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Article ; Online: Overview of the COVID-19 text mining tool interactive demonstration track in BioCreative VII.

Chatr-Aryamontri, Andrew / Hirschman, Lynette / Ross, Karen E / Oughtred, Rose / Krallinger, Martin / Dolinski, Kara / Tyers, Mike / Korves, Tonia / Arighi, Cecilia N

Database : the journal of biological databases and curation

2022 Volume 2022

Abstract: The coronavirus disease 2019 (COVID-19) pandemic has compelled biomedical researchers to communicate data in real time to establish more effective medical treatments and public health policies. Nontraditional sources such as preprint publications, i.e. ... ...

Abstract	The coronavirus disease 2019 (COVID-19) pandemic has compelled biomedical researchers to communicate data in real time to establish more effective medical treatments and public health policies. Nontraditional sources such as preprint publications, i.e. articles not yet validated by peer review, have become crucial hubs for the dissemination of scientific results. Natural language processing (NLP) systems have been recently developed to extract and organize COVID-19 data in reasoning systems. Given this scenario, the BioCreative COVID-19 text mining tool interactive demonstration track was created to assess the landscape of the available tools and to gauge user interest, thereby providing a two-way communication channel between NLP system developers and potential end users. The goal was to inform system designers about the performance and usability of their products and to suggest new additional features. Considering the exploratory nature of this track, the call for participation solicited teams to apply for the track, based on their system's ability to perform COVID-19-related tasks and interest in receiving user feedback. We also recruited volunteer users to test systems. Seven teams registered systems for the track, and >30 individuals volunteered as test users; these volunteer users covered a broad range of specialties, including bench scientists, bioinformaticians and biocurators. The users, who had the option to participate anonymously, were provided with written and video documentation to familiarize themselves with the NLP tools and completed a survey to record their evaluation. Additional feedback was also provided by NLP system developers. The track was well received as shown by the overall positive feedback from the participating teams and the users. Database URL: https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-4/.
MeSH term(s)	COVID-19/epidemiology ; Data Mining/methods ; Databases, Factual ; Documentation ; Humans ; Natural Language Processing
Language	English
Publishing date	2022-09-22
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	2496706-3
ISSN	1758-0463 ; 1758-0463
ISSN (online)	1758-0463
ISSN	1758-0463
DOI	10.1093/database/baac084
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Global analysis of the yeast knockout phenome.

Turco, Gina / Chang, Christie / Wang, Rebecca Y / Kim, Griffin / Stoops, Emily H / Richardson, Brianna / Sochat, Vanessa / Rust, Jennifer / Oughtred, Rose / Thayer, Nathaniel / Kang, Fan / Livstone, Michael S / Heinicke, Sven / Schroeder, Mark / Dolinski, Kara J / Botstein, David / Baryshnikova, Anastasia

Science advances

2023 Volume 9, Issue 21, Page(s) eadg5702

Abstract: Genome-wide phenotypic screens in the budding ... ...

Abstract	Genome-wide phenotypic screens in the budding yeast
MeSH term(s)	Humans ; Saccharomyces cerevisiae/genetics
Language	English
Publishing date	2023-05-26
Publishing country	United States
Document type	Journal Article
ZDB-ID	2810933-8
ISSN	2375-2548 ; 2375-2548
ISSN (online)	2375-2548
ISSN	2375-2548
DOI	10.1126/sciadv.adg5702
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Article: A Computational Framework for Genome-wide Characterization of the Human Disease Landscape.

Lee, Young-Suk / Krishnan, Arjun / Oughtred, Rose / Rust, Jennifer / Chang, Christie S / Ryu, Joseph / Kristensen, Vessela N / Dolinski, Kara / Theesfeld, Chandra L / Troyanskaya, Olga G

Cell systems

2019 Volume 8, Issue 2, Page(s) 152–162.e6

Abstract: A key challenge for the diagnosis and treatment of complex human diseases is identifying their molecular basis. Here, we developed a unified computational framework, ... ...

