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Jerven T. Bolleman

dblp:64/8909 · DBLP profile ↗
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6ranked-venue papers
0as first author
2since 2021 · last 2023
0000-0002-7449-1266ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 6 · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
6 papers
Bioinformatics and computational biology · 100%
Databases, data mining, and information retrieval
2 papers
Data integration and cleaning · 100%

Topics — the 7 heaviest of 9, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › protein analysis › protein bioinformatics
protein annotation		 1.122023
Annotation of biologically relevant ligands in UniProtKB using ChEBI · Bioinform. 2023
Enzyme annotation in UniProtKB using Rhea · Bioinform. 2020
Bioinformatics and computational biology › genome annotation
automatic annotation		 0.922021
UniRule: a unified rule resource for automatic annotation in the UniProt Knowledgebase · Bioinform. 2021
UniRule: a unified rule resource for automatic annotation in the UniProt Knowledgebase · Bioinform. 2020
Bioinformatics and computational biology
protein function prediction		 0.922021
UniRule: a unified rule resource for automatic annotation in the UniProt Knowledgebase · Bioinform. 2021
UniRule: a unified rule resource for automatic annotation in the UniProt Knowledgebase · Bioinform. 2020
Bioinformatics and computational biology › protein function prediction
enzyme function annotation		 0.412020
Enzyme annotation in UniProtKB using Rhea · Bioinform. 2020
Bioinformatics and computational biology
identifier mapping		 0.212015
SPARQL-enabled identifier conversion with Identifiers.org · Bioinform. 2015
Bioinformatics and computational biology › data integration
semantic data integration		 0.212015
SPARQL-enabled identifier conversion with Identifiers.org · Bioinform. 2015
Data integration and cleaning › scientific data integration
biological data integration		 0.212014
The EBI RDF platform: linked open data for the life sciences · Bioinform. 2014

Methods — techniques the papers use, named apart from their topics

ontology-based annotation · 1.1SPARQL querying · 1.1interpro signatures · 0.9SPARQL · 0.4
YearPublicationVenuePosition
2023 Annotation of biologically relevant ligands in UniProtKB using ChEBI
abstract
MOTIVATION: To provide high quality, computationally tractable annotation of binding sites for biologically relevant (cognate) ligands in UniProtKB using the chemical ontology ChEBI (Chemical Entities of Biological Interest), to better support efforts to study and predict functionally relevant interactions between protein sequences and structures and small molecule ligands. RESULTS: We structured the data model for cognate ligand binding site annotations in UniProtKB and performed a complete reannotation of all cognate ligand binding sites using stable unique identifiers from ChEBI, which we now use as the reference vocabulary for all such annotations. We developed improved search and query facilities for cognate ligands in the UniProt website, REST API and SPARQL endpoint that leverage the chemical structure data, nomenclature and classification that ChEBI provides. AVAILABILITY AND IMPLEMENTATION: Binding site annotations for cognate ligands described using ChEBI are available for UniProtKB protein sequence records in several formats (text, XML and RDF) and are freely available to query and download through the UniProt website (www.uniprot.org), REST API (www.uniprot.org/help/api), SPARQL endpoint (sparql.uniprot.org/) and FTP site (https://ftp.uniprot.org/pub/databases/uniprot/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Elisabeth Coudert, Sebastien Gehant, Edouard De Castro, Monica Pozzato, Delphine Baratin, Teresa Batista Neto, Christian J. A. Sigrist, Nicole Redaschi, Alan J. Bridge, Lucila Aimo, Ghislaine Argoud-Puy, Andrea H. Auchincloss, Kristian B. Axelsen, Parit Bansal, Marie-Claude