulou, Sandra Hines, Kyle M. Judkins, Clay Davis, Christine G. Elsik, Paul Kitts, Mariano Loza-Coll, Greg Wray, Taku Hibino, Eric Röttinger, Allison M. Churcher, Annamaria Locascio, Arcady Mushegian, Masashi Kinukawa, Anna Reade, Katherine M. Buckley, I. R. Gibbons, Bert Gold, Aleksandar Milosavljevic, David Epel, Victor D. Vacquier, Ling Ling Pu, Vincenzo Cavalieri, Erin L. Allgood, Lan Zhang, Lynne V. Nazareth, Constantin N. Flytzanis, Ian Bosdet, Yi-Hsien Su, Zeev Pancer, Matthew L. Rowe, Robert C. Angerer, David R. McClay, William H. Klein, Rachel F. Gray, Julian L. Wong, Shunsuke Yaguchi, Robert Bellé, Aaron J. Mackey, Herath Jayantha Gunaratne, Karl Frederik Bergeron, Bruce P. Brandhorst, Greg Murray, Avis H. Cohen, Stephanie Bell, Kristin Tessmar-Raible, Ian K. Townley, Bertrand Cosson, Thomas D. Glenn, Jongmin Nam, Cynthia A. Bradham, Michael Dean, Joseph Chacko, Anthony J. Robertson, Margherita Branno, Valeria Matranga, K. James Durbin, Esther Miranda, Lili Chen, Eran Elhaik, Robert D. Burke, Rita A. Wright, Paola Oliveri, Sandra L. Lee, Gary W. Moy, Alexander E Primus, Shawn S. McCafferty, Cristina Calestani, David A. Garfield, Erica Sodergren, Karen Wilson, Joel Smith, Marco A. Marra, Cynthia Messier, Julia Morales, Kim D. Pruitt, Rachel Thorn, Rachel Gill, John S. Taylor, Mark E. Hahn, Victor Sapojnikov, Meredith Howard-Ashby, Lynne M. Angerer, Maurice R. Elphick, Kathy R. Foltz, Anne Marie Genevière, Justin T. Reese, Blanca E. Galindo, Kim C. Worley, Andrew Leone, Glen Humphrey, Kevin Berney, Olga Ermolaeva, George Miner, David P. Terwilliger, Elly Suk Hen Chow, Lora Lewis, Dan Graur, C. Titus Brown, Gerard Manning, Kevin J. Peterson, Angela Jolivet, Michele K. Anderson, Francesca Rizzo, Ekaterina Voronina, Thierry Lepage, Giorgio Matassi, Antonio Fernandez-Guerra, Mamoru Nomura, Charles A. Whittaker, James R.R. Whittle, James A. Coffman, George M. Weinstock, Mohammed M. Idris, Ashlan M. Musante, Sebastian D. Fugmann, Katherine D. Walton, Sorin Istrail, Shu-Yu Wu, Cerrissa Hamilton, Jonah Cool, Jacqueline E. Schein, Stacey M. Curry, Athula Wikramanayke, Seth Carbonneau, Blair J. Rossetti, Christopher E. Killian, Melissa J. Landrum, Amanda P. Rawson, Jenifer C. Croce, Ryan C. Range, Rahul Satija, John J. Stegeman, Yufeng Shen, Cavit Agca, Terry Gaasterland, Rocky Cheung, Takae Kiyama, Nikki Adams, Jonathan P. Rast, Robert Piotr Olinski, Andrew Cree, Mark Scally, Shuguang Liang, David A. Parker, Rebecca Thomason, Gretchen E. Hofmann, Michelle M. Roux, Ronghui Xu, Robert A. Obar, Enrique Arboleda, Odile Mulner-Lorillon, Shannon Dugan-Rocha, David J. Bottjer, Gabriele Amore, Manoj P. Samanta, Waraporn Tongprasit, Véronique Duboc, La Ronda Jackson, Fred H. Wilt, Viktor Stolc, Anna T. Neill, Michael Raisch, Pei Yun Lee, Jia L. Song, Margaret Morgan, Brian T. Livingston, Sofia Hussain, Zheng Wei, Bryan J. Cole, Tonya F. Severson, Victor V. Solovyev, Finn Hallböök, Donna M. Muzny, Christine A. Byrum, Albert J. Poustka, Xiuqian Mu, Andrew R. Jackson, Shin Heesun, Euan R. Brown, Nansheng Chen, Patrick Cormier, Ralph Haygood, Pedro Martinez, R. Andrew Cameron, D. Wang, Wendy S. Beane, Eric H. Davidson, Christie Kovar, Hemant Kelkar, Charles A. Ettensohn, Sham V. Nair, Robert L. Morris, Stefan C. Materna, Michael C. Thorndyke, Richard A. Gibbs, Dan O Mellott, Department of Physiology and Biophysics, Stony Brook University [The State University of New York] ( SBU ), Astronomy Unit ( AU ), Queen Mary University of London ( QMUL ), Urban and Industrial Air Quality Group, CSIRO Energy Technology, Commonwealth Scientific and Industrial Research Organisation Energy Technology ( CSIRO Energy Technology ), Commonwealth Scientific and Industrial Research Organisation, Center for Polymer Studies ( CPS ), Boston University [Boston] ( BU ), Physics Department [Boston] ( BU-Physics ), Max Planck Institute for Psycholinguistics, Max-Planck-Institut, Department of Biology [Norton], Wheaton College [Norton], Mathematical Institute [Oxford] ( MI ), University of Oxford [Oxford], Centre for the Analysis of Time Series ( CATS ), London School of Economics and Political Science ( LSE ), Thomas Jefferson National Accelerator Facility ( Jefferson Lab ), Thomas Jefferson National Accelerator Facility, Laboratoire d'Energétique et de Mécanique Théorique Appliquée ( LEMTA ), Université de Lorraine ( UL ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire Evolution, Génomes et Spéciation ( LEGS ), Centre National de la Recherche Scientifique ( CNRS ), Department of Geology, University of Illinois at Urbana-Champaign [Urbana], Department of Electrical and Computer Engineering [Portland] ( ECE ), Portland State University [Portland] ( PSU ), Saint-Gobain Crystals [USA], SAINT-GOBAIN, Institute for Animal Health ( IAH ), Biotechnology and Biological Sciences Research Council, Center for Agricultural Resources Research, Chinese Academy of Sciences [Changchun Branch] ( CAS ), Ipsen Inc. [Milford] ( Ipsen ), IPSEN, Department of Physics [Berkeley], University of California [Berkeley], Institute for Climate and Atmospheric Science [Leeds] ( ICAS ), University of Leeds, Chung-Ang University ( CAU ), Chung-Ang University [Seoul], Antarctic Climate and Ecosystems Cooperative Research Center ( ACE-CRC ), Institute of Aerodynamics and Fluid Mechanics ( AER ), Technische Universität München [München] ( TUM ), Mer et santé ( MS ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Centre National de la Recherche Scientifique ( CNRS ), Imperial College London, Radio and Atmospheric Sciences Division, National Physical Laboratory [Teddington] ( NPL ), International Research Institute for Climate and Society ( IRI ), Earth Institute at Columbia University, Columbia University [New York]-Columbia University [New York], Soils Group, The Macaulay Institute, Department of Haematology, University of Cambridge [UK] ( CAM ), School of Biology and Biochemistry, Queen's University, Leslie Hill Institute for Plant Conservation ( PCU ), University of Cape Town, Institute for Microelectronics and Microsystems/ Istituto per la Microelettronica e Microsistemi ( IMM ), Consiglio Nazionale delle Ricerche ( CNR ), Laboratoire d'acoustique de l'université du Mans ( LAUM ), Le Mans Université ( UM ) -Centre National de la Recherche Scientifique ( CNRS ), Interactive Systems