Open-source large language models in action: A bioinformatics chatbot for PRIDE database.

Bai J, Kamatchinathan S, Kundu DJ, Bandla C, Vizcaíno JA, Perez-Riverol Y.

Proteomics, 2024

doi:10.1002/pmic.202400005.

Open Source Large Language Models in Action: A Bioinformatics Chatbot for PRIDE database

Bai J, Kamatchinathan S, Kundu DJ, Bandla C, Vizcaino JA, Riverol YP.

2024

doi:10.22541/au.171025539.92037103/v1.

OpenMS 3 enables reproducible analysis of large-scale mass spectrometry data.

Pfeuffer J, Bielow C, Wein S, Jeong K, Netz E, Walter A, Alka O, Nilse L, Colaianni PD, McCloskey D, Kim J, Rosenberger G, Bichmann L, Walzer M, Veit J, Boudaud B, Bernt M, Patikas N, Pilz M, Startek MP, Kutuzova S, Heumos L, Charkow J, Sing JC, Feroz A, Siraj A, Weisser H, Dijkstra TMH, Perez-Riverol Y, Röst H, Kohlbacher O, Sachsenberg T.

Nat Methods, 2024

doi:10.1038/s41592-024-02197-7.

Mass spectrometry-based proteomics data from thousands of HeLa control samples.

Webel H, Perez-Riverol Y, Nielsen AB, Rasmussen S.

Sci Data, 2024

doi:10.1038/s41597-024-02922-z.

EMBL's European Bioinformatics Institute (EMBL-EBI) in 2023.

Thakur M, Buniello A, Brooksbank C, Gurwitz KT, Hall M, Hartley M, Hulcoop DG, Leach AR, Marques D, Martin M, Mithani A, McDonagh EM, Mutasa-Gottgens E, Ochoa D, Perez-Riverol Y, Stephenson J, Varadi M, Velankar S, Vizcaino JA, Witham R, McEntyre J.

Nucleic Acids Res, 2024

doi:10.1093/nar/gkad1088.

Assessing the contribution of rare variants to congenital heart disease through a large-scale case-control exome study

Audain E, Wilsdon A, Dombrowsky G, Sifrim A, Breckpot J, Perez-Riverol Y, Loughna S, Daly A, Antoniou P, Hofmann P, Perez-Riverol A, Kahlert A, Bauer U, Pickardt T, Klaassen S, Berger F, Daehnert I, Dittrich S, Stiller B, Abdul-Khaliq H, Bu’lock F, Uebing A, Kramer H, Iyer V, Larsen LA, Brook JD, Hitz M.

2023

doi:10.1101/2023.12.23.23300495.

Phosphorylation in the<i>Plasmodium falciparum</i>proteome: A meta-analysis of publicly available data sets

Camacho OJM, Ramsbottom KA, Prakash A, Sun Z, Riverol YP, Bowler-Barnett E, Martin M, Fan J, Deutsch EW, Vizcaíno JA, Jones AR.

2023

doi:10.1101/2023.11.20.567785.

The Association of Biomolecular Resource Facilities Proteome Informatics Research Group Study on Metaproteomics (iPRG-2020).

Jagtap PD, Hoopmann MR, Neely BA, Harvey A, Käll L, Perez-Riverol Y, Abajorga MK, Thomas JA, Weintraub ST, Palmblad M.

J Biomol Tech, 2023

doi:10.7171/3fc1f5fe.a058bad4.

Mass spectrometry-based proteomics data from thousands of HeLa control samples

Webel H, Perez-Riverol Y, Nielson AB, Rasmussen S.

2023

doi:10.21203/rs.3.rs-3083547/v2.

lesSDRF is more: maximizing the value of proteomics data through streamlined metadata annotation.

Claeys T, Van Den Bossche T, Perez-Riverol Y, Gevaert K, Vizcaíno JA, Martens L.

Nat Commun, 2023

doi:10.1038/s41467-023-42543-5.

OpenMS 3 expands the frontiers of open-source computational mass spectrometry

Sachsenberg T, Pfeuffer J, Bielow C, Wein S, Jeong K, Netz E, Walter A, Alka O, Nilse L, Colaianni P, McCloskey D, Kim J, Rosenberger G, Bichmann L, Walzer M, Veit J, Boudaud B, Bernt M, Patikas N, Pilz M, Startek MP, Kutuzova S, Heumos L, Charkow J, Sing J, Feroz A, Siraj A, Weisser H, Dijkstra T, Perez-Riverol Y, Röst H, Kohlbacher O.

2023

doi:10.21203/rs.3.rs-3286368/v1.

Tissue-based absolute quantification using large-scale TMT and LFQ experiments.

Wang H, Dai C, Pfeuffer J, Sachsenberg T, Sanchez A, Bai M, Perez-Riverol Y.

Proteomics, 2023

doi:10.1002/pmic.202300188.

quantms: A cloud-based pipeline for proteomics reanalysis enables the quantification of 17521 proteins in 9,502 human samples.

Dai C, Pfeuffer J, Wang H, Sachsenberg T, Demichev V, Kohlbacher O, Perez-Riverol Y.

2023

doi:10.21203/rs.3.rs-3002027/v1.

lesSDRF Is More: Maximizing The Value Of Proteomics Data Through Streamlined Metadata Annotation

Claeys T, Bossche TVD, Perez-Riverol Y, Gevaert K, Vizcaino JA, Martens L.

2023

doi:10.21203/rs.3.rs-2937726/v1.

LFQ-Based Peptide and Protein Intensity Differential Expression Analysis.

Bai M, Deng J, Dai C, Pfeuffer J, Sachsenberg T, Perez-Riverol Y.

J Proteome Res, 2023

doi:10.1021/acs.jproteome.2c00812.

Tissue-based absolute quantification using large-scale TMT and LFQ experiments

Wang H, Dai C, Pfeuffer J, Sachsenberg T, Sanchez A, Bai M, Riverol YP.

2023

doi:10.22541/au.168174437.77664121/v1.

ProteomicsML: An Online Platform for Community-Curated Data sets and Tutorials for Machine Learning in Proteomics.

Rehfeldt TG, Gabriels R, Bouwmeester R, Gessulat S, Neely BA, Palmblad M, Perez-Riverol Y, Schmidt T, Vizcaíno JA, Deutsch EW.

J Proteome Res, 2023

doi:10.1021/acs.jproteome.2c00629.

Proteomics Standards Initiative at Twenty Years: Current Activities and Future Work.

Deutsch EW, Vizcaíno JA, Jones AR, Binz PA, Lam H, Klein J, Bittremieux W, Perez-Riverol Y, Tabb DL, Walzer M, Ricard-Blum S, Hermjakob H, Neumann S, Mak TD, Kawano S, Mendoza L, Van Den Bossche T, Gabriels R, Bandeira N, Carver J, Pullman B, Sun Z, Hoffmann N, Shofstahl J, Zhu Y, Licata L, Quaglia F, Tosatto SCE, Orchard SE.

J Proteome Res, 2023

doi:10.1021/acs.jproteome.2c00637.