Abstract	A key challenge for the diagnosis and treatment of complex human diseases is identifying their molecular basis. Here, we developed a unified computational framework, URSA
MeSH term(s)	Gene Expression Profiling/methods ; Genomics/methods ; Humans ; Machine Learning/standards ; Transcriptome/genetics
Language	English
Publishing date	2019-01-23
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2854138-8
ISSN	2405-4720 ; 2405-4712
ISSN (online)	2405-4720
ISSN	2405-4712
DOI	10.1016/j.cels.2018.12.010
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Article ; Online: The BioGRID database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions.

Oughtred, Rose / Rust, Jennifer / Chang, Christie / Breitkreutz, Bobby-Joe / Stark, Chris / Willems, Andrew / Boucher, Lorrie / Leung, Genie / Kolas, Nadine / Zhang, Frederick / Dolma, Sonam / Coulombe-Huntington, Jasmin / Chatr-Aryamontri, Andrew / Dolinski, Kara / Tyers, Mike

Protein science : a publication of the Protein Society

2020 Volume 30, Issue 1, Page(s) 187–200

Abstract: The BioGRID (Biological General Repository for Interaction Datasets, thebiogrid.org) is an open-access database resource that houses manually curated protein and genetic interactions from multiple species including yeast, worm, fly, mouse, and human. The ...

Abstract	The BioGRID (Biological General Repository for Interaction Datasets, thebiogrid.org) is an open-access database resource that houses manually curated protein and genetic interactions from multiple species including yeast, worm, fly, mouse, and human. The ~1.93 million curated interactions in BioGRID can be used to build complex networks to facilitate biomedical discoveries, particularly as related to human health and disease. All BioGRID content is curated from primary experimental evidence in the biomedical literature, and includes both focused low-throughput studies and large high-throughput datasets. BioGRID also captures protein post-translational modifications and protein or gene interactions with bioactive small molecules including many known drugs. A built-in network visualization tool combines all annotations and allows users to generate network graphs of protein, genetic and chemical interactions. In addition to general curation across species, BioGRID undertakes themed curation projects in specific aspects of cellular regulation, for example the ubiquitin-proteasome system, as well as specific disease areas, such as for the SARS-CoV-2 virus that causes COVID-19 severe acute respiratory syndrome. A recent extension of BioGRID, named the Open Repository of CRISPR Screens (ORCS, orcs.thebiogrid.org), captures single mutant phenotypes and genetic interactions from published high throughput genome-wide CRISPR/Cas9-based genetic screens. BioGRID-ORCS contains datasets for over 1,042 CRISPR screens carried out to date in human, mouse and fly cell lines. The biomedical research community can freely access all BioGRID data through the web interface, standardized file downloads, or via model organism databases and partner meta-databases.
MeSH term(s)	Animals ; COVID-19/genetics ; COVID-19/virology ; Databases, Factual ; Humans ; Mice ; Protein Interaction Mapping ; Proteins/genetics ; SARS-CoV-2/genetics ; SARS-CoV-2/pathogenicity ; User-Computer Interface
Chemical Substances	Proteins
Keywords	covid19
Language	English
Publishing date	2020-11-23
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1106283-6
ISSN	1469-896X ; 0961-8368
ISSN (online)	1469-896X
ISSN	0961-8368
DOI	10.1002/pro.3978
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Zs.A 3407: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
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Article ; Online: Proteome-wide, Structure-Based Prediction of Protein-Protein Interactions/New Molecular Interactions Viewer.

Dong, Shaowei / Lau, Vincent / Song, Richard / Ierullo, Matthew / Esteban, Eddi / Wu, Yingzhou / Sivieng, Teeratham / Nahal, Hardeep / Gaudinier, Allison / Pasha, Asher / Oughtred, Rose / Dolinski, Kara / Tyers, Mike / Brady, Siobhan M / Grene, Ruth / Usadel, Björn / Provart, Nicholas J

Plant physiology

2019 Volume 179, Issue 4, Page(s) 1893–1907

Abstract: Determining the complete Arabidopsis ( ...