Labs ( ISL ), Carnegie Mellon University [Pittsburgh] ( CMU ), Dalian Institute of Chemical Physics ( DICP ), Architectures, Languages and Compilers to Harness the End of Moore Years ( ALCHEMY ), Laboratoire de Recherche en Informatique ( LRI ), Université Paris-Sud - Paris 11 ( UP11 ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -Université Paris-Sud - Paris 11 ( UP11 ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique ( Inria ), Clean Air Task Force ( CATF ), Clean Air Task Force, Space Physics Laboratory, Indian Space Research Organisation ( ISRO ), Centre d'études et de recherches appliquées à la gestion ( CERAG ), Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Centre National de la Recherche Scientifique ( CNRS ), Department of Microbiology and Immunology, College of Medicine and Health Sciences-Sultan Qaboos University, European Molecular Biology Laboratory [Heidelberg] ( EMBL ), Department of Biostatistics, University of Michigan [Ann Arbor], Department of Radiation Oncology [Michigan] ( Radonc ), Department of Physics and Astronomy [Leicester], University of Leicester, Informatique, Biologie Intégrative et Systèmes Complexes ( IBISC ), Université d'Évry-Val-d'Essonne ( UEVE ) -Centre National de la Recherche Scientifique ( CNRS ), Institut für Meteorologie und Klimaforschung ( IMK ), Karlsruher Institut für Technologie ( KIT ), Physics Department [UNB], University of New Brunswick ( UNB ), Laboratoire Parole et Langage ( LPL ), Centre National de la Recherche Scientifique ( CNRS ) -Aix Marseille Université ( AMU ), Institut des Sciences Chimiques de Rennes ( ISCR ), Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Ecole Nationale Supérieure de Chimie de Rennes-Institut National des Sciences Appliquées ( INSA ) -Centre National de la Recherche Scientifique ( CNRS ), Biogéosciences [Dijon] ( BGS ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS ), Bioprojet, Laboratoire de Matériaux à Porosité Contrôlée ( LMPC ), Université de Haute-Alsace (UHA) Mulhouse - Colmar ( Université de Haute-Alsace (UHA) ) -Ecole Nationale Supérieure de Chimie de Mulhouse-Centre National de la Recherche Scientifique ( CNRS ), School of Information Engineering [USTB] ( SIE ), University of Science and Technology Beijing [Beijing] ( USTB ), Laboratory for Atmospheric and Space Physics [Boulder] ( LASP ), University of Colorado Boulder [Boulder], Department of Applied Mathematics [Sheffield], University of Sheffield [Sheffield], School of Mathematics and Statistics [Sheffield] ( SoMaS ), Laboratoire de Mécanique de Lille - FRE 3723 ( LML ), Université de Lille, Sciences et Technologies-Ecole Centrale de Lille-Centre National de la Recherche Scientifique ( CNRS ), Computer Science Department [UCLA] ( CSD ), University of California at Los Angeles [Los Angeles] ( UCLA ), Développement et évolution ( DE ), Université Paris-Sud - Paris 11 ( UP11 ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire de Biologie du Développement de Villefranche sur mer ( LBDV ), Laboratoire Pierre Aigrain ( LPA ), Fédération de recherche du Département de physique de l'Ecole Normale Supérieure - ENS Paris ( FRDPENS ), Centre National de la Recherche Scientifique ( CNRS ) -École normale supérieure - Paris ( ENS Paris ) -Centre National de la Recherche Scientifique ( CNRS ) -École normale supérieure - Paris ( ENS Paris ) -Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Université Paris Diderot - Paris 7 ( UPD7 ) -Centre National de la Recherche Scientifique ( CNRS ), Department of Mathematics and Statistics [Mac Gill], McGill University, Departamento de Botánica [Comahue], Universidad nacional del Comahue, Bioénergétique Cellulaire et Pathologique ( BECP ), Université Joseph Fourier - Grenoble 1 ( UJF ) -Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ), Environnements et Paléoenvironnements OCéaniques ( EPOC ), Observatoire aquitain des sciences de l'univers ( OASU ), Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ) -École pratique des hautes études ( EPHE ) -Centre National de la Recherche Scientifique ( CNRS ), Institut Jacques Monod ( IJM ), Université Paris Diderot - Paris 7 ( UPD7 ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratori Nazionali del Sud ( LNS ), National Institute for Nuclear Physics ( INFN ), Departament de Matemàtiques [Barcelona], Universitat Autònoma de Barcelona [Barcelona] ( UAB ), Max-Planck-Institut für Kohlenforschung (coal research), Institute of Oceanology [CAS] ( IOCAS ), National Chiao Tung University ( NCTU ), Department of Hydrology and Water Resources ( HWR ), University of Arizona, Centre for Educational Technology, Environment Department [York], University of York [York, UK], State Key Laboratory of Nuclear Physics and Technology ( SKL-NPT ), Peking University [Beijing], Department of Physics and Astronomy [Iowa City], University of Iowa [Iowa], NASA Ames Research Center ( ARC ), Department of Materials, Digital Language & Knowledge Contents Research Association ( DICORA ), Hankuk University of Foreign Studies, Department of Physics [Coventry], University of Warwick [Coventry], Space Science and Technology Department [Didcot] ( RAL Space ), STFC Rutherford Appleton Laboratory ( RAL ), Science and Technology Facilities Council ( STFC ) -Science and Technology Facilities Council ( STFC ), Institut de biologie et chimie des protéines [Lyon] ( IBCP ), Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique ( CNRS ), H M Nautical Almanac Office [RAL] ( HMNAO ), Rutherford Appleton Laboratory, United Kingdom Met Office [Exeter], University College of London [London] ( UCL ), Department of Pathology and Laboratory Medicine [UCLA], University of California at Los Angeles [Los Angeles] ( UCLA ) -School of Medicine, School of Earth and Environmental Sciences [Seoul] ( SEES ), Seoul National University [Seoul], Department of Chemistry, Seoul Women's University, MicroMachines Centre ( MMC ), Nanyang Technological University [Singapour], Regroupement Québécois sur les Matériaux de Pointe ( RQMP ), École Polytechnique de Montréal ( EPM ) -Université de Sherbrooke [Sherbrooke]-McGill University-Université de Montréal-Fonds Québécois de Recherche sur la Nature et les Technologies ( FQRNT ), Département de Physique [Montréal], Université de Montréal, School of Earth and Environment [Leeds] ( SEE ), Centre for Ecology and Hydrology ( CEH ), Natural Environment Research Council ( NERC ), Norwegian Institute for Water Research ( NIVA ), Norwegian Institute for Water Research, Stony Brook University [SUNY] (SBU), State University of New York (SUNY)-State University of New York (SUNY), Astronomy Unit [London] (AU), Queen Mary University of London (QMUL), Commonwealth Scientific and Industrial Research Organisation Energy Technology (CSIRO Energy Technology), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Department of Biochemistry and Molecular Biology [Houston], The University of Texas Medical School at Houston, Mathematical Institute [Oxford] (MI), University of Oxford, Centre for the Analysis of Time Series (CATS), London School of Economics and Political Science (LSE), Thomas Jefferson National Accelerator Facility (Jefferson Lab), Laboratoire Énergies et Mécanique Théorique et Appliquée (LEMTA ), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Evolution, Génomes et Spéciation (LEGS), Centre National de la Recherche Scientifique (CNRS), University of Illinois System-University of Illinois System, Department of Electrical and Computer Engineering [Portland] (ECE), Portland State University [Portland] (PSU), Saint-Gobain, Institute for Animal Health (IAH), Biotechnology and Biological Sciences Research Council (BBSRC), Chinese Academy of Sciences [Changchun Branch] (CAS), Ipsen Inc. [Milford] (Ipsen), University of California [Berkeley] (UC Berkeley), University of California (UC)-University of California (UC), Institute for Climate and Atmospheric Science [Leeds] (ICAS), School of Earth and Environment [Leeds] (SEE), University of Leeds-University of Leeds, Chung-Ang University (CAU), Antarctic Climate and Ecosystems Cooperative Research Centre (ACE-CRC), Institute of Aerodynamics and Fluid Mechanics (AER), Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), Mer et santé (MS), Station biologique de Roscoff [Roscoff] (SBR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), National Physical Laboratory [Teddington] (NPL), International Research Institute for Climate and Society (IRI), Macaulay Institute, University of Cambridge [UK] (CAM), Queen's University [Kingston, Canada], Leslie Hill Institute for Plant Conservation (PCU), Istituto per la Microelettronica e Microsistemi [Catania] (IMM), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), Laboratoire d'Acoustique de l'Université du Mans (LAUM), Le Mans Université (UM)-Centre National de la Recherche Scientifique (CNRS), Interactive Systems Labs (ISL), Carnegie Mellon University [Pittsburgh] (CMU), Dalian Institute of Chemical Physics (DICP), Architectures, Languages and Compilers to Harness the End of Moore Years (ALCHEMY), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Clean Air Task Force (CATF), Indian Space Research Organisation (ISRO), Centre d'études et de recherches appliquées à la gestion (CERAG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Centre National de la Recherche Scientifique (CNRS), Sultan Qaboos University (SQU)-College of Medicine and Health Sciences [Baylor], Baylor University-Baylor University, European Molecular Biology Laboratory [Heidelberg] (EMBL), University of Michigan System-University of Michigan System, Department of Radiation Oncology [Michigan] (Radonc), Informatique, Biologie Intégrative et Systèmes Complexes (IBISC), Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), Institute for Meteorology and Climate Research (IMK), Karlsruhe Institute of Technology (KIT), University of New Brunswick (UNB), Laboratoire Parole et Langage (LPL), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Institut des Sciences Chimiques de Rennes (ISCR), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Ecole Nationale Supérieure de Chimie de Rennes (ENSCR)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Biogéosciences [UMR 6282] (BGS), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Matériaux à Porosité Contrôlée (LMPC), Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS), School of Information Engineering [USTB] (SIE), University of Science and Technology Beijing [Beijing] (USTB), Laboratory for Atmospheric and Space Physics [Boulder] (LASP), University of Colorado [Boulder], School of Mathematics and Statistics [Sheffield] (SoMaS), Laboratoire de Mécanique de Lille - FRE 3723 (LML), Université de Lille, Sciences et Technologies-Centrale Lille-Centre National de la Recherche Scientifique (CNRS), Computer Science Department [UCLA] (CSD), University of California [Los Angeles] (UCLA), Développement et évolution (DE), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Biologie du Développement de Villefranche sur mer (LBDV), Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Pierre Aigrain (LPA), Fédération de recherche du Département de physique de l'Ecole Normale Supérieure - ENS Paris (FRDPENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Department of Mathematics and Statistics [Montréal], McGill University = Université McGill [Montréal, Canada], Departamento de Botánica [Bariloche], Centro Regional Universitario Bariloche [Bariloche] (CRUB), Universidad Nacional del Comahue [Neuquén] (UNCOMA)-Universidad Nacional del Comahue [Neuquén] (UNCOMA), Bioénergétique Cellulaire et Pathologique (BECP), Université Joseph Fourier - Grenoble 1 (UJF)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Environnements et Paléoenvironnements OCéaniques (EPOC), Observatoire aquitain des sciences de l'univers (OASU), Université Sciences et Technologies - Bordeaux 1 (UB)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Sciences et Technologies - Bordeaux 1 (UB)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Institut Jacques Monod (IJM (UMR_7592)), Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Laboratori Nazionali del Sud (LNS), Istituto Nazionale di Fisica Nucleare (INFN), Departament de Matemàtiques [Barcelona] (UAB), Universitat Autònoma de Barcelona (UAB), Max-Planck-Institut für Kohlenforschung (Coal Research), Max-Planck-Gesellschaft, CAS Institute of Oceanology (IOCAS), Chinese Academy of Sciences [Beijing] (CAS), National Chiao Tung University (NCTU), Department of Hydrology and Water Resources (HWR), State Key Laboratory of Nuclear Physics and Technology (SKL-NPT), University