The ProteomeXchange consortium at 10 years: 2023 update.

Deutsch EW, Bandeira N, Perez-Riverol Y, Sharma V, Carver JJ, Mendoza L, Kundu DJ, Wang S, Bandla C, Kamatchinathan S, Hewapathirana S, Pullman BS, Wertz J, Sun Z, Kawano S, Okuda S, Watanabe Y, MacLean B, MacCoss MJ, Zhu Y, Ishihama Y, Vizcaíno JA.

Nucleic Acids Res, 2023

doi:10.1093/nar/gkac1040.

Proteomic repository data submission, dissemination, and reuse: key messages.

Perez-Riverol Y.

Expert Rev Proteomics, 2022

doi:10.1080/14789450.2022.2160324.

Method for Independent Estimation of the False Localization Rate for Phosphoproteomics.

Ramsbottom KA, Prakash A, Riverol YP, Camacho OM, Martin MJ, Vizcaíno JA, Deutsch EW, Jones AR.

J Proteome Res, 2022

doi:10.1021/acs.jproteome.1c00827.

A Comprehensive Evaluation of Consensus Spectrum Generation Methods in Proteomics.

Luo X, Bittremieux W, Griss J, Deutsch EW, Sachsenberg T, Levitsky LI, Ivanov MV, Bubis JA, Gabriels R, Webel H, Sanchez A, Bai M, Käll L, Perez-Riverol Y.

J Proteome Res, 2022

doi:10.1021/acs.jproteome.2c00069.

A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics.

Van Puyvelde B, Daled S, Willems S, Gabriels R, Gonzalez de Peredo A, Chaoui K, Mouton-Barbosa E, Bouyssié D, Boonen K, Hughes CJ, Gethings LA, Perez-Riverol Y, Bloomfield N, Tate S, Schiltz O, Martens L, Deforce D, Dhaenens M.

Sci Data, 2022

doi:10.1038/s41597-022-01216-6.

Proteomics Standards Initiative's ProForma 2.0: Unifying the Encoding of Proteoforms and Peptidoforms.

LeDuc RD, Deutsch EW, Binz PA, Fellers RT, Cesnik AJ, Klein JA, Van Den Bossche T, Gabriels R, Yalavarthi A, Perez-Riverol Y, Carver J, Bittremieux W, Kawano S, Pullman B, Bandeira N, Kelleher NL, Thomas PM, Vizcaíno JA.

J Proteome Res, 2022

doi:10.1021/acs.jproteome.1c00771.

A comprehensive evaluation of consensus spectrum generation methods in proteomics

Luo X, Bittremieux W, Griss J, Deutsch EW, Sachsenberg T, Levitsky LI, Ivanov MV, Bubis JA, Gabriels R, Webel H, Sanchez A, Bai M, Kall L, Perez-Riverol Y.

2022

doi:10.1101/2022.01.25.477699.

Generation of ENSEMBL-based proteogenomics databases boosts the identification of non-canonical peptides.

Umer HM, Audain E, Zhu Y, Pfeuffer J, Sachsenberg T, Lehtiö J, Branca RM, Perez-Riverol Y.

Bioinformatics, 2022

doi:10.1093/bioinformatics/btab838.

The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences.

Perez-Riverol Y, Bai J, Bandla C, García-Seisdedos D, Hewapathirana S, Kamatchinathan S, Kundu DJ, Prakash A, Frericks-Zipper A, Eisenacher M, Walzer M, Wang S, Brazma A, Vizcaíno JA.

Nucleic Acids Res, 2022

doi:10.1093/nar/gkab1038.

Mapping the Melanoma Plasma Proteome (MPP) Using Single-Shot Proteomics Interfaced with the WiMT Database.

Almeida N, Rodriguez J, Pla Parada I, Perez-Riverol Y, Woldmar N, Kim Y, Oskolas H, Betancourt L, Valdés JG, Sahlin KB, Pizzatti L, Szasz AM, Kárpáti S, Appelqvist R, Malm J, B Domont G, C S Nogueira F, Marko-Varga G, Sanchez A.

Cancers (Basel), 2021

doi:10.3390/cancers13246224.

A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics

Van Puyvelde B, Daled S, Willems S, Gabriels R, de Peredo AG, Chaoui K, Mouton-Barbosa E, Bouyssié D, Boonen K, Hughes CJ, Gethings LA, Perez-Riverol Y, Bloomfield N, Tate S, Schiltz O, Martens L, Deforce D, Dhaenens M.

2021

doi:10.1101/2021.11.24.469852.

A method for independent estimation of false localisation rate for phosphoproteomics

Ramsbottom KA, Prakash A, Riverol YP, Camacho OM, Martin M, Vizcaíno JA, Deutsch EW, Jones AR.

2021

doi:10.1101/2021.10.18.464791.

A proteomics sample metadata representation for multiomics integration and big data analysis.

Dai C, Füllgrabe A, Pfeuffer J, Solovyeva EM, Deng J, Moreno P, Kamatchinathan S, Kundu DJ, George N, Fexova S, Grüning B, Föll MC, Griss J, Vaudel M, Audain E, Locard-Paulet M, Turewicz M, Eisenacher M, Uszkoreit J, Van Den Bossche T, Schwämmle V, Webel H, Schulze S, Bouyssié D, Jayaram S, Duggineni VK, Samaras P, Wilhelm M, Choi M, Wang M, Kohlbacher O, Brazma A, Papatheodorou I, Bandeira N, Deutsch EW, Vizcaíno JA, Bai M, Sachsenberg T, Levitsky LI, Perez-Riverol Y.

Nat Commun, 2021

doi:10.1038/s41467-021-26111-3.

Correction: Integrative analysis of genomic variants reveals new associations of candidate haploinsufficient genes with congenital heart disease.

Audain E, Wilsdon A, Breckpot J, Izarzugaza JMG, Fitzgerald TW, Kahlert AK, Sifrim A, Wünnemann F, Perez-Riverol Y, Abdul-Khaliq H, Bak M, Bassett AS, Benson DW, Berger F, Daehnert I, Devriendt K, Dittrich S, Daubeney PE, Garg V, Hackmann K, Hoff K, Hofmann P, Dombrowsky G, Pickardt T, Bauer U, Keavney BD, Klaassen S, Kramer HH, Marshall CR, Milewicz DM, Lemaire S, Coselli JS, Mitchell ME, Tomita-Mitchell A, Prakash SK, Stamm K, Stewart AFR, Silversides CK, Siebert R, Stiller B, Rosenfeld JA, Vater I, Postma AV, Caliebe A, Brook JD, Andelfinger G, Hurles ME, Thienpont B, Larsen LA, Hitz MP.

PLoS Genet, 2021

doi:10.1371/journal.pgen.1009809.

Integrative analysis of genomic variants reveals new associations of candidate haploinsufficient genes with congenital heart disease.