Abstract	Determining the complete Arabidopsis (
MeSH term(s)	Algorithms ; Arabidopsis/genetics ; Arabidopsis/metabolism ; Arabidopsis Proteins/chemistry ; Arabidopsis Proteins/metabolism ; Models, Molecular ; Molecular Docking Simulation ; Protein Interaction Maps ; Proteome ; Software ; Two-Hybrid System Techniques
Chemical Substances	Arabidopsis Proteins ; Proteome
Language	English
Publishing date	2019-01-24
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	208914-2
ISSN	1532-2548 ; 0032-0889
ISSN (online)	1532-2548
ISSN	0032-0889
DOI	10.1104/pp.18.01216
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Article ; Online: Selective Neuronal Vulnerability in Alzheimer's Disease: A Network-Based Analysis.

Neuron

2020 Volume 107, Issue 5, Page(s) 821–835.e12

Abstract: A major obstacle to treating Alzheimer's disease (AD) is our lack of understanding of the molecular mechanisms underlying selective neuronal vulnerability, a key characteristic of the disease. Here, we present a framework integrating high-quality neuron- ... ...

Abstract	A major obstacle to treating Alzheimer's disease (AD) is our lack of understanding of the molecular mechanisms underlying selective neuronal vulnerability, a key characteristic of the disease. Here, we present a framework integrating high-quality neuron-type-specific molecular profiles across the lifetime of the healthy mouse, which we generated using bacTRAP, with postmortem human functional genomics and quantitative genetics data. We demonstrate human-mouse conservation of cellular taxonomy at the molecular level for neurons vulnerable and resistant in AD, identify specific genes and pathways associated with AD neuropathology, and pinpoint a specific functional gene module underlying selective vulnerability, enriched in processes associated with axonal remodeling, and affected by amyloid accumulation and aging. We have made all cell-type-specific profiles and functional networks available at http://alz.princeton.edu. Overall, our study provides a molecular framework for understanding the complex interplay between Aβ, aging, and neurodegeneration within the most vulnerable neurons in AD.
MeSH term(s)	Aging/genetics ; Aging/pathology ; Alzheimer Disease/genetics ; Alzheimer Disease/pathology ; Animals ; Gene Expression Profiling/methods ; Gene Regulatory Networks/physiology ; Humans ; Machine Learning ; Mice ; Neurons/pathology ; Transcriptome
Language	English
Publishing date	2020-06-29
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	808167-0
ISSN	1097-4199 ; 0896-6273
ISSN (online)	1097-4199
ISSN	0896-6273
DOI	10.1016/j.neuron.2020.06.010
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Zs.A 2496: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
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Article ; Online: The BioC-BioGRID corpus: full text articles annotated for curation of protein-protein and genetic interactions.

Islamaj Dogan, Rezarta / Kim, Sun / Chatr-Aryamontri, Andrew / Chang, Christie S / Oughtred, Rose / Rust, Jennifer / Wilbur, W John / Comeau, Donald C / Dolinski, Kara / Tyers, Mike

Database : the journal of biological databases and curation

2017 Volume 2017

Abstract: A great deal of information on the molecular genetics and biochemistry of model organisms has been reported in the scientific literature. However, this data is typically described in free text form and is not readily amenable to computational analyses. ... ...

Abstract	A great deal of information on the molecular genetics and biochemistry of model organisms has been reported in the scientific literature. However, this data is typically described in free text form and is not readily amenable to computational analyses. To this end, the BioGRID database systematically curates the biomedical literature for genetic and protein interaction data. This data is provided in a standardized computationally tractable format and includes structured annotation of experimental evidence. BioGRID curation necessarily involves substantial human effort by expert curators who must read each publication to extract the relevant information. Computational text-mining methods offer the potential to augment and accelerate manual curation. To facilitate the development of practical text-mining strategies, a new challenge was organized in BioCreative V for the BioC task, the collaborative Biocurator Assistant Task. This was a non-competitive, cooperative task in which the participants worked together to build BioC-compatible modules into an integrated pipeline to assist BioGRID curators. As an integral part of this task, a test collection of full text articles was developed that contained both biological entity annotations (gene/protein and organism/species) and molecular interaction annotations (protein-protein and genetic interactions (PPIs and GIs)). This collection, which we call the BioC-BioGRID corpus, was annotated by four BioGRID curators over three rounds of annotation and contains 120 full text articles curated in a dataset representing two major model organisms, namely budding yeast and human. The BioC-BioGRID corpus contains annotations for 6409 mentions of genes and their Entrez Gene IDs, 186 mentions of organism names and their NCBI Taxonomy IDs, 1867 mentions of PPIs and 701 annotations of PPI experimental evidence statements, 856 mentions of GIs and 399 annotations of GI evidence statements. The purpose, characteristics and possible future uses of the BioC-BioGRID corpus are detailed in this report.Database URL: http://bioc.sourceforge.net/BioC-BioGRID.html.
MeSH term(s)	Data Curation/methods ; Data Mining/methods ; Databases, Genetic ; Proteins/genetics ; Proteins/metabolism
Chemical Substances	Proteins
Language	English
Publishing date	2017-01-10
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Intramural ; Research Support, N.I.H., Extramural
ZDB-ID	2496706-3
ISSN	1758-0463 ; 1758-0463
ISSN (online)	1758-0463
ISSN	1758-0463
DOI	10.1093/database/baw147
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Characterization of E3Histone, a novel testis ubiquitin protein ligase which ubiquitinates histones.