of Iowa [Iowa City], NASA Ames Research Center (ARC), Digital Language & Knowledge Contents Research Association (DICORA), Space Science and Technology Department [Didcot] (RAL Space), STFC Rutherford Appleton Laboratory (RAL), Science and Technology Facilities Council (STFC)-Science and Technology Facilities Council (STFC), Institut de biologie et chimie des protéines [Lyon] (IBCP), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), H M Nautical Almanac Office [RAL] (HMNAO), University College of London [London] (UCL), University of California (UC)-University of California (UC)-School of Medicine, School of Earth and Environmental Sciences [Seoul] (SEES), Seoul National University [Seoul] (SNU), MicroMachines Centre (MMC), Regroupement Québécois sur les Matériaux de Pointe (RQMP), École Polytechnique de Montréal (EPM)-Université de Sherbrooke (UdeS)-McGill University = Université McGill [Montréal, Canada]-Université de Montréal (UdeM)-Fonds Québécois de Recherche sur la Nature et les Technologies (FQRNT), Université de Montréal (UdeM), Centre for Ecology and Hydrology (CEH), Natural Environment Research Council (NERC), Norwegian Institute for Water Research (NIVA), SEA URCHIN GENOME SEQUENCING CONSORTIUM, SODERGREN E, WEINSTOCK GM, DAVIDSON EH, CAMERON RA, GIBBS RA, ANGERER RC, ANGERER LM, ARNONE MI, BURGESS DR, BURKE RD, COFFMAN JA, DEAN M, ELPHICK MR, ETTENSOHN CA, FOLTZ KR, HAMDOUN A, HYNES RO, KLEIN WH, MARZLUFF W, MCCLAY DR, MORRIS RL, MUSHEGIAN A, RAST JP, SMITH LC, THORNDYKE MC, VACQUIER VD, WESSEL GM, WRAY G, ZHANG L, ELSIK CG, ERMOLAEVA O, HLAVINA W, HOFMANN G, KITTS P, LANDRUM MJ, MACKEY AJ, MAGLOTT D, PANOPOULOU G, POUSTKA AJ, PRUITT K, SAPOJNIKOV V, SONG X, SOUVOROV A, SOLOVYEV V, WEI Z, WHITTAKER CA, WORLEY K, DURBIN KJ, SHEN Y, FEDRIGO O, GARFIELD D, HAYGOOD R, PRIMUS A, SATIJA R, SEVERSON T, GONZALEZ-GARAY ML, JACKSON AR, MILOSAVLJEVIC A, TONG M, KILLIAN CE, LIVINGSTON BT, WILT FH, ADAMS N, BELLE R, CARBONNEAU S, CHEUNG R, CORMIER P, COSSON B, CROCE J, FERNANDEZ-GUERRA A, GENEVIERE AM, GOEL M, KELKAR H, MORALES J, MULNER-LORILLON O, ROBERTSON AJ, GOLDSTONE JV, COLE B, EPEL D, GOLD B, HAHN ME, HOWARD-ASHBY M, SCALLY M, STEGEMAN JJ, ALLGOOD EL, COOL J, JUDKINS KM, MCCAFFERTY SS, MUSANTE AM, OBAR RA, RAWSON AP, ROSSETTI BJ, GIBBONS IR, HOFFMAN MP, LEONE A, ISTRAIL S, MATERNA SC, SAMANTA MP, STOLC V, TONGPRASIT W, TU Q, BERGERON KF, BRANDHORST BP, WHITTLE J, BERNEY K, BOTTJER DJ, CALESTANI C, PETERSON K, CHOW E, YUAN QA, ELHAIK E, GRAUR D, REESE JT, BOSDET I, HEESUN S, MARRA MA, SCHEIN J, ANDERSON MK, BROCKTON V, BUCKLEY KM, COHEN AH, FUGMANN SD, HIBINO T, LOZA-COLL M, MAJESKE AJ, MESSIER C, NAIR SV, PANCER Z, TERWILLIGER DP, AGCA C, ARBOLEDA E, CHEN N, CHURCHER AM, HALLBOOK F, HUMPHREY GW, IDRIS MM, KIYAMA T, LIANG S, MELLOTT D, MU X, MURRAY G, OLINSKI RP, RAIBLE F, ROWE M, TAYLOR JS, TESSMAR-RAIBLE K, WANG D, WILSON KH, YAGUCHI S, GAASTERLAND T, GALINDO BE, GUNARATNE HJ, JULIANO C, KINUKAWA M, MOY GW, NEILL AT, NOMURA M, RAISCH M, READE A, ROUX MM, SONG JL, SU YH, TOWNLEY IK, VORONINA E, WONG JL, AMORE G, BRANNO M, BROWN ER, CAVALIERI, V, DUBOC V, DULOQUIN L, FLYTZANIS C, GACHE C, LAPRAZ F, LEPAGE T, LOCASCIO A, MART, University of California-University of California, Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC), Consiglio Nazionale delle Ricerche (CNR), Centre National de la Recherche Scientifique (CNRS)-Le Mans Université (UM), Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Ecole Nationale Supérieure de Chimie de Rennes (ENSCR)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Biogéosciences [UMR 6282] [Dijon] (BGS), Centre National de la Recherche Scientifique (CNRS)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Ecole Nationale Supérieure de Chimie de Mulhouse-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Fédération de recherche du Département de physique de l'Ecole Normale Supérieure - ENS Paris (FRDPENS), Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-École pratique des hautes études (EPHE), University of California-University of California-School of Medicine, Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Ecole Nationale Supérieure de Chimie de Rennes (ENSCR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Université de Lille, Sciences et Technologies-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille, Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Joseph Fourier - Grenoble 1 (UJF), University of Manchester Institute of Science and Technology (UMIST), Condensed Matter Physics and Materials Science Department, Brookhaven National Laboratory, Brookhaven National Laboratory [Upton, NY] (BNL), UT-Battelle, LLC-Stony Brook University [SUNY] (SBU), State University of New York (SUNY)-State University of New York (SUNY)-U.S. Department of Energy [Washington] (DOE)-UT-Battelle, LLC-Stony Brook University [SUNY] (SBU), State University of New York (SUNY)-State University of New York (SUNY)-U.S. Department of Energy [Washington] (DOE), Baylor College of Medicine (BCM), Baylor University, Laboratoire de Traitement de l'Information Medicale (LaTIM), Université européenne de Bretagne - European University of Brittany (UEB)-Université de Brest (UBO)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest), Laboratoire de Modélisation et Simulation Multi Echelle (MSME), Université Paris-Est Marne-la-Vallée (UPEM)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS), Duke University [Durham], Instituto Andaluz de Geofísica y Prevención de Desastres Sísmicos [Granada] (IAGPDS), Universidad de Granada (UGR), Laboratoire d'Ingénierie des Matériaux de Bretagne (LIMATB), Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université de Brest (UBO), University of New South Wales [Sydney] (UNSW), Celera Genomics (CRA), Celera Genomics, Paléobiodiversité et paléoenvironnements, Muséum national d'Histoire naturelle (MNHN)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Università degli Studi di Roma Tor Vergata [Roma], Unité de recherches forestières (BORDX PIERR UR ), Institut National de la Recherche Agronomique (INRA), Deptartment of Neuroscience, Uppsala University, State Key Laboratory of Palaeobiology and Stratigraphy, Nanjing Institute of Geology and Palaeontology (NIGPAS-CAS), Chinese Academy of Sciences [Nanjing Branch]-Chinese Academy of Sciences [Nanjing Branch], Institut Méditerranéen d'Ecologie et de Paléoécologie (IMEP), Université Paul Cézanne - Aix-Marseille 3-Université de Provence - Aix-Marseille 1-Avignon Université (AU)-Centre National de la Recherche Scientifique (CNRS), Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China, Université Paris Diderot - Paris 7 (UPD7), Department of Physical and Environmental Sciences [Toronto], University of Toronto at Scarborough, inconnu temporaire UPEMLV, Inconnu, Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Department of Atmospheric Sciences [Seattle], University of Washington [Seattle], National Institute of Advanced Industrial Science and Technology (AIST), Department of Pharmacy, Università degli studi di Genova = University of Genoa (UniGe), Interdisciplinary Arts and Sciences Department, St. Vincent's Hospital, Sydney, Laboratoire des Sciences de l'Environnement Marin (LEMAR) (LEMAR), Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Institut Universitaire Européen de la Mer (IUEM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Department of Electrical Engineering (DEE-POSTECH), Pohang University of Science and Technology (POSTECH), Centre Suisse d'Electronique et de Microtechnique SA [Neuchatel] (CSEM), Centre Suisse d'Electronique et Microtechnique SA (CSEM), Human Genome Sequencing Center [Houston] (HGSC), Brookhaven National Laboratory, Meteorological Service of Canada, 4905 Dufferin Street, Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Mines-Télécom [Paris] (IMT), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Université Paris-Est Marne-la-Vallée (UPEM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Unité de Recherches Forestières, Department of Physical and Environmental Sciences, University of Toronto [Scarborough, Canada], National Institute for Nuclear Physics (INFN), University of Genoa (UNIGE), Institut de Recherche pour le Développement (IRD)-Institut Universitaire Européen de la Mer (IUEM), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université de Brest (UBO)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS), Universidad de Granada = University of Granada (UGR), Laboratoire d'Energétique et de Mécanique Théorique Appliquée (LEMTA ), Technische Universität München [München] (TUM), Queen's University [Kingston], Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Grenoble Alpes (UGA), Institut für Meteorologie und Klimaforschung (IMK), Karlsruher Institut für Technologie (KIT), Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Supérieure de Chimie de Rennes-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES), Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Ecole Centrale de Lille-Université de Lille, Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-École pratique des hautes études (EPHE)-Centre National de la Recherche Scientifique (CNRS), Universitat Autònoma de Barcelona [Barcelona] (UAB), École Polytechnique de Montréal (EPM)-Université de Sherbrooke [Sherbrooke]-Université de Montréal [Montréal]-McGill University-Fonds Québécois de Recherche sur la Nature et les Technologies (FQRNT), Université de Montréal [Montréal], U.S. Department of Energy [Washington] (DOE)-UT-Battelle, LLC-Stony Brook University [SUNY] (SBU), Université de Bretagne Sud (UBS)-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université de Brest (UBO)-Université de Brest (UBO), Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC), Université Paul Cézanne - Aix-Marseille 3-Centre National de la Recherche Scientifique (CNRS)-Avignon Université (AU)-Université de Provence - Aix-Marseille 1, Institut Universitaire Européen de la Mer (IUEM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Centre National de la Recherche Scientifique (CNRS)-Université de Brest (UBO), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Université de Lille, Sciences et Technologies-Ecole Centrale de Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), École normale supérieure - Paris (ENS Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Male, MESH: Signal Transduction, MESH: Sequence Analysis, DNA, MESH : Transcription Factors, MESH : Calcification, Physiologic, Genome, MESH : Proteins, 0302 clinical medicine, MESH : Embryonic Development, MESH: Gene Expression Regulation, Developmental, Innate, MESH: Embryonic Development, Developmental, Nervous System Physiological Phenomena, MESH: Animals, MESH: Proteins, [SDV.BDD]Life Sciences [q-bio]/Development Biology, Complement Activation, ComputingMilieux_MISCELLANEOUS, MESH: Evolution, Molecular, MESH : Strongylocentrotus purpuratus, Genetics, 0303 health sciences, MESH: Nervous System Physiological Phenomena, Multidisciplinary, biology, Medicine (all), MESH: Immunologic Factors, Gene Expression Regulation, Developmental, Genome project, MESH: Transcription Factors, MESH : Immunity, Innate, MESH : Complement Activation, MESH: Genes, Bacterial artificial chromosome (BAC)DeuterostomesStrongylocentrotus purpuratusVertebrate innovations, Echinoderm, MESH : Nervous System Physiological Phenomena, embryonic structures, MESH: Cell Adhesion Molecules, MESH : Genes, MESH: Immunity, Innate, Sequence Analysis, Signal Transduction, MESH: Computational Biology, Genome evolution, MESH: Complement Activation, Sequence analysis, Evolution, MESH: Strongylocentrotus purpuratus, MESH : Male, Embryonic Development, MESH : Immunologic Factors, Article, MESH: Calcification, Physiologic, Calcification, MESH : Cell Adhesion Molecules, Evolution, Molecular, 03 medical and health sciences, Calcification, Physiologic, Animals, Immunologic Factors, MESH: Genome, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, MESH : Evolution, Molecular, Physiologic, Gene, Strongylocentrotus purpuratus, [ SDV.BBM ] Life Sciences [q-bio]/Biochemistry, Molecular Biology, 030304 developmental biology, MESH : Signal Transduction, Bacterial artificial chromosome, Immunity, Molecular, Computational Biology, Proteins, Cell Adhesion Molecules, Genes, Immunity, Innate, Transcription Factors, Sequence Analysis, DNA, DNA, biology.organism_classification, MESH: Male, Gene Expression Regulation, MESH : Animals, MESH : Gene Expression Regulation, Developmental, MESH : Genome, 030217 neurology & neurosurgery, MESH : Computational Biology, MESH : Sequence Analysis, DNA
Abstract

