Alan J. Bridge

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EDBT 2026 Demo / reviewers in the wild / expert

dblp:83/4460 · DBLP profile ↗

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9ranked-venue papers

0as first author

1since 2021 · last 2023

0000-0003-2148-9135ORCID · verified

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

Applied, interdisciplinary, general and emerging computing · 8 · 1 since 2021Systems, architecture and hardware · 1

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 5 papers		Bioinformatics and computational biology · 100%
Computer architecture, parallel and distributed computing, and storage systems 1 paper		High-performance computing · 100%

Topics — the 6 heaviest of 7, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Bioinformatics and computational biology › protein analysis › protein bioinformatics protein annotation	1.1	2	2023	Annotation of biologically relevant ligands in UniProtKB using ChEBI · Bioinform. 2023 Enzyme annotation in UniProtKB using Rhea · Bioinform. 2020
Bioinformatics and computational biology bioinformatics infrastructure	0.4	1	2020	The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences · Bioinform. 2020
Bioinformatics and computational biology › protein function prediction enzyme function annotation	0.4	1	2020	Enzyme annotation in UniProtKB using Rhea · Bioinform. 2020
Bioinformatics and computational biology › sequence analysis › sequence profile analysis profile search	0.2	1	2013	pfsearchV3: a code acceleration and heuristic to search PROSITE profiles · Bioinform. 2013
Bioinformatics and computational biology › protein structure analysis protein domain identification	0.2	1	2013	pfsearchV3: a code acceleration and heuristic to search PROSITE profiles · Bioinform. 2013
High-performance computing performance optimization	0.0	1	2013	pfsearchV3: a code acceleration and heuristic to search PROSITE profiles · Bioinform. 2013

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

ontology-based annotation · 1.1SPARQL querying · 1.1profile hidden markov model · 0.3heuristic search · 0.3knowledge curation · 0.2hierarchical classification · 0.2