Audain E, Wilsdon A, Breckpot J, Izarzugaza JMG, Fitzgerald TW, Kahlert AK, Sifrim A, Wünnemann F, Perez-Riverol Y, Abdul-Khaliq H, Bak M, Bassett AS, Benson DW, Berger F, Daehnert I, Devriendt K, Dittrich S, Daubeney PE, Garg V, Hackmann K, Hoff K, Hofmann P, Dombrowsky G, Pickardt T, Bauer U, Keavney BD, Klaassen S, Kramer HH, Marshall CR, Milewicz DM, Lemaire S, Coselli JS, Mitchell ME, Tomita-Mitchell A, Prakash SK, Stamm K, Stewart AFR, Silversides CK, Siebert R, Stiller B, Rosenfeld JA, Vater I, Postma AV, Caliebe A, Brook JD, Andelfinger G, Hurles ME, Thienpont B, Larsen LA, Hitz MP.

PLoS Genet, 2021

doi:10.1371/journal.pgen.1009679.

MaxDIA enables library-based and library-free data-independent acquisition proteomics.

Sinitcyn P, Hamzeiy H, Salinas Soto F, Itzhak D, McCarthy F, Wichmann C, Steger M, Ohmayer U, Distler U, Kaspar-Schoenefeld S, Prianichnikov N, Yılmaz Ş, Rudolph JD, Tenzer S, Perez-Riverol Y, Nagaraj N, Humphrey SJ, Cox J.

Nat Biotechnol, 2021

doi:10.1038/s41587-021-00968-7.

Universal Spectrum Identifier for mass spectra.

Deutsch EW, Perez-Riverol Y, Carver J, Kawano S, Mendoza L, Van Den Bossche T, Gabriels R, Binz PA, Pullman B, Sun Z, Shofstahl J, Bittremieux W, Mak TD, Klein J, Zhu Y, Lam H, Vizcaíno JA, Bandeira N.

Nat Methods, 2021

doi:10.1038/s41592-021-01184-6.

Generation of ENSEMBL-based proteogenomics databases boosts the identification of non-canonical peptides

Umer HM, Zhu Y, Pfeuffer J, Sachsenberg T, Lehtiö J, Branca R, Perez-Riverol Y.

2021

doi:10.1101/2021.06.08.447496.

A proteomics sample metadata representation for multiomics integration, and big data analysis

Dai C, Füllgrabe A, Pfeuffer J, Solovyeva E, Deng J, Moreno P, Kamatchinathan S, Kundu DJ, George N, Fexova S, Grüning B, Föll MC, Griss J, Vaudel M, Audain E, Locard-Paulet M, Turewicz M, Eisenacher M, Uszkoreit J, Van Den Bossche T, Schwämmle V, Webel H, Schulze S, Bouyssié D, Jayaram S, Duggineni VK, Samaras P, Wilhelm M, Choi M, Wang M, Kohlbacher O, Brazma A, Papatheodorou I, Bandeira N, Deutsch EW, Vizcaíno JA, Bai M, Sachsenberg T, Levitsky L, Perez-Riverol Y.

2021

doi:10.1101/2021.05.21.445143.

Universal Spectrum Explorer: A Standalone (Web-)Application for Cross-Resource Spectrum Comparison.

Schmidt T, Samaras P, Dorfer V, Panse C, Kockmann T, Bichmann L, van Puyvelde B, Perez-Riverol Y, Deutsch EW, Kuster B, Wilhelm M.

J Proteome Res, 2021

doi:10.1021/acs.jproteome.1c00096.

An integrated landscape of protein expression in human cancer.

Jarnuczak AF, Najgebauer H, Barzine M, Kundu DJ, Ghavidel F, Perez-Riverol Y, Papatheodorou I, Brazma A, Vizcaíno JA.

Sci Data, 2021

doi:10.1038/s41597-021-00890-2.

The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS): an open community for bioinformatics training and research.

Bittremieux W, Bouyssié D, Dorfer V, Locard-Paulet M, Perez-Riverol Y, Schwämmle V, Uszkoreit J, Van Den Bossche T.

Rapid Commun Mass Spectrom, 2021

doi:10.1002/rcm.9087.

User-friendly, scalable tools and workflows for single-cell RNA-seq analysis.

Moreno P, Huang N, Manning JR, Mohammed S, Solovyev A, Polanski K, Bacon W, Chazarra R, Talavera-López C, Doyle MA, Marnier G, Grüning B, Rasche H, George N, Fexova SK, Alibi M, Miao Z, Perez-Riverol Y, Haeussler M, Brazma A, Teichmann S, Meyer KB, Papatheodorou I.

Nat Methods, 2021

doi:10.1038/s41592-021-01102-w.

BioContainers Registry: Searching Bioinformatics and Proteomics Tools, Packages, and Containers.

Bai J, Bandla C, Guo J, Vera Alvarez R, Bai M, Vizcaíno JA, Moreno P, Grüning B, Sallou O, Perez-Riverol Y.

J Proteome Res, 2021

doi:10.1021/acs.jproteome.0c00904.

Deep learning embedder method and tool for mass spectra similarity search.

Qin C, Luo X, Deng C, Shu K, Zhu W, Griss J, Hermjakob H, Bai M, Perez-Riverol Y.

J Proteomics, 2021

doi:10.1016/j.jprot.2020.104070.

Universal Spectrum Identifier for mass spectra

Deutsch EW, Perez-Riverol Y, Carver J, Kawano S, Mendoza L, Van Den Bossche T, Gabriels R, Binz P, Pullman B, Sun Z, Shofstahl J, Bittremieux W, Mak TD, Klein J, Zhu Y, Lam H, Vizcaíno JA, Bandeira N.

2020

doi:10.1101/2020.12.07.415539.

MassIVE.quant: a community resource of quantitative mass spectrometry-based proteomics datasets.

Choi M, Carver J, Chiva C, Tzouros M, Huang T, Tsai TH, Pullman B, Bernhardt OM, Hüttenhain R, Teo GC, Perez-Riverol Y, Muntel J, Müller M, Goetze S, Pavlou M, Verschueren E, Wollscheid B, Nesvizhskii AI, Reiter L, Dunkley T, Sabidó E, Bandeira N, Vitek O.

Nat Methods, 2020

doi:10.1038/s41592-020-0955-0.

Universal Spectrum Explorer: A standalone (web-)application for cross-resource spectrum comparison

Schmidt T, Samaras P, Dorfer V, Panse C, Kockmann T, Bichmann L, van Puyvelde B, Perez-Riverol Y, Deutsch EW, Kuster B, Wilhelm M.

2020

doi:10.1101/2020.09.08.287557.

Toward a Sample Metadata Standard in Public Proteomics Repositories.

Perez-Riverol Y, European Bioinformatics Community for Mass Spectrometry.

J Proteome Res, 2020

doi:10.1021/acs.jproteome.0c00376.

BioContainers Registry: searching for bioinformatics tools, packages and containers

Bai J, Bandla C, Guo J, Alvarez RV, Vizcaíno JA, Bai M, Moreno P, Grüning BA, Sallou O, Perez-Riverol Y.

2020

doi:10.1101/2020.07.21.187609.