We report the sequence and analysis of the 814-megabase genome of the sea urchin Strongylocentrotus purpuratus , a model for developmental and systems biology. The sequencing strategy combined whole-genome shotgun and bacterial artificial chromosome (BAC) sequences. This use of BAC clones, aided by a pooling strategy, overcame difficulties associated with high heterozygosity of the genome. The genome encodes about 23,300 genes, including many previously thought to be vertebrate innovations or known only outside the deuterostomes. This echinoderm genome provides an evolutionary outgroup for the chordates and yields insights into the evolution of deuterostomes.

Published
2006

43. A QTL resource and comparison tool for pigs: PigQTLDB

Author

Max F. Rothschild, James M. Reecy, Donna Maglott, Zhi-Liang Hu, Svetlana Dracheva, Wonhee Jang, and John W M Bastiaansen

Subjects
Genome, business.industry, Swine, Entrez Gene, Gene Expression Profiling, Quantitative Trait Loci, food and beverages, Chromosome Mapping, Information Storage and Retrieval, Computational biology, Biology, Quantitative trait locus, Human genetics, Biotechnology, Resource (project management), Phenotype, Family-based QTL mapping, Databases, Genetic, Genetics, Trait, Animals, business, Functional genomics
Abstract

During the past decade, efforts to map quantitative trait loci (QTL) in pigs have resulted in hundreds of QTL being reported for growth, meat quality, reproduction, disease resistance, and other traits. It is a challenge to locate, interpret, and compare QTL results from different studies. We have developed a pig QTL database (PigQTLdb) that integrates available pig QTL data in the public domain, thus, facilitating the use of this QTL data in future studies. We also developed a pig trait classification system to standardize names of traits and to simplify organization and searching of the trait data. These steps made it possible to compare primary data from diverse sources and methods. We used existing pig map databases and other publicly available data resources (such as PubMed) to avoid redundant developmental work. The PigQTLdb was also designed to include data representing major genes and markers associated with a large effect on economically important traits. To date, over 790 QTL from 73 publications have been curated into the database. Those QTL cover more than 300 different traits. The data have been submitted to the Entrez Gene and the Map Viewer resources at NCBI, where the information about markers was matched to marker records in NCBI's UniSTS database. Having these data in a public resource like NCBI allows regularly updated automatic matching of markers to public sequence data by e-PCR. The submitted data, and the results of these calculations, are retrievable from NCBI via Entrez Gene, Map Viewer, and UniSTS. Efforts were undertaken to improve the integrated functional genomics resources for pigs.

Published
2005

44. Initial sequencing and comparative analysis of the mouse genome

Author

Laura Elnitski, David B. Jaffe, Jia Li, Marina Alexandersson, Michael J. Morgan, Shiaw Pyng Yang, Robert Baertsch, Claire M. Wade, John Tromp, Michael C. Zody, Terrence S. Furey, Emma Overton-Larty, Stephen D. Brown, Scott Schwartz, Diane M. Dunn, J. P. Leger, Kris A. Wetterstrand, David Torrents, Ratna Shownkeen, Brian Schultz, Kim C. Worley, Richard D. Emes, John Mayer, Tom Landers, Beverley Meredith, Carol Scott, R. J. Weber, Sean R. Eddy, David Kulp, Jun Kawai, J Bailey, Fan Hsu, Diana L. Kolbe, Kirsten McLay, Marc Botcherby, Richard Mott, Tracie L. Miner, Jill P. Mesirov, Cristyn Kells, Michael A. Quail, Melanie M. Wall, Alistair G. Rust, Josep F. Abril, Ian F Korf, Peter An, Roderic Guigó, Abel Ureta-Vidal, Evan Mauceli, L. Steven Johnson, Arian F.A. Smit, Arkadiusz Kasprzyk, Michael C. Wendl, Deanna M. Church, Francis S. Collins, Wayne N. Frankel, Pallavi Eswara, Bin Ma, Robert H. Waterston, Stylianos E. Antonarakis, Edward M. Rubin, John Douglas Mcpherson, Andrew Sheridan, Megan McCarthy, Ming Li, Colin N. Dewey, Justin Deri, Rosie Levine, Matthew Jones, Sheila Dodge, Richard R. Copley, Leo Goodstadt, Shan Yang, Donna Maglott, Jamey Wierzbowski, Nick Goldman, Evgeny M. Zdobnov, Simon G. Gregory, C M Clee, Steven Leonard, Elaine R. Mardis, Simon C. Potter, Sarah Sims, Richard A. Gibbs, Mark S. Guyer, Francesca Chiaromonte, Susan Lucas, Mark Diekhans, Steve Searle, Rachel Ainscough, Jane Peterson, Emmanouil T. Dermitzakis, Robert Nicol, Lucy Matthews, Guy Slater, Adam Felsenfeld, Karen Foley, Lucinda Fulton, Tim Hubbard, Richard K. Wilson, Deana W. LaHillier, W. Richard McCombie, Johanna Thompson, Robert David, John Attwood, Anthony P. West, Jane Rogers, Evan Keibler, Lisa Cook, Raju Kucherlapati, Steven Seaman, William E. Nash, Ian J. Jackson, Jonathan Singer, Axin Hua, Tina Graves, Ted Sharpe, Dudley Wyman, Bruce W. Birren, Stuart McLaren, David Willey, A Joy, Douglas Smith, Alexandre Reymond, Paul Flicek, Simon Cawley, Richa Agarwala, Diane Gage, Evanne Trevaskis, Ginger A. Fewell, Michael R. Brent, Tracy C. Ponce, W. James Kent, Timothy Holzer, Eduardo Eyras, Michael J. O’Connor, Webb Miller, Donna M. Muzny, Andrew von Niederhausern, Inna Dubchak, Eitan E. Winter, Catherine Ucla, Arne Stabenau, Michael N. Nhan, Piero Carninci, Michele Clamp, Pavel A. Pevzner, James Meldrim, Tim Cutts, R. D. Campbell, Joy Davies, Wratko Hlavina, Elinor K. Karlsson, David Haussler, John Burton, Peer Bork, Nicole Stange-Thomann, Mikita Suyama, Mark J. Daly, Ewan Birney, Edward J. Kulbokas, Craig Pohl, James C. Mullikin, Chad Nusbaum, Genís Parra, Jade P. Vinson, Yoshihide Hayashizaki, Sante Gnerre, Eric Berry, Daniel G. Brown, Asif T. Chinwalla, Emmanuel Mongin, Robert B. Weiss, Raymond Wheeler, Andrew Kirby, Yasushi Okazaki, Lior Pachter, Ross C. Hardison, Brian Spencer, Carol J. Bult, Joanne O. Nelson, Pankaj K. Agarwal, Darren Grafham, Gustavo Glusman, Thomas A. Jones, Glenn Tesler, Simon Whelan, James Cuff, Robert S. Fulton, K F Barlow, Jörg Schultz, Matthias S. Schwartz, Alex Poliakov, Jonathan Butler, Bruce A. Roe, Angela S. Hinrichs, Alan Coulson, Kate Montgomery, Eric D. Green, Stephan Beck, Val Curwen, Krishna M. Roskin, Robert W. Plumb, Chris P. Ponting, Ralph Santos, Victor Sapojnikov, Nicolas Bray, Kymberlie H. Pepin, Charles W. Sugnet, Olivier Couronne, Ivica Letunic, Sophie Williams, Kimberly D. Delehaunty, Kerstin Lindblad-Toh, Zemin Ning, Karen Oliver, Toby Bloom, Michael Kamal, Nicholas J. Dickens, Eric S. Lander, Christine Lloyd, Donna Karolchik, Adrienne Hunt, Antonarakis, Stylianos, Couronne, Olivier, Dermitzakis, Emmanouil, and Zdobnov, Evgeny