Blatter, Jerven T. Bolleman, Emmanuel Boutet, Lionel Breuza, Blanca Cabrera Gil, Cristina Casals-Casas, Kamal Chikh Echioukh, Béatrice A. Cuche, Anne Estreicher, Maria Livia Famiglietti, Marc Feuermann, Elisabeth Gasteiger, Pascale Gaudet, Vivienne Baillie Gerritsen, Arnaud Gos, Nadine Gruaz-Gumowski, Chantal Hulo, Nevila Hyka-Nouspikel, Florence Jungo, Arnaud Kerhornou, Philippe Le Mercier, Damien Lieberherr, Patrick Masson, Anne Morgat, Venkatesh Muthukrishnan, Salvo Paesano, Ivo Pedruzzi, Sandrine Pilbout, Lucille Pourcel, Sylvain Poux, Manuela Pruess, Catherine Rivoire, Karin Sonesson, Shyamala Sundaram, Alex Bateman, Maria Jesus Martin, Sandra E. Orchard, Michele Magrane, Shadab Ahmad, Emanuele Alpi, Emily H. Bowler-Barnett, Ramona Britto, Hema Bye-A-Jee, Austra Cukura, Paul Denny 0002, Tunca Dogan, Thankgod Ebenezer, Penelope Garmiri, Leonardo Jose da Costa Gonzales, Emma Hatton-Ellis, Abdulrahman Hussein, Alexandr Ignatchenko, Giuseppe Insana, Rizwan Ishtiaq, Vishal Joshi, Dushyanth Jyothi, Swaathi Kandasamy, Antonia Lock, Aurelien Luciani, Marija Lugaric, Yvonne Lussi, Alistair MacDougall, Fábio Madeira, Mahdi Mahmoudy, Alok Mishra 0004, Katie Moulang, Andrew Nightingale, Sangya Pundir, Guoying Qi, Shriya Raj, Pedro Raposo, Daniel Rice, Rabie Saidi, Elena Speretta, James D. Stephenson, Prabhat Totoo, Edward Turner, Nidhi Tyagi, Preethi Vasudev, Kate Warner, Xavier Watkins, Rossana Zaru, Hermann Zellner, Cathy H. Wu, Cecilia N. Arighi, Leslie Arminski, Chuming Chen, Yongxing Chen, Hongzhan Huang, Kati Laiho, Peter B. McGarvey, Darren A. Natale, Karen E. Ross, C. R. Vinayaka, Qinghua Wang 0003
 Bioinform.16
2021 UniRule: a unified rule resource for automatic annotation in the UniProt Knowledgebase
Alistair MacDougall, Vladimir Volynkin, Rabie Saidi, Diego Poggioli, Hermann Zellner, Emma Hatton-Ellis, Vishal Joshi, Claire O'Donovan, Sandra E. Orchard, Andrea H. Auchincloss, Delphine Baratin, Jerven T. Bolleman, Elisabeth Coudert, Edouard De Castro, Chantal Hulo, Patrick Masson, Ivo Pedruzzi, Catherine Rivoire, Cecilia N. Arighi, Qinghua Wang 0003, Chuming Chen, Hongzhan Huang, John S. Garavelli, C. R. Vinayaka, Lai-Su Yeh, Darren A. Natale, Kati Laiho, Maria Jesus Martin, Alexandre Renaux, Klemens Pichler
 Bioinform.12
2020 UniRule: a unified rule resource for automatic annotation in the UniProt Knowledgebase
abstract
MOTIVATION: The number of protein records in the UniProt Knowledgebase (UniProtKB: https://www.uniprot.org) continues to grow rapidly as a result of genome sequencing and the prediction of protein-coding genes. Providing functional annotation for these proteins presents a significant and continuing challenge. RESULTS: In response to this challenge, UniProt has developed a method of annotation, known as UniRule, based on expertly curated rules, which integrates related systems (RuleBase, HAMAP, PIRSR, PIRNR) developed by the members of the UniProt consortium. UniRule uses protein family signatures from InterPro, combined with taxonomic and other constraints, to select sets of reviewed proteins which have common functional properties supported by experimental evidence. This annotation is propagated to unreviewed records in UniProtKB that meet the same selection criteria, most of which do not have (and are never likely to have) experimentally verified functional annotation. Release 2020_01 of UniProtKB contains 6496 UniRule rules which provide annotation for 53 million proteins, accounting for 30% of the 178 million records in UniProtKB. UniRule provides scalable enrichment of annotation in UniProtKB. AVAILABILITY AND IMPLEMENTATION: UniRule rules are integrated into UniProtKB and can be viewed at https://www.uniprot.org/unirule/. UniRule rules and the code required to run the rules, are publicly available for researchers who wish to annotate their own sequences. The implementation used to run the rules is known as UniFIRE and is available at https://gitlab.ebi.ac.uk/uniprot-public/unifire.