Year	Publication	Venue	Position
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.	9
2020	The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences abstract SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Rachel Drysdale, Charles E. Cook, Robert Petryszak, Vivienne Baillie Gerritsen, Mary Barlow, Elisabeth Gasteiger, Franziska Gruhl, Jerry Lanfear, Rodrigo Lopez, Nicole Redaschi, Heinz Stockinger, Daniel Teixeira, Aravind Venkatesan, Alex Bateman, Alan J. Bridge, Guy Cochrane, Robert D. Finn, Frank Oliver Glöckner, Marc Hanauer, Thomas M. Keane, Luana Licata, Per Oksvold, Sandra E. Orchard, Christine A. Orengo, Helen E. Parkinson, Bengt Persson, Pablo Porras, Jordi Rambla De Argila, Ana Rath, Charlotte Rodwell, Ugis Sarkans, Dietmar Schomburg, Ian Sillitoe, J. Dylan Spalding, Mathias Uhlen, Sameer Velankar, Juan Antonio Vizcaíno, Kalle von Feilitzen, Christian von Mering, Andy Yates, Niklas Blomberg, Christine Durinx, Johanna R. McEntyre	Bioinform.	16
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.	16
2018	Towards in the Field Fast Pathogens Detection Using FPGAs abstract The rapid detection and identification of infectious pathogens are a critical need for healthcare in both developed and developing countries. There is a need for portable diagnostics and epidemiological surveillance systems that can be deployed in remote field settings for infectious diseases testing, such as pandemic "hotspots" in Africa or southeast Asia. Together with the Vital-IT and Swiss-Prot groups, we developed a fast pathogen detection embedded hardware accelerator (no bigger than 75x50mm) capable of analyzing up to 250Mnt/s of DNA sequence. To deploy this solution, an embedded DNA Next Generation Sequencer (NGS) is used for retrieving the genetic material from patient's sample at the point of care (e.g., the Oxford Nanopore Technologies MinION). Successively, the collected DNA sequences are analized in order to detect the presence of known pathogen patterns. Currently, up to 100 different pathogens can be analized at the same time using one single embedded accelerator. Due to the low-power consumption of this hardware accelerator, it is possible to give access to worldwide health organizations and local administrations to a complete real-time embedded and battery powered surveillance solution. Moreover, this solution can greatly scale to big computing clusters, where a larger pathogens number can be analized in parallel, outperforming current state of art computationally expensive solutions. Simone Casale Brunet, Thierry Schüpbach, Nicolas Guex, Christian Iseli, Alan J. Bridge, Dmitry Kuznetsov, Christian J. A. Sigrist, Phillippe Lemercier, Ioannis Xenarios, Endri Bezati	FPL	5
2018	Scaling up data curation using deep learning: An application to literature triage in genomic variation resources abstract Manually curating biomedical knowledge from publications is necessary to build a knowledge based service that provides highly precise and organized information to users. The process of retrieving relevant publications for curation, which is also known as document triage, is usually carried out by querying and reading articles in PubMed. However, this query-based method often obtains unsatisfactory precision and recall on the retrieved results, and it is difficult to manually generate optimal queries. To address this, we propose a machine-learning assisted triage method. We collect previously curated publications from two databases UniProtKB/Swiss-Prot and the NHGRI-EBI GWAS Catalog, and used them as a gold-standard dataset for training deep learning models based on convolutional neural networks. We then use the trained models to classify and rank new publications for curation. For evaluation, we apply our method to the real-world manual curation process of UniProtKB/Swiss-Prot and the GWAS Catalog. We demonstrate that our machine-assisted triage method outperforms the current query-based triage methods, improves efficiency, and enriches curated content. Our method achieves a precision 1.81 and 2.99 times higher than that obtained by the current query-based triage methods of UniProtKB/Swiss-Prot and the GWAS Catalog, respectively, without compromising recall. In fact, our method retrieves many additional relevant publications that the query-based method of UniProtKB/Swiss-Prot could not find. As these results show, our machine learning-based method can make the triage process more efficient and is being implemented in production so that human curators can focus on more challenging tasks to improve the quality of knowledge bases. Kyubum Lee, Maria Livia Famiglietti, Aoife McMahon, Chih-Hsuan Wei, Jacqueline A. L. MacArthur, Sylvain Poux, Lionel Breuza, Alan J. Bridge, Fiona Cunningham, Ioannis Xenarios, Zhiyong Lu	PLoS Comput. Biol.	8