Liu, Zhiqian / Oughtred, Rose / Wing, Simon S

Molecular and cellular biology

2005 Volume 25, Issue 7, Page(s) 2819–2831

Abstract: During spermatogenesis, a large fraction of cellular proteins is degraded as the spermatids evolve to their elongated mature forms. In particular, histones must be degraded in early elongating spermatids to permit chromatin condensation. Our laboratory ... ...

Abstract	During spermatogenesis, a large fraction of cellular proteins is degraded as the spermatids evolve to their elongated mature forms. In particular, histones must be degraded in early elongating spermatids to permit chromatin condensation. Our laboratory previously demonstrated the activation of ubiquitin conjugation during spermatogenesis. This activation is dependent on the ubiquitin-conjugating enzyme (E2) UBC4, and a testis-particular isoform, UBC4-testis, is induced when histones are degraded. Therefore, we tested whether there are UBC4-dependent ubiquitin protein ligases (E3s) that can ubiquitinate histones. Indeed, a novel enzyme, E3Histone, which could conjugate ubiquitin to histones H1, H2A, H2B, H3, and H4 in vitro, was found. Only the UBC4/UBC5 family of E2s supported E3Histone-dependent ubiquitination of histone H2A, and of this family, UBC4-1 and UBC4-testis are the preferred E2s. We purified this ligase activity 3,600-fold to near homogeneity. Mass spectrometry of the final material revealed the presence of a 482-kDa HECT domain-containing protein, which was previously named LASU1. Anti-LASU1 antibodies immunodepleted E3Histone activity. Mass spectrometry and size analysis by gel filtration and glycerol gradient centrifugation suggested that E3Histone is a monomer of LASU1. Our assays also show that this enzyme is the major UBC4-1-dependent histone-ubiquitinating E3. E3Histone is therefore a HECT domain E3 that likely plays an important role in the chromatin condensation that occurs during spermatid maturation.
MeSH term(s)	Amino Acid Sequence ; Animals ; Cattle ; Histones/metabolism ; Humans ; Male ; Mice ; Molecular Sequence Data ; Sequence Alignment ; Testis/enzymology ; Ubiquitin/metabolism ; Ubiquitin-Protein Ligases/chemistry ; Ubiquitin-Protein Ligases/genetics ; Ubiquitin-Protein Ligases/isolation & purification ; Ubiquitin-Protein Ligases/metabolism
Chemical Substances	Histones ; Ubiquitin ; HUWE1 protein, human (EC 2.3.2.26) ; Huwe1 protein, mouse (EC 2.3.2.26) ; Ubiquitin-Protein Ligases (EC 2.3.2.27)
Language	English
Publishing date	2005-04
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	779397-2
ISSN	1098-5549 ; 0270-7306
ISSN (online)	1098-5549
ISSN	0270-7306
DOI	10.1128/MCB.25.7.2819-2831.2005
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Article ; Online: The BioGRID interaction database: 2019 update.