Subjects
RNA, Untranslated, Proteome, Untranslated/genetics, Genome, Transgenic, Repetitive Sequences, Mice, Models, Neoplasms, Conserved Sequence, ddc:616, Genetics, Mice, Knockout, Base Composition, Multidisciplinary, Sex Chromosomes, Pseudogenes/genetics, Genomics, Multigene Family/genetics, Physical Chromosome Mapping, Neoplasms/genetics, CpG Islands/genetics, Proteome/genetics, Genetic Variation/genetics, Multigene Family, Mice/classification/ genetics, Models, Animal, Conserved Sequence/genetics, Sequence Analysis, Pseudogenes, Human, Genome evolution, Evolution, Sequence analysis, Knockout, Quantitative Trait Loci, Mice, Transgenic, Sex Chromosomes/genetics, Computational biology, Biology, Synteny, Chromosomes, Evolution, Molecular, Nucleic Acid/genetics, Genetic, Species Specificity, Mammalian/ genetics, Animals, Humans, Genes/genetics, Selection, Genetic, Selection, Repetitive Sequences, Nucleic Acid, Comparative genomics, Animal, Genome, Human, Molecular, Genetic Variation, DNA, Sequence Analysis, DNA, Chromosomes, Mammalian, Gene Expression Regulation, Genes, Mutagenesis, RNA, Human genome, CpG Islands, Quantitative Trait Loci/genetics, Reference genome
Abstract

The sequence of the mouse genome is a key informational tool for understanding the contents of the human genome and a key experimental tool for biomedical research. Here, we report the results of an international collaboration to produce a high-quality draft sequence of the mouse genome. We also present an initial comparative analysis of the mouse and human genomes, describing some of the insights that can be gleaned from the two sequences. We discuss topics including the analysis of the evolutionary forces shaping the size, structure and sequence of the genomes; the conservation of large-scale synteny across most of the genomes; the much lower extent of sequence orthology covering less than half of the genomes; the proportions of the genomes under selection; the number of protein-coding genes; the expansion of gene families related to reproduction and immunity; the evolution of proteins; and the identification of intraspecies polymorphism.

Published
2002

45. The NIH genetic testing registry: Hereditary, pharmacogenetic, and somatic tests for oncology practice

Author

Malheiro Aj, Ricardo Villamarin-Salomon, Craig Wallin, Brandi L. Kattman, Guangfeng Song, Donna Maglott, Cathy Fomous, Kenneth S. Katz, James Ostell, Michael Ovetsky, Wendy S. Rubinstein, Vichet Hem, Baoshan Gu, and Jennifer M. Lee

Subjects
Oncology, Cancer Research, medicine.medical_specialty, medicine.diagnostic_test, Somatic cell, business.industry, Internal medicine, medicine, Hereditary Cancer, Dosing, business, Pharmacogenetics, Genetic testing
Abstract

11104 Background: Oncology professionals need to access and gauge information about tests for hereditary cancer predisposition, biomarkers, pharmacogenetic dosing and risk-based chemoprevention. NI...

Published
2014

46. A fast and scalable radiation hybrid map construction and integration strategy

Author

Donna Maglott, Gregory D. Schuler, David Applegate, Alejandro A. Schäffer, and Richa Agarwala

Subjects
Genetic Markers, Computation, Genetic Vectors, Biology, Haploidy, Hybrid Cells, computer.software_genre, Travelling salesman problem, Longest common subsequence problem, Software, Genetics, Methods, Humans, Genetics (clinical), Probability, Likelihood Functions, Contig, business.industry, Chromosome Mapping, Computational Biology, Data quality, Scalability, Combinatorial optimization, Data mining, business, computer, Algorithms
Abstract

This paper describes a fast and scalable strategy for constructing a radiation hybrid (RH) map from data on different RH panels. The maps on each panel are then integrated to produce a single RH map for the genome. Recurring problems in using maps from several sources are that the maps use different markers, the maps do not place the overlapping markers in same order, and the objective functions for map quality are incomparable. We use methods from combinatorial optimization to develop a strategy that addresses these issues. We show that by the standard objective functions of obligate chromosome breaks and maximum likelihood, software for the traveling salesman problem produces RH maps with better quality much more quickly than using software specifically tailored for RH mapping. We use known algorithms for the longest common subsequence problem as part of our map integration strategy. We demonstrate our methods by reconstructing and integrating maps for markers typed on the Genebridge 4 (GB4) and the Stanford G3 panels publicly available from the RH database. We compare map quality of our integrated map with published maps for GB4 panel and G3 panel by considering whether markers occur in the same order on a map and in DNA sequence contigs submitted to GenBank. We find that all of the maps are inconsistent with the sequence data for at least 50% of the contigs, but our integrated maps are more consistent. The map integration strategy not only scales to multiple RH maps but also to any maps that have comparable criteria for measuring map quality. Our software improves on current technology for doing RH mapping in areas of computation time and algorithms for considering a large number of markers for mapping. The essential impediments to producing dense high-quality RH maps are data quality and panel size, not computation.

