Fábio Madeira

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4ranked-venue papers

1as first author

1since 2021 · last 2023

0000-0001-8728-9449ORCID · verified

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

Applied, interdisciplinary, general and emerging computing · 4 · 1 first-author · 1 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 3 papers		Bioinformatics and computational biology · 100%
Computer architecture, parallel and distributed computing, and storage systems 1 paper		Cloud and datacenter computing · 100%

Topics — the 8 heaviest of 9, 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	0.7	1	2023	Annotation of biologically relevant ligands in UniProtKB using ChEBI · Bioinform. 2023
Bioinformatics and computational biology multiple sequence alignment	0.3	1	2018	JABAWS 2.2 distributed web services for Bioinformatics: protein disorder, conservation and RNA secondary structure · Bioinform. 2018
Bioinformatics and computational biology › protein structure prediction protein disorder prediction	0.3	1	2018	JABAWS 2.2 distributed web services for Bioinformatics: protein disorder, conservation and RNA secondary structure · Bioinform. 2018
Bioinformatics and computational biology protein structure prediction	0.3	1	2018	JABAWS 2.2 distributed web services for Bioinformatics: protein disorder, conservation and RNA secondary structure · Bioinform. 2018
Bioinformatics and computational biology › RNA biology › RNA analysis › RNA bioinformatics › RNA structure prediction RNA secondary structure prediction	0.3	1	2018	JABAWS 2.2 distributed web services for Bioinformatics: protein disorder, conservation and RNA secondary structure · Bioinform. 2018
Bioinformatics and computational biology sequence analysis	0.3	1	2018	JABAWS 2.2 distributed web services for Bioinformatics: protein disorder, conservation and RNA secondary structure · Bioinform. 2018
Cloud and datacenter computing › cluster resource management and scheduling cluster resource management	0.1	1	2018	JABAWS 2.2 distributed web services for Bioinformatics: protein disorder, conservation and RNA secondary structure · Bioinform. 2018
Cloud and datacenter computing job scheduling	0.1	1	2018	JABAWS 2.2 distributed web services for Bioinformatics: protein disorder, conservation and RNA secondary structure · Bioinform. 2018

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

web services · 0.7ontology-based annotation · 0.7SPARQL querying · 0.7support vector machine · 0.2position-specific scoring matrix · 0.2artificial neural network · 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.	79
2018	JABAWS 2.2 distributed web services for Bioinformatics: protein disorder, conservation and RNA secondary structure abstract Summary: JABAWS 2.2 is a computational framework that simplifies the deployment of web services for Bioinformatics. In addition to the five multiple sequence alignment (MSA) algorithms in JABAWS 1.0, JABAWS 2.2 includes three additional MSA programs (Clustal Omega, MSAprobs, GLprobs), four protein disorder prediction methods (DisEMBL, IUPred, Ronn, GlobPlot), 18 measures of protein conservation as implemented in AACon, and RNA secondary structure prediction by the RNAalifold program. JABAWS 2.2 can be deployed on a variety of in-house or hosted systems. JABAWS 2.2 web services may be accessed from the Jalview multiple sequence analysis workbench (Version 2.8 and later), as well as directly via the JABAWS command line interface (CLI) client. JABAWS 2.2 can be deployed on a local virtual server as a Virtual Appliance (VA) or simply as a Web Application Archive (WAR) for private use. Improvements in JABAWS 2.2 also include simplified installation and a range of utility tools for usage statistics collection, and web services querying and monitoring. The JABAWS CLI client has been updated to support all the new services and allow integration of JABAWS 2.2 services into conventional scripts. A public JABAWS 2 server has been in production since December 2011 and served over 800 000 analyses for users worldwide. Availability and implementation: JABAWS 2.2 is made freely available under the Apache 2 license and can be obtained from: http://www.compbio.dundee.ac.uk/jabaws. Contact: [email protected]. Peter V. Troshin, James B. Procter, Alexander Sherstnev, Daniel L. Barton, Fábio Madeira, Geoffrey J. Barton	Bioinform.	5
2015	14-3-3-Pred: improved methods to predict 14-3-3-binding phosphopeptides abstract MOTIVATION: The 14-3-3 family of phosphoprotein-binding proteins regulates many cellular processes by docking onto pairs of phosphorylated Ser and Thr residues in a constellation of intracellular targets. Therefore, there is a pressing need to develop new prediction methods that use an updated set of 14-3-3-binding motifs for the identification of new 14-3-3 targets and to prioritize the downstream analysis of >2000 potential interactors identified in high-throughput experiments. RESULTS: Here, a comprehensive set of 14-3-3-binding targets from the literature was used to develop 14-3-3-binding phosphosite predictors. Position-specific scoring matrix, support vector machines (SVM) and artificial neural network (ANN) classification methods were trained to discriminate experimentally determined 14-3-3-binding motifs from non-binding phosphopeptides. ANN, position-specific scoring matrix and SVM methods showed best performance for a motif window spanning from -6 to +4 around the binding phosphosite, achieving Matthews correlation coefficient of up to 0.60. Blind prediction showed that all three methods outperform two popular 14-3-3-binding site predictors, Scansite and ELM. The new methods were used for prediction of 14-3-3-binding phosphosites in the human proteome. Experimental analysis of high-scoring predictions in the FAM122A and FAM122B proteins confirms the predictions and suggests the new 14-3-3-predictors will be generally useful. AVAILABILITY AND IMPLEMENTATION: A standalone prediction web server is available at http://www.compbio.dundee.ac.uk/1433pred. Human candidate 14-3-3-binding phosphosites were integrated in ANIA: ANnotation and Integrated Analysis of the 14-3-3 interactome database. Fábio Madeira, Michele Tinti, Gavuthami Murugesan, Emily Berrett, Margaret Stafford, Rachel Toth, Christian Cole, Carol MacKintosh, Geoffrey J. Barton	Bioinform.	1
2013	Constraining Protein Docking with Coevolution Data for Medical Research Ludwig Krippahl, Fábio Madeira, Pedro Barahona	AIME	2