Oughtred, Rose / Stark, Chris / Breitkreutz, Bobby-Joe / Rust, Jennifer / Boucher, Lorrie / Chang, Christie / Kolas, Nadine / O'Donnell, Lara / Leung, Genie / McAdam, Rochelle / Zhang, Frederick / Dolma, Sonam / Willems, Andrew / Coulombe-Huntington, Jasmin / Chatr-Aryamontri, Andrew / Dolinski, Kara / Tyers, Mike

Nucleic acids research

2018 Volume 47, Issue D1, Page(s) D529–D541

Abstract: The Biological General Repository for Interaction Datasets (BioGRID: https://thebiogrid.org) is an open access database dedicated to the curation and archival storage of protein, genetic and chemical interactions for all major model organism species and ... ...

Abstract	The Biological General Repository for Interaction Datasets (BioGRID: https://thebiogrid.org) is an open access database dedicated to the curation and archival storage of protein, genetic and chemical interactions for all major model organism species and humans. As of September 2018 (build 3.4.164), BioGRID contains records for 1 598 688 biological interactions manually annotated from 55 809 publications for 71 species, as classified by an updated set of controlled vocabularies for experimental detection methods. BioGRID also houses records for >700 000 post-translational modification sites. BioGRID now captures chemical interaction data, including chemical-protein interactions for human drug targets drawn from the DrugBank database and manually curated bioactive compounds reported in the literature. A new dedicated aspect of BioGRID annotates genome-wide CRISPR/Cas9-based screens that report gene-phenotype and gene-gene relationships. An extension of the BioGRID resource called the Open Repository for CRISPR Screens (ORCS) database (https://orcs.thebiogrid.org) currently contains over 500 genome-wide screens carried out in human or mouse cell lines. All data in BioGRID is made freely available without restriction, is directly downloadable in standard formats and can be readily incorporated into existing applications via our web service platforms. BioGRID data are also freely distributed through partner model organism databases and meta-databases.
MeSH term(s)	Animals ; CRISPR-Cas Systems ; Data Curation ; Databases, Factual ; Drug Discovery ; Genes ; Humans ; Mice ; Protein Interaction Mapping
Language	English
Publishing date	2018-11-05
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gky1079
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Article ; Online: The BioGRID interaction database: 2017 update.

Chatr-Aryamontri, Andrew / Oughtred, Rose / Boucher, Lorrie / Rust, Jennifer / Chang, Christie / Kolas, Nadine K / O'Donnell, Lara / Oster, Sara / Theesfeld, Chandra / Sellam, Adnane / Stark, Chris / Breitkreutz, Bobby-Joe / Dolinski, Kara / Tyers, Mike

Nucleic acids research

2016 Volume 45, Issue D1, Page(s) D369–D379

Abstract: The Biological General Repository for Interaction Datasets (BioGRID: https://thebiogrid.org) is an open access database dedicated to the annotation and archival of protein, genetic and chemical interactions for all major model organism species and humans. ...

Abstract	The Biological General Repository for Interaction Datasets (BioGRID: https://thebiogrid.org) is an open access database dedicated to the annotation and archival of protein, genetic and chemical interactions for all major model organism species and humans. As of September 2016 (build 3.4.140), the BioGRID contains 1 072 173 genetic and protein interactions, and 38 559 post-translational modifications, as manually annotated from 48 114 publications. This dataset represents interaction records for 66 model organisms and represents a 30% increase compared to the previous 2015 BioGRID update. BioGRID curates the biomedical literature for major model organism species, including humans, with a recent emphasis on central biological processes and specific human diseases. To facilitate network-based approaches to drug discovery, BioGRID now incorporates 27 501 chemical-protein interactions for human drug targets, as drawn from the DrugBank database. A new dynamic interaction network viewer allows the easy navigation and filtering of all genetic and protein interaction data, as well as for bioactive compounds and their established targets. BioGRID data are directly downloadable without restriction in a variety of standardized formats and are freely distributed through partner model organism databases and meta-databases.
MeSH term(s)	Animals ; Computational Biology/methods ; Data Curation ; Data Mining ; Databases, Genetic ; Humans ; Protein Interaction Mapping ; Protein Interaction Maps ; Protein Processing, Post-Translational ; Proteins/chemistry ; Proteins/genetics ; Proteins/metabolism ; Software
Chemical Substances	Proteins
Language	English
Publishing date	2016-12-14
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gkw1102
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