Published
2000

47. RefSeq and LocusLink: NCBI gene-centered resources

Author

Kim D. Pruitt and Donna Maglott

Subjects
Databases, Factual, Biology, Genome, Article, Complementary DNA, Genetics, RefSeq, Gene family, Animals, Humans, RNA, Messenger, Gene, Comparative genomics, Information Services, Internet, National Library of Medicine (U.S.), Chromosome Mapping, Proteins, Genome project, United States, Genes, National Institutes of Health (U.S.), Sequence Alignment, Reference genome, Biotechnology
Abstract

Thousands of genes have been painstakingly identified and characterized a few genes at a time. Many thousands more are being predicted by large scale cDNA and genomic sequencing projects, with levels of evidence ranging from supporting mRNA sequence and comparative genomics to computing ab initio models. This, coupled with the burgeoning scientific literature, makes it critical to have a comprehensive directory for genes and reference sequences for key genomes. The NCBI provides two resources, LocusLink and RefSeq, to meet these needs. LocusLink organizes information around genes to generate a central hub for accessing gene-specific information for fruit fly, human, mouse, rat and zebrafish. RefSeq provides reference sequence standards for genomes, transcripts and proteins; human, mouse and rat mRNA RefSeqs, and their corresponding proteins, are discussed here. Together, RefSeq and LocusLink provide a non-redundant view of genes and other loci to support research on genes and gene families, variation, gene expression and genome annotation. Additional information about LocusLink and RefSeq is available at http://www.ncbi.nlm.nih.gov/LocusLink/.

Published
2000

48. NCBI's LocusLink and RefSeq

Author

Donna Maglott, Kim D. Pruitt, Kenneth S. Katz, and Hugues Sicotte

Subjects
Internet, Databases, Factual, National Library of Medicine (U.S.), Information Storage and Retrieval, Computational biology, Biology, Bioinformatics, United States, Article, Genetics, RefSeq, Database Management Systems, Humans, natural sciences, Reference genome, Web site
Abstract

The NCBI has introduced two new web resources-LocusLink and RefSeq-that facilitate retrieval of gene-based information and provide reference sequence standards. These resources are designed to provide a non-redundant view of current knowledge about human genes, transcripts and proteins. Additional information about these resources is available on the LocusLink web site at http://www.ncbi.nlm.nih.gov/LocusLink/

Published
1999

49. The Human Variome Project

Author

Marc S. Greenblatt, Carol Isaacson Barash, Jan Helge Solbakk, John Burn, Rajkumar Ramesar, Paul Flicek, Sue Povey, Rudolph E. Tanzi, Graham R. Taylor, Donna Maglott, Heather J. Howard, Michael Katz, Carolyn Sue Richards, Sean V. Tavtigian, Anthony J. Brookes, Joji Utsunomiya, Myles Axton, Nelson B. Freimer, María Jesús Sobrido, Timothy D. Smith, Daniela Seminara, Garry R. Cutting, Finlay A. Macrae, Arleen D. Auerbach, Samuel F. Berkovic, Johan T. den Dunnen, Richard G.H. Cotton, Michael E. Watson, and Gabriela Möslein

Subjects
Genetics, Human health, Multidisciplinary, Data_GENERAL, Human Variome Project, Interdisciplinary communication, ComputingMethodologies_GENERAL, Biology
Abstract

An ambitious plan to collect, curate, and make accessible information on genetic variations affecting human health is beginning to be realized.

Published
2008

50. A genome-wide analysis of biomineralization-related proteins in the sea urchin Strongylocentrotus purpuratus

Author

Victor Sapojnikov, Donna Maglott, A.M. Buchanan, Fred H. Wilt, Andrew Cameron, Christopher E. Killian, Melissa J. Landrum, Brian T. Livingston, Olga Ermolaeva, and Charles A. Ettensohn

Subjects
Biomineralization, Molecular Sequence Data, Genome, Paleontology, Calcification, Physiologic, biology.animal, Consensus Sequence, Animals, Amino Acid Sequence, Sea urchin, Gene, Molecular Biology, In Situ Hybridization, DNA Primers, biology, Echinoderm, Reverse Transcriptase Polymerase Chain Reaction, Phylum, Proteins, Vertebrate, Cell Biology, biology.organism_classification, Strongylocentrotus purpuratus, Evolutionary biology, Sea Urchins, Vertebrates, Sequence Alignment, Echinodermata, Developmental Biology
Abstract

Biomineralization, the biologically controlled formation of mineral deposits, is of widespread importance in biology, medicine, and engineering. Mineralized structures are found in most metazoan phyla and often have supportive, protective, or feeding functions. Among deuterostomes, only echinoderms and vertebrates produce extensive biomineralized structures. Although skeletons appeared independently in these two groups, ancestors of the vertebrates and echinoderms may have utilized similar components of a shared genetic “toolkit” to carry out biomineralization. The present study had two goals. First, we sought to expand our understanding of the proteins involved in biomineralization in the sea urchin, a powerful model system for analyzing the basic cellular and molecular mechanisms that underlie this process. Second, we sought to shed light on the possible evolutionary relationships between biomineralization in echinoderms and vertebrates. We used several computational methods to survey the genome of the purple sea urchin Strongylocentrotus purpuratus for gene products involved in biomineralization. Our analysis has greatly expanded the collection of biomineralization-related proteins. We have found that these proteins are often members of small families encoded by genes that are clustered in the genome. Most of the proteins are sea urchin-specific; that is, they have no apparent homologues in other invertebrate deuterostomes or vertebrates. Similarly, many of the vertebrate proteins that mediate mineral deposition do not have counterparts in the S. purpuratus genome. Our findings therefore reveal substantial differences in the primary sequences of proteins that mediate biomineral formation in echinoderms and vertebrates, possibly reflecting loose constraints on the primary structures of the proteins involved. On the other hand, certain cellular and molecular processes associated with earlier events in skeletogenesis appear similar in echinoderms and vertebrates, leaving open the possibility of deeper evolutionary relationships.

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