2015	The SwissLipids knowledgebase for lipid biology abstract MOTIVATION: Lipids are a large and diverse group of biological molecules with roles in membrane formation, energy storage and signaling. Cellular lipidomes may contain tens of thousands of structures, a staggering degree of complexity whose significance is not yet fully understood. High-throughput mass spectrometry-based platforms provide a means to study this complexity, but the interpretation of lipidomic data and its integration with prior knowledge of lipid biology suffers from a lack of appropriate tools to manage the data and extract knowledge from it. RESULTS: To facilitate the description and exploration of lipidomic data and its integration with prior biological knowledge, we have developed a knowledge resource for lipids and their biology-SwissLipids. SwissLipids provides curated knowledge of lipid structures and metabolism which is used to generate an in silico library of feasible lipid structures. These are arranged in a hierarchical classification that links mass spectrometry analytical outputs to all possible lipid structures, metabolic reactions and enzymes. SwissLipids provides a reference namespace for lipidomic data publication, data exploration and hypothesis generation. The current version of SwissLipids includes over 244 000 known and theoretically possible lipid structures, over 800 proteins, and curated links to published knowledge from over 620 peer-reviewed publications. We are continually updating the SwissLipids hierarchy with new lipid categories and new expert curated knowledge. AVAILABILITY: SwissLipids is freely available at http://www.swisslipids.org/. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Lucila Aimo, Robin Liechti, Nevila Hyka-Nouspikel, Anne Niknejad, Anne Gleizes, Lou Götz, Dmitry Kuznetsov, Fabrice P. A. David, F. Gisou van der Goot, Howard Riezman, Lydie Bougueleret, Ioannis Xenarios, Alan J. Bridge	Bioinform.	13
2014	Reconciliation of metabolites and biochemical reactions for metabolic networks abstract Genome-scale metabolic network reconstructions are now routinely used in the study of metabolic pathways, their evolution and design. The development of such reconstructions involves the integration of information on reactions and metabolites from the scientific literature as well as public databases and existing genome-scale metabolic models. The reconciliation of discrepancies between data from these sources generally requires significant manual curation, which constitutes a major obstacle in efforts to develop and apply genome-scale metabolic network reconstructions. In this work, we discuss some of the major difficulties encountered in the mapping and reconciliation of metabolic resources and review three recent initiatives that aim to accelerate this process, namely BKM-react, MetRxn and MNXref (presented in this article). Each of these resources provides a pre-compiled reconciliation of many of the most commonly used metabolic resources. By reducing the time required for manual curation of metabolite and reaction discrepancies, these resources aim to accelerate the development and application of high-quality genome-scale metabolic network reconstructions and models. Thomas Bernard, Alan J. Bridge, Anne Morgat, Sébastien Moretti, Ioannis Xenarios, Marco Pagni	Briefings Bioinform.	2
2013	pfsearchV3: a code acceleration and heuristic to search PROSITE profiles abstract SUMMARY: The PROSITE resource provides a rich and well annotated source of signatures in the form of generalized profiles that allow protein domain detection and functional annotation. One of the major limiting factors in the application of PROSITE in genome and metagenome annotation pipelines is the time required to search protein sequence databases for putative matches. We describe an improved and optimized implementation of the PROSITE search tool pfsearch that, combined with a newly developed heuristic, addresses this limitation. On a modern x86_64 hyper-threaded quad-core desktop computer, the new pfsearchV3 is two orders of magnitude faster than the original algorithm. AVAILABILITY AND IMPLEMENTATION: Source code and binaries of pfsearchV3 are freely available for download at http://web.expasy.org/pftools/#pfsearchV3, implemented in C and supported on Linux. PROSITE generalized profiles including the heuristic cut-off scores are available at the same address. Thierry Schüpbach, Marco Pagni, Alan J. Bridge, Lydie Bougueleret, Ioannis Xenarios, Lorenzo Cerutti	Bioinform.	3
2013	Application of text-mining for updating protein post-translational modification annotation in UniProtKB abstract BACKGROUND: The annotation of protein post-translational modifications (PTMs) is an important task of UniProtKB curators and, with continuing improvements in experimental methodology, an ever greater number of articles are being published on this topic. To help curators cope with this growing body of information we have developed a system which extracts information from the scientific literature for the most frequently annotated PTMs in UniProtKB. RESULTS: The procedure uses a pattern-matching and rule-based approach to extract sentences with information on the type and site of modification. A ranked list of protein candidates for the modification is also provided. For PTM extraction, precision varies from 57% to 94%, and recall from 75% to 95%, according to the type of modification. The procedure was used to track new publications on PTMs and to recover potential supporting evidence for phosphorylation sites annotated based on the results of large scale proteomics experiments. CONCLUSIONS: The information retrieval and extraction method we have developed in this study forms the basis of a simple tool for the manual curation of protein post-translational modifications in UniProtKB/Swiss-Prot. Our work demonstrates that even simple text-mining tools can be effectively adapted for database curation tasks, providing that a thorough understanding of the working process and requirements are first obtained. This system can be accessed at http://eagl.unige.ch/PTM/. Anne-Lise Veuthey, Alan J. Bridge, Julien Gobeill, Patrick Ruch, Johanna R. McEntyre, Lydie Bougueleret, Ioannis Xenarios	BMC Bioinform.	2