Valerie Wood

Ms. Valerie Wood


Research Focus & Metrics

Academic Career Length

28Years (1999-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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3 March 2023 in Genetics
Verified
Journal Article
Open access
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TCThe Gene Ontology Consortium
The Gene Ontology Consortium
SASuzi A Aleksander
Suzi A Aleksander
JBJames Balhoff
James Balhoff
SCSeth Carbon
Seth Carbon
JCJ Michael Cherry
J Michael Cherry
MWMonte Westerfield
Monte Westerfield
The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO—a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations—evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)—mechanistic models of molecular “pathways” (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project.

21 February 2002 in Nature
Verified
Journal Article
Closed access
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VWValerie Wood
Valerie Wood
RGR Gwilliam
R Gwilliam
MRM-A Rajandream
M-A Rajandream
MLM Lyne
M Lyne
RLR Lyne
R Lyne
LCL Cerrutti
L Cerrutti

18 May 2008 in Nature
Verified
Journal Article
Closed access
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BWBrian T. Wilhelm
Brian T. Wilhelm
SMSamuel Marguerat
Samuel Marguerat
SWStephen Watt
Stephen Watt
FSFalk Schubert
Falk Schubert
VWValerie Wood
Valerie Wood
Jurg BahlerJurg Bahler
Jurg Bahler

16 May 2010 in Nature Biotechnology
Verified
Journal Article
Closed access
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DKDong-Uk Kim
Dong-Uk Kim
JHJacqueline Hayles
Jacqueline Hayles
DKDongsup Kim
Dongsup Kim
VWValerie Wood
Valerie Wood
HPHan-Oh Park
Han-Oh Park
KHKwang-Lae Hoe
Kwang-Lae Hoe

13 November 2007 in Nucleic Acids Research
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Journal Article
Open access
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BBBobby-Joe Breitkreutz
Bobby-Joe Breitkreutz
CSChris Stark
Chris Stark
TRTeresa Reguly
Teresa Reguly
LBLorrie Boucher
Lorrie Boucher
ABAshton Breitkreutz
Ashton Breitkreutz
MTMike Tyers
Mike Tyers
The Biological General Repository for Interaction Datasets (BioGRID) database ( http://www.thebiogrid.org ) was developed to house and distribute collections of protein and genetic interactions from major model organism species. BioGRID currently contains over 198 000 interactions from six different species, as derived from both high-throughput studies and conventional focused studies. Through comprehensive curation efforts, BioGRID now includes a virtually complete set of interactions reported to date in the primary literature for both the budding yeast Saccharomyces cerevisiae and the fission yeast Schizosaccharomyces pombe . A number of new features have been added to the BioGRID including an improved user interface to display interactions based on different attributes, a mirror site and a dedicated interaction management system to coordinate curation across different locations. The BioGRID provides interaction data with monthly updates to Saccharomyces Genome Database, Flybase and Entrez Gene. Source code for the BioGRID and the linked Osprey network visualization system is now freely available without restriction.

14 November 2024 in Nucleic Acids Research
Verified
Journal Article
Open access
PDF file
TPTyphaine Paysan-Lafosse
Typhaine Paysan-Lafosse
AAAntonina Andreeva
Antonina Andreeva
MBMatthias Blum
Matthias Blum
SCSara Rocio Chuguransky
Sara Rocio Chuguransky
TGTiago Grego
Tiago Grego
ABAlex Bateman
Alex Bateman
The Pfam protein families database is a comprehensive collection of protein domains and families used for genome annotation and protein structure and function analysis (https://www.ebi.ac.uk/interpro/). This update describes major developments in Pfam since 2020, including decommissioning the Pfam website and integration with InterPro, harmonization with the ECOD structural classification, and expanded curation of metagenomic, microprotein and repeat-containing families. We highlight how AlphaFold structure predictions are being leveraged to refine domain boundaries and identify new domains. New families discovered through large-scale sequence similarity analysis of AlphaFold models are described. We also detail the development of Pfam-N, which uses deep learning to expand family coverage, achieving an 8.8% increase in UniProtKB coverage compared to standard Pfam. We discuss plans for more frequent Pfam releases integrated with InterPro and the potential for artificial intelligence to further assist curation. Despite recent advances, many protein families remain to be classified, and Pfam continues working toward comprehensive coverage of the protein universe.