Alistair MacDougall, Vladimir Volynkin, Rabie Saidi, Diego Poggioli, Hermann Zellner, Emma Hatton-Ellis, Vishal Joshi, Claire O'Donovan, Sandra E. Orchard, Andrea H. Auchincloss, Delphine Baratin, Jerven T. Bolleman, Elisabeth Coudert, Edouard De Castro, Chantal Hulo, Patrick Masson, Ivo Pedruzzi, Catherine Rivoire, Cecilia N. Arighi, Qinghua Wang 0003, Chuming Chen, Hongzhan Huang, John S. Garavelli, C. R. Vinayaka, Lai-Su Yeh, Darren A. Natale, Kati Laiho, Maria Jesus Martin, Alexandre Renaux, Klemens Pichler
 Bioinform.12
2020 Enzyme annotation in UniProtKB using Rhea
abstract
MOTIVATION: To provide high quality computationally tractable enzyme annotation in UniProtKB using Rhea, a comprehensive expert-curated knowledgebase of biochemical reactions which describes reaction participants using the ChEBI (Chemical Entities of Biological Interest) ontology. RESULTS: We replaced existing textual descriptions of biochemical reactions in UniProtKB with their equivalents from Rhea, which is now the standard for annotation of enzymatic reactions in UniProtKB. We developed improved search and query facilities for the UniProt website, REST API and SPARQL endpoint that leverage the chemical structure data, nomenclature and classification that Rhea and ChEBI provide. AVAILABILITY AND IMPLEMENTATION: UniProtKB at https://www.uniprot.org; UniProt REST API at https://www.uniprot.org/help/api; UniProt SPARQL endpoint at https://sparql.uniprot.org/; Rhea at https://www.rhea-db.org.
Anne Morgat, Thierry Lombardot, Elisabeth Coudert, Kristian B. Axelsen, Teresa Batista Neto, Sebastien Gehant, Parit Bansal, Jerven T. Bolleman, Elisabeth Gasteiger, Edouard De Castro, Delphine Baratin, Monica Pozzato, Ioannis Xenarios, Sylvain Poux, Nicole Redaschi, Alan J. Bridge
 Bioinform.8
2015 SPARQL-enabled identifier conversion with Identifiers.org
abstract
MOTIVATION: On the semantic web, in life sciences in particular, data is often distributed via multiple resources. Each of these sources is likely to use their own International Resource Identifier for conceptually the same resource or database record. The lack of correspondence between identifiers introduces a barrier when executing federated SPARQL queries across life science data. RESULTS: We introduce a novel SPARQL-based service to enable on-the-fly integration of life science data. This service uses the identifier patterns defined in the Identifiers.org Registry to generate a plurality of identifier variants, which can then be used to match source identifiers with target identifiers. We demonstrate the utility of this identifier integration approach by answering queries across major producers of life science Linked Data. AVAILABILITY AND IMPLEMENTATION: The SPARQL-based identifier conversion service is available without restriction at http://identifiers.org/services/sparql.
Sarala M. Wimalaratne, Jerven T. Bolleman, Nick S. Juty, Toshiaki Katayama, Michel Dumontier, Nicole Redaschi, Nicolas Le Novère, Henning Hermjakob, Camille Laibe
 Bioinform.2
2014 The EBI RDF platform: linked open data for the life sciences
abstract
MOTIVATION: Resource description framework (RDF) is an emerging technology for describing, publishing and linking life science data. As a major provider of bioinformatics data and services, the European Bioinformatics Institute (EBI) is committed to making data readily accessible to the community in ways that meet existing demand. The EBI RDF platform has been developed to meet an increasing demand to coordinate RDF activities across the institute and provides a new entry point to querying and exploring integrated resources available at the EBI.
Simon Jupp, James Malone, Jerven T. Bolleman, Marco Brandizi, Mark Davies, Leyla Jael Castro, Anna Gaulton, Sebastien Gehant, Camille Laibe, Nicole Redaschi, Sarala M. Wimalaratne, Maria Jesus Martin, Nicolas Le Novère, Helen E. Parkinson, Ewan Birney, Andrew M. Jenkinson
 Bioinform.3