Integrative analysis of genomic variants reveals new associations of candidate haploinsufficient genes with congenital heart disease

Audain E, Wilsdon A, Breckpot J, Izarzugaza J, Fitzgerald T, Kahlert A, Sifrim A, Wünnemann F, Perez-Riverol Y, Abdul-Khaliq H, Bak M, Bassett A, Belmont J, Benson D, Berger F, Daehnert I, Devriendt K, Dittrich S, Daubeney P, Garg V, Hackmann K, Hoff K, Hofmann P, Dombrowsky G, Pickardt T, Bauer U, Keavney B, Klaassen S, Kramer H, Marshall C, Milewicz D, Lemaire S, Coselli J, Mitchell M, Tomita-Mitchell A, Prakash S, Stamm K, Stewart A, Silversides C, Siebert R, Stiller B, Rosenfeld J, Vater I, Postma A, Caliebe A, Brook J, Andelfinger G, Hurles M, Thienpont B, Larsen L, Hitz M.

2020

doi:10.1101/2020.06.25.169573.

The omics discovery REST interface.

Dass G, Vu MT, Xu P, Audain E, Hitz MP, Grüning BA, Hermjakob H, Perez-Riverol Y.

Nucleic Acids Res, 2020

doi:10.1093/nar/gkaa326.

User-friendly, scalable tools and workflows for single-cell analysis

Moreno P, Huang N, Manning J, Mohammed S, Solovyev A, Polanski K, Chazarra R, Talavera-Lopez C, Doyle M, Marnier G, Grüning B, Rasche H, Bacon W, Perez-Riverol Y, Haeussler M, Meyer K, Teichmann S, Papatheodorou I.

2020

doi:10.1101/2020.04.08.032698.

The Omics Discovery REST interface

Dass G, Vu M, Xu P, Audain E, Hitz M, Hermjakob H, Perez-Riverol Y.

2020

doi:10.1101/2020.02.10.939967.

ThermoRawFileParser: Modular, Scalable, and Cross-Platform RAW File Conversion.

Hulstaert N, Shofstahl J, Sachsenberg T, Walzer M, Barsnes H, Martens L, Perez-Riverol Y.

J Proteome Res, 2020

doi:10.1021/acs.jproteome.9b00328.

The ProteomeXchange consortium in 2020: enabling 'big data' approaches in proteomics.

Deutsch EW, Bandeira N, Sharma V, Perez-Riverol Y, Carver JJ, Kundu DJ, García-Seisdedos D, Jarnuczak AF, Hewapathirana S, Pullman BS, Wertz J, Sun Z, Kawano S, Okuda S, Watanabe Y, Hermjakob H, MacLean B, MacCoss MJ, Zhu Y, Ishihama Y, Vizcaíno JA.

Nucleic Acids Res, 2020

doi:10.1093/nar/gkz984.

Scalable Data Analysis in Proteomics and Metabolomics Using BioContainers and Workflows Engines.

Perez-Riverol Y, Moreno P.

Proteomics, 2020

doi:10.1002/pmic.201900147.

Novel functional proteins coded by the human genome discovered in metastases of melanoma patients.

Sanchez A, Kuras M, Murillo JR, Pla I, Pawlowski K, Szasz AM, Gil J, Nogueira FCS, Perez-Riverol Y, Eriksson J, Appelqvist R, Miliotis T, Kim Y, Baldetorp B, Ingvar C, Olsson H, Lundgren L, Ekedahl H, Horvatovich P, Sugihara Y, Welinder C, Wieslander E, Kwon HJ, Domont GB, Malm J, Rezeli M, Betancourt LH, Marko-Varga G.

Cell Biol Toxicol, 2020

doi:10.1007/s10565-019-09494-4.

Phoenix Enhancer: proteomics data mining using clustered spectra

Bai M, Qin C, Shu K, Griss J, Perez-Riverol Y, Zhu W, Hermjakob H.

2019

doi:10.1101/846303.

Sodium dodecyl sulfate free gel electrophoresis/electroelution sorting for peptide fractionation.

Ramos Y, González A, Sosa-Acosta P, Perez-Riverol Y, García Y, Castellanos-Serra L, Gil J, Sánchez A, González LJ, Besada V.

J Sep Sci, 2019

doi:10.1002/jssc.201900495.

BioHackathon series in 2013 and 2014: improvements of semantic interoperability in life science data and services

Katayama T, Kawashima S, Micklem G, Kawano S, Kim J, Kocbek S, Okamoto S, Wang Y, Wu H, Yamaguchi A, Yamamoto Y, Antezana E, Aoki-Kinoshita KF, Arakawa K, Banno M, Baran J, Bolleman JT, Bonnal RJP, Bono H, Fernández-Breis JT, Buels R, Campbell MP, Chiba H, Cock PJA, Cohen KB, Dumontier M, Fujisawa T, Fujiwara T, Garcia L, Gaudet P, Hattori E, Hoehndorf R, Itaya K, Ito M, Jamieson D, Jupp S, Juty N, Kalderimis A, Kato F, Kawaji H, Kawashima T, Kinjo AR, Komiyama Y, Kotera M, Kushida T, Malone J, Matsubara M, Mizuno S, Mizutani S, Mori H, Moriya Y, Murakami K, Nakazato T, Nishide H, Nishimura Y, Ogishima S, Ohta T, Okuda S, Ono H, Perez-Riverol Y, Shinmachi D, Splendiani A, Strozzi F, Suzuki S, Takehara J, Thompson M, Tokimatsu T, Uchiyama I, Verspoor K, Wilkinson MD, Wimalaratne S, Yamada I, Yamamoto N, Yarimizu M, Kawamoto S, Takagi T.

2019

doi:10.12688/f1000research.18238.1.

Quantifying the impact of public omics data.

Perez-Riverol Y, Zorin A, Dass G, Vu MT, Xu P, Glont M, Vizcaíno JA, Jarnuczak AF, Petryszak R, Ping P, Hermjakob H.

Nat Commun, 2019

doi:10.1038/s41467-019-11461-w.

Correction: Ten Simple Rules for Taking Advantage of Git and GitHub.

PLOS Computational Biology Staff.

PLoS Comput Biol, 2019

doi:10.1371/journal.pcbi.1007142.

An integrated landscape of protein expression in human cancer

Jarnuczak AF, Najgebauer H, Barzine M, Kundu DJ, Ghavidel F, Perez-Riverol Y, Papatheodorou I, Brazma A, Vizcaíno JA.

2019

doi:10.1101/665968.

Proteomics Standards Initiative Extended FASTA Format.

Binz PA, Shofstahl J, Vizcaíno JA, Barsnes H, Chalkley RJ, Menschaert G, Alpi E, Clauser K, Eng JK, Lane L, Seymour SL, Sánchez LFH, Mayer G, Eisenacher M, Perez-Riverol Y, Kapp EA, Mendoza L, Baker PR, Collins A, Van Den Bossche T, Deutsch EW.

J Proteome Res, 2019

doi:10.1021/acs.jproteome.9b00064.

Proteomics Standards Initiative Extended FASTA Format (PEFF)

Binz P, Shofstahl J, Vizcaíno JA, Barsnes H, Chalkley RJ, Menschaert G, Alpi E, Clauser K, Eng JK, Lane L, Seymour SL, Sánchez LFH, Mayer G, Eisenacher M, Perez-Riverol Y, Kapp EA, Mendoza L, Baker PR, Collins A, Van Den Bossche T, Deutsch EW.

2019

doi:10.1101/624494.

ThermoRawFileParser: modular, scalable and cross-platform RAW file conversion

Hulstaert N, Sachsenberg T, Walzer M, Barsnes H, Martens L, Perez-Riverol Y.

2019

doi:10.1101/622852.

Scalable data analysis in proteomics and metabolomics using BioContainers and workflows engines

Perez-Riverol Y, Moreno P.

2019

doi:10.1101/604413.

Recommendations for the packaging and containerizing of bioinformatics software

Gruening B, Sallou O, Moreno P, da Veiga Leprevost F, Ménager H, Søndergaard D, Röst H, Sachsenberg T, O'Connor B, Madeira F, Dominguez Del Angel V, Crusoe MR, Varma S, Blankenberg D, Jimenez RC, Perez-Riverol Y, BioContainers Community.

2019

doi:10.12688/f1000research.15140.2.

Spectral Clustering Improves Label-Free Quantification of Low-Abundant Proteins.

Griss J, Stanek F, Hudecz O, Dürnberger G, Perez-Riverol Y, Vizcaíno JA, Mechtler K.

J Proteome Res, 2019

doi:10.1021/acs.jproteome.8b00377.

mzTab-M: A Data Standard for Sharing Quantitative Results in Mass Spectrometry Metabolomics.

Hoffmann N, Rein J, Sachsenberg T, Hartler J, Haug K, Mayer G, Alka O, Dayalan S, Pearce JTM, Rocca-Serra P, Qi D, Eisenacher M, Perez-Riverol Y, Vizcaíno JA, Salek RM, Neumann S, Jones AR.

Anal Chem, 2019

doi:10.1021/acs.analchem.8b04310.

Protein Inference Using PIA Workflows and PSI Standard File Formats.

Uszkoreit J, Perez-Riverol Y, Eggers B, Marcus K, Eisenacher M.

J Proteome Res, 2019

doi:10.1021/acs.jproteome.8b00723.

The PRIDE database and related tools and resources in 2019: improving support for quantification data.

Perez-Riverol Y, Csordas A, Bai J, Bernal-Llinares M, Hewapathirana S, Kundu DJ, Inuganti A, Griss J, Mayer G, Eisenacher M, Pérez E, Uszkoreit J, Pfeuffer J, Sachsenberg T, Yilmaz S, Tiwary S, Cox J, Audain E, Walzer M, Jarnuczak AF, Ternent T, Brazma A, Vizcaíno JA.

Nucleic Acids Res, 2019

doi:10.1093/nar/gky1106.

An "on-matrix" digestion procedure for AP-MS experiments dissects the interplay between complex-conserved and serotype-specific reactivities in Dengue virus-human plasma interactome.

Ramos Y, Huerta V, Martín D, Palomares S, Yero A, Pupo D, Gallien S, Martín AM, Pérez-Riverol Y, Sarría M, Guirola O, Chinea G, Domon B, González LJ.

J Proteomics, 2019

doi:10.1016/j.jprot.2017.07.004.

Recommendations for the packaging and containerizing of bioinformatics software.

Gruening B, Sallou O, Moreno P, da Veiga Leprevost F, Ménager H, Søndergaard D, Röst H, Sachsenberg T, O'Connor B, Madeira F, Dominguez Del Angel V, Crusoe MR, Varma S, Blankenberg D, Jimenez RC, BioContainers Community, Perez-Riverol Y.

F1000Res, 2018

doi:10.12688/f1000research.15140.2.

Galaxy-Kubernetes integration: scaling bioinformatics workflows in the cloud

Moreno P, Pireddu L, Roger P, Goonasekera N, Afgan E, van den Beek M, He S, Larsson A, Schober D, Ruttkies C, Johnson D, Rocca-Serra P, Weber RJ, Gruening B, Salek RM, Kale N, Perez-Riverol Y, Papatheodorou I, Spjuth O, Neumann S.

2018

doi:10.1101/488643.

Expanding the Use of Spectral Libraries in Proteomics.

Deutsch EW, Perez-Riverol Y, Chalkley RJ, Wilhelm M, Tate S, Sachsenberg T, Walzer M, Käll L, Delanghe B, Böcker S, Schymanski EL, Wilmes P, Dorfer V, Kuster B, Volders PJ, Jehmlich N, Vissers JPC, Wolan DW, Wang AY, Mendoza L, Shofstahl J, Dowsey AW, Griss J, Salek RM, Neumann S, Binz PA, Lam H, Vizcaíno JA, Bandeira N, Röst H.

J Proteome Res, 2018

doi:10.1021/acs.jproteome.8b00485.

Protein inference using PIA workflows and PSI standard file formats

Uszkoreit J, Perez-Riverol Y, Eggers B, Marcus K, Eisenacher M.

2018

doi:10.1101/424473.

Mass spectrometry evaluation of a neuroblastoma SH-SY5Y cell culture protocol.

Murillo JR, Pla I, Goto-Silva L, Nogueira FCS, Domont GB, Perez-Riverol Y, Sánchez A, Junqueira M.

Anal Biochem, 2018

doi:10.1016/j.ab.2018.08.013.

Quantifying the impact of public omics data

Perez-Riverol Y, Zorin A, Dass G, Glont M, Vizcaíno JA, Jarnuczak AF, Petryszak R, Ping P, Hermjakob H.

2018

doi:10.1101/282517.

ABRF Proteome Informatics Research Group (iPRG) 2016 Study: Inferring Proteoforms from Bottom-up Proteomics Data.

Lee JY, Choi H, Colangelo CM, Davis D, Hoopmann MR, Käll L, Lam H, Payne SH, Perez-Riverol Y, The M, Wilson R, Weintraub ST, Palmblad M.

J Biomol Tech, 2018

doi:10.7171/jbt.18-2902-003.

Bioconda: sustainable and comprehensive software distribution for the life sciences.

Grüning B, Dale R, Sjödin A, Chapman BA, Rowe J, Tomkins-Tinch CH, Valieris R, Köster J, Bioconda Team.

Nat Methods, 2018

doi:10.1038/s41592-018-0046-7.

Future Prospects of Spectral Clustering Approaches in Proteomics.

Perez-Riverol Y, Vizcaíno JA, Griss J.

Proteomics, 2018

doi:10.1002/pmic.201700454.

Response to "Comparison and Evaluation of Clustering Algorithms for Tandem Mass Spectra".

Griss J, Perez-Riverol Y, The M, Käll L, Vizcaíno JA.

J Proteome Res, 2018

doi:10.1021/acs.jproteome.7b00824.

A Protein Standard That Emulates Homology for the Characterization of Protein Inference Algorithms.

The M, Edfors F, Perez-Riverol Y, Payne SH, Hoopmann MR, Palmblad M, Forsström B, Käll L.

J Proteome Res, 2018

doi:10.1021/acs.jproteome.7b00899.

Bioconda: A sustainable and comprehensive software distribution for the life sciences

Grüning B, Dale R, Sjödin A, Chapman BA, Rowe J, Tomkins-Tinch CH, Valieris R, Caprez A, Batut B, Haudgaard M, Cokelaer T, Beauchamp KA, Pedersen BS, Hoogstrate Y, Bretaudeau A, Ryan D, Corguillé GL, Yusuf D, Luna-Valero S, Kirchner R, Brinda K, Wollmann T, Raden M, Heeringen SJv, Soranzo N, Pantano L, Charlop-Powers Z, Unneberg P, Smet MD, Martin M, Kuster GV, Antao T, Miladi M, Thornton K, Brueffer C, Beek Mvd, Maticzka D, Blank C, Will S, Gravouil K, Wolff J, Holtgrewe M, Fallmann J, Piro VC, Shlyakhter I, Yousif A, Mabon P, Zhang X, Shen W, Cabral J, Thomas C, Enns E, Brown J, Boekel J, Hollander Md, Kelleher J, Turaga N, Ruiter JRd, Bouvier D, Gladman S, Choudhary S, Harding N, Eggenhofer F, Kratz A, Fang Z, Kleinkauf R, Timm H, Cock PJA, Seiler E, Brislawn C, Nguyen H, Stovner EB, Ewels P, Chambers M, Johnson JE, Hägglund E, Ye S, Guimera RV, Pruesse E, Dunn WA, Parsons L, Patro R, Koppstein D, Grassi E, Wohlers I, Reynolds A, Cornwell M, Stoler N, Blankenberg D, He G, Bargull M, Junge A, Farouni R, Freeberg M, Singh S, Bogema DR, Cumbo F, Wang L, Larson DE, Workentine ML, Devisetty UK, Laurent S, Roger P, Garnier X, Agren R, Khan A, Eppley JM, Li W, Stöcker BK, Rausch T, Taylor J, Wright PR, Taranto AP, Chicco D, Sennblad B, Baaijens JA, Gopez M, Abdennur N, Milne I, Preussner J, Pinello L, Srivastava A, Chande AT, Kensche PR, Pirola Y, Knudsen M, Bruijn Id, Blin K, Gonnella G, Enache OM, Rai V, Waters NR, Hiltemann S, Bendall ML, Stahl C, Miles A, Boursin Y, Perez-Riverol Y, Schmeier S, Clarke E, Arvai K, Jung M, Domenico TD, Seiler J, Rasche E, Kornobis E, Beisser D, Rahmann S, Mikheyev AS, Tran C, Capellades J, Schröder C, Salatino AE, Dirmeier S, Webster TH, Moskalenko O, Stephen G, Köster J.

2017

doi:10.1101/207092.

A protein standard that emulates homology for the characterization of protein inference algorithms

The M, Edfors F, Perez-Riverol Y, Payne SH, Hoopmann MR, Palmblad M, Forsström B, Käll L.

2017

doi:10.1101/236471.

Accurate and fast feature selection workflow for high-dimensional omics data.

Perez-Riverol Y, Kuhn M, Vizcaíno JA, Hitz MP, Audain E.

PLoS One, 2017

doi:10.1371/journal.pone.0189875.

Enhanced Missing Proteins Detection in NCI60 Cell Lines Using an Integrative Search Engine Approach.

Guruceaga E, Garin-Muga A, Prieto G, Bejarano B, Marcilla M, Marín-Vicente C, Perez-Riverol Y, Casal JI, Vizcaíno JA, Corrales FJ, Segura V.

J Proteome Res, 2017

doi:10.1021/acs.jproteome.7b00388.

Proteomics Standards Initiative: Fifteen Years of Progress and Future Work.

Deutsch EW, Orchard S, Binz PA, Bittremieux W, Eisenacher M, Hermjakob H, Kawano S, Lam H, Mayer G, Menschaert G, Perez-Riverol Y, Salek RM, Tabb DL, Tenzer S, Vizcaíno JA, Walzer M, Jones AR.

J Proteome Res, 2017

doi:10.1021/acs.jproteome.7b00370.

OLS Client and OLS Dialog: Open Source Tools to Annotate Public Omics Datasets.

Perez-Riverol Y, Ternent T, Koch M, Barsnes H, Vrousgou O, Jupp S, Vizcaíno JA.

Proteomics, 2017

doi:10.1002/pmic.201700244.

Four simple recommendations to encourage best practices in research software.

Jiménez RC, Kuzak M, Alhamdoosh M, Barker M, Batut B, Borg M, Capella-Gutierrez S, Chue Hong N, Cook M, Corpas M, Flannery M, Garcia L, Gelpí JL, Gladman S, Goble C, González Ferreiro M, Gonzalez-Beltran A, Griffin PC, Grüning B, Hagberg J, Holub P, Hooft R, Ison J, Katz DS, Leskošek B, López Gómez F, Oliveira LJ, Mellor D, Mosbergen R, Mulder N, Perez-Riverol Y, Pergl R, Pichler H, Pope B, Sanz F, Schneider MV, Stodden V, Suchecki R, Svobodová Vařeková R, Talvik HA, Todorov I, Treloar A, Tyagi S, van Gompel M, Vaughan D, Via A, Wang X, Watson-Haigh NS, Crouch S.

F1000Res, 2017

doi:10.12688/f1000research.11407.1.

Accurate and Fast feature selection workflow for high-dimensional omics data

Perez-Riverol Y, Kun M, Vizcaíno JA, Hitz M, Audain E.

2017

doi:10.1101/144162.

The mzIdentML Data Standard Version 1.2, Supporting Advances in Proteome Informatics.

Vizcaíno JA, Mayer G, Perkins S, Barsnes H, Vaudel M, Perez-Riverol Y, Ternent T, Uszkoreit J, Eisenacher M, Fischer L, Rappsilber J, Netz E, Walzer M, Kohlbacher O, Leitner A, Chalkley RJ, Ghali F, Martínez-Bartolomé S, Deutsch EW, Jones AR.

Mol Cell Proteomics, 2017

doi:10.1074/mcp.m117.068429.

Discovering and linking public omics data sets using the Omics Discovery Index.

Perez-Riverol Y, Bai M, da Veiga Leprevost F, Squizzato S, Park YM, Haug K, Carroll AJ, Spalding D, Paschall J, Wang M, Del-Toro N, Ternent T, Zhang P, Buso N, Bandeira N, Deutsch EW, Campbell DS, Beavis RC, Salek RM, Sarkans U, Petryszak R, Keays M, Fahy E, Sud M, Subramaniam S, Barbera A, Jiménez RC, Nesvizhskii AI, Sansone SA, Steinbeck C, Lopez R, Vizcaíno JA, Ping P, Hermjakob H.

Nat Biotechnol, 2017

doi:10.1038/nbt.3790.

BioContainers: an open-source and community-driven framework for software standardization.

da Veiga Leprevost F, Grüning BA, Alves Aflitos S, Röst HL, Uszkoreit J, Barsnes H, Vaudel M, Moreno P, Gatto L, Weber J, Bai M, Jimenez RC, Sachsenberg T, Pfeuffer J, Vera Alvarez R, Griss J, Nesvizhskii AI, Perez-Riverol Y.

Bioinformatics, 2017

doi:10.1093/bioinformatics/btx192.

Synthetic human proteomes for accelerating protein research.

Perez-Riverol Y, Vizcaíno JA.

Nat Methods, 2017

doi:10.1038/nmeth.4191.

The ProteomeXchange consortium in 2017: supporting the cultural change in proteomics public data deposition.

Deutsch EW, Csordas A, Sun Z, Jarnuczak A, Perez-Riverol Y, Ternent T, Campbell DS, Bernal-Llinares M, Okuda S, Kawano S, Moritz RL, Carver JJ, Wang M, Ishihama Y, Bandeira N, Hermjakob H, Vizcaíno JA.

Nucleic Acids Res, 2017

doi:10.1093/nar/gkw936.

In-depth analysis of protein inference algorithms using multiple search engines and well-defined metrics.

Audain E, Uszkoreit J, Sachsenberg T, Pfeuffer J, Liang X, Hermjakob H, Sanchez A, Eisenacher M, Reinert K, Tabb DL, Kohlbacher O, Perez-Riverol Y.

J Proteomics, 2017

doi:10.1016/j.jprot.2016.08.002.

Ten Simple Rules for Taking Advantage of git and GitHub

Perez-Riverol Y, Gatto L, Wang R, Sachsenberg T, Uszkoreit J, Veiga Leprevost Fd, Fufezan C, Ternent T, Eglen SJ, Katz DS, Pollard TJ, Konovalov A, Flight RM, Blin K, Vizcaino JA.

2016

doi:10.1101/048744.

Omics Discovery Index - Discovering and Linking Public Omics Datasets

Perez-Riverol Y, Bai M, Leprevost F, Squizzato S, Park YM, et al.

2016

doi:10.1101/049205.

A multicenter study benchmarks software tools for label-free proteome quantification.

Navarro P, Kuharev J, Gillet LC, Bernhardt OM, MacLean B, Röst HL, Tate SA, Tsou CC, Reiter L, Distler U, Rosenberger G, Perez-Riverol Y, Nesvizhskii AI, Aebersold R, Tenzer S.

Nat Biotechnol, 2016

doi:10.1038/nbt.3685.

2016 update of the PRIDE database and its related tools.

Vizcaíno JA, Csordas A, Del-Toro N, Dianes JA, Griss J, Lavidas I, Mayer G, Perez-Riverol Y, Reisinger F, Ternent T, Xu QW, Wang R, Hermjakob H.

Nucleic Acids Res, 2016

doi:10.1093/nar/gkw880.

Recognizing millions of consistently unidentified spectra across hundreds of shotgun proteomics datasets.

Griss J, Perez-Riverol Y, Lewis S, Tabb DL, Dianes JA, Del-Toro N, Rurik M, Walzer MW, Kohlbacher O, Hermjakob H, Wang R, Vizcaíno JA.

Nat Methods, 2016

doi:10.1038/nmeth.3902.

Ten Simple Rules for Taking Advantage of Git and GitHub.

Perez-Riverol Y, Gatto L, Wang R, Sachsenberg T, Uszkoreit J, Leprevost Fda V, Fufezan C, Ternent T, Eglen SJ, Katz DS, Pollard TJ, Konovalov A, Flight RM, Blin K, Vizcaíno JA.

PLoS Comput Biol, 2016

doi:10.1371/journal.pcbi.1004947.

Accurate estimation of isoelectric point of protein and peptide based on amino acid sequences.

Audain E, Ramos Y, Hermjakob H, Flower DR, Perez-Riverol Y.

Bioinformatics, 2016

doi:10.1093/bioinformatics/btv674.

Novel interactions of domain III from the envelope glycoprotein of dengue 2 virus with human plasma proteins.

Huerta V, Ramos Y, Yero A, Pupo D, Martín D, Toledo P, Fleitas N, Gallien S, Martín AM, Márquez GJ, Pérez-Riverol Y, Sarría M, Guirola O, González LJ, Domon B, Chinea G.

J Proteomics, 2016

doi:10.1016/j.jprot.2015.11.003.

PRIDE Inspector Toolsuite: Moving Toward a Universal Visualization Tool for Proteomics Data Standard Formats and Quality Assessment of ProteomeXchange Datasets.

Perez-Riverol Y, Xu QW, Wang R, Uszkoreit J, Griss J, Sanchez A, Reisinger F, Csordas A, Ternent T, Del-Toro N, Dianes JA, Eisenacher M, Hermjakob H, Vizcaíno JA.

Mol Cell Proteomics, 2016

doi:10.1074/mcp.o115.050229.

2016 update of the PRIDE database and its related tools.

Vizcaíno JA, Csordas A, del-Toro N, Dianes JA, Griss J, Lavidas I, Mayer G, Perez-Riverol Y, Reisinger F, Ternent T, Xu QW, Wang R, Hermjakob H.

Nucleic Acids Res, 2016

doi:10.1093/nar/gkv1145.

Computational proteomics: Integrating mass spectral data into a biological context.

Carvalho PC, Padron G, Calvete JJ, Perez-Riverol Y.

J Proteomics, 2015

doi:10.1016/j.jprot.2015.10.013.

Data for comparative proteomics analysis of the antitumor effect of CIGB-552 peptide in HT-29 colon adenocarcinoma cells.

Núñez de Villavicencio-Díaz T, Ramos Gómez Y, Oliva Argüelles B, Fernández Masso JR, Rodríguez-Ulloa A, Cruz García Y, Guirola-Cruz O, Perez-Riverol Y, Javier González L, Tiscornia I, Victoria S, Bollati-Fogolín M, Besada Pérez V, Guerra Vallespi M.

Data Brief, 2015

doi:10.1016/j.dib.2015.06.024.

Comparative proteomics analysis of the antitumor effect of CIGB-552 peptide in HT-29 colon adenocarcinoma cells.

Núñez de Villavicencio-Díaz T, Ramos Gómez Y, Oliva Argüelles B, Fernández Masso JR, Rodríguez-Ulloa A, Cruz García Y, Guirola-Cruz O, Perez-Riverol Y, Javier González L, Tiscornia I, Victoria S, Bollati-Fogolín M, Besada Pérez V, Guerra Vallespi M.

J Proteomics, 2015

doi:10.1016/j.jprot.2015.05.024.

PIA: An Intuitive Protein Inference Engine with a Web-Based User Interface.

Uszkoreit J, Maerkens A, Perez-Riverol Y, Meyer HE, Marcus K, Stephan C, Kohlbacher O, Eisenacher M.

J Proteome Res, 2015

doi:10.1021/acs.jproteome.5b00121.

ms-data-core-api: an open-source, metadata-oriented library for computational proteomics.

Perez-Riverol Y, Uszkoreit J, Sanchez A, Ternent T, Del Toro N, Hermjakob H, Vizcaíno JA, Wang R.

Bioinformatics, 2015

doi:10.1093/bioinformatics/btv250.

Open source libraries and frameworks for biological data visualisation: a guide for developers.

Wang R, Perez-Riverol Y, Hermjakob H, Vizcaíno JA.

Proteomics, 2015

doi:10.1002/pmic.201400377.

Identifying novel biomarkers through data mining-a realistic scenario?

Griss J, Perez-Riverol Y, Hermjakob H, Vizcaíno JA.

Proteomics Clin Appl, 2015

doi:10.1002/prca.201400107.

Making proteomics data accessible and reusable: current state of proteomics databases and repositories.

Perez-Riverol Y, Alpi E, Wang R, Hermjakob H, Vizcaíno JA.

Proteomics, 2015

doi:10.1002/pmic.201400302.

On best practices in the development of bioinformatics software.

Leprevost Fda V, Barbosa VC, Francisco EL, Perez-Riverol Y, Carvalho PC.

Front Genet, 2014

doi:10.3389/fgene.2014.00199.

The mzTab data exchange format: communicating mass-spectrometry-based proteomics and metabolomics experimental results to a wider audience.

Griss J, Jones AR, Sachsenberg T, Walzer M, Gatto L, Hartler J, Thallinger GG, Salek RM, Steinbeck C, Neuhauser N, Cox J, Neumann S, Fan J, Reisinger F, Xu QW, Del Toro N, Pérez-Riverol Y, Ghali F, Bandeira N, Xenarios I, Kohlbacher O, Vizcaíno JA, Hermjakob H.

Mol Cell Proteomics, 2014

doi:10.1074/mcp.o113.036681.

Editorial: Genomics and proteomics behind drug design.

Perez-Riverol Y, Carvalho PC.

Curr Top Med Chem, 2014

doi:10.2174/1568026613666131204101110.

Bioinformatics tools for the functional interpretation of quantitative proteomics results.

Villavicencio-Diaz TN, Rodriguez-Ulloa A, Guirola-Cruz O, Perez-Riverol Y.

Curr Top Med Chem, 2014

doi:10.2174/1568026613666131204105110.

A survey of molecular descriptors used in mass spectrometry based proteomics.

Audain E, Sanchez A, Vizcaíno JA, Perez-Riverol Y.

Curr Top Med Chem, 2014

doi:10.2174/1568026613666131204113537.

Open source libraries and frameworks for mass spectrometry based proteomics: a developer's perspective.

Perez-Riverol Y, Wang R, Hermjakob H, Müller M, Vesada V, Vizcaíno JA.

Biochim Biophys Acta, 2014

doi:10.1016/j.bbapap.2013.02.032.

SCX charge state selective separation of tryptic peptides combined with 2D-RP-HPLC allows for detailed proteome mapping.

Betancourt LH, De Bock PJ, Staes A, Timmerman E, Perez-Riverol Y, Sanchez A, Besada V, Gonzalez LJ, Vandekerckhove J, Gevaert K.

J Proteomics, 2013

doi:10.1016/j.jprot.2013.06.033.

JBioWH: an open-source Java framework for bioinformatics data integration.

Vera R, Perez-Riverol Y, Perez S, Ligeti B, Kertész-Farkas A, Pongor S.

Database (Oxford), 2013

doi:10.1093/database/bat051.

Pinpointing differentially expressed domains in complex protein mixtures with the cloud service of PatternLab for Proteomics.

Leprevost FV, Lima DB, Crestani J, Perez-Riverol Y, Zanchin N, Barbosa VC, Carvalho PC.

J Proteomics, 2013

doi:10.1016/j.jprot.2013.06.013.

HI-bone: a scoring system for identifying phenylisothiocyanate-derivatized peptides based on precursor mass and high intensity fragment ions.

Perez-Riverol Y, Sánchez A, Noda J, Borges D, Carvalho PC, Wang R, Vizcaíno JA, Betancourt L, Ramos Y, Duarte G, Nogueira FC, González LJ, Padrón G, Tabb DL, Hermjakob H, Domont GB, Besada V.

Anal Chem, 2013

doi:10.1021/ac303239g.

Effectively addressing complex proteomic search spaces with peptide spectrum matching.

Borges D, Perez-Riverol Y, Nogueira FC, Domont GB, Noda J, da Veiga Leprevost F, Besada V, França FM, Barbosa VC, Sánchez A, Carvalho PC.

Bioinformatics, 2013

doi:10.1093/bioinformatics/btt106.

Computational proteomics pitfalls and challenges: HavanaBioinfo 2012 workshop report.

Perez-Riverol Y, Hermjakob H, Kohlbacher O, Martens L, Creasy D, Cox J, Leprevost F, Shan BP, Pérez-Nueno VI, Blazejczyk M, Punta M, Vierlinger K, Valiente PA, Leon K, Chinea G, Guirola O, Bringas R, Cabrera G, Guillen G, Padron G, Gonzalez LJ, Besada V.

J Proteomics, 2013

doi:10.1016/j.jprot.2013.01.019.

The PRoteomics IDEntifications (PRIDE) database and associated tools: status in 2013.

Vizcaíno JA, Côté RG, Csordas A, Dianes JA, Fabregat A, Foster JM, Griss J, Alpi E, Birim M, Contell J, O'Kelly G, Schoenegger A, Ovelleiro D, Pérez-Riverol Y, Reisinger F, Ríos D, Wang R, Hermjakob H.

Nucleic Acids Res, 2013

doi:10.1093/nar/gks1262.

Selective isolation of multiply charged peptides: a confident strategy for protein identification using a linear trap quadrupole mass spectrometer.

Sanchez A, Sun W, Ma J, Betancourt L, Perez-Riverol Y, de-Cossio JF, Padron G, Jiang Y, He F, Gonzalez LJ, Besada V.

Eur J Mass Spectrom (Chichester), 2012

doi:10.1255/ejms.1204.

A parallel systematic-Monte Carlo algorithm for exploring conformational space.

Perez-Riverol Y, Vera R, Mazola Y, Musacchio A.

Curr Top Med Chem, 2012

doi:10.2174/1568026611209061790.

The PRoteomics IDEntification (PRIDE) Converter 2 framework: an improved suite of tools to facilitate data submission to the PRIDE database and the ProteomeXchange consortium.

Côté RG, Griss J, Dianes JA, Wang R, Wright JC, van den Toorn HW, van Breukelen B, Heck AJ, Hulstaert N, Martens L, Reisinger F, Csordas A, Ovelleiro D, Perez-Rivevol Y, Barsnes H, Hermjakob H, Vizcaíno JA.

Mol Cell Proteomics, 2012

doi:10.1074/mcp.o112.021543.

Isoelectric point optimization using peptide descriptors and support vector machines.

Perez-Riverol Y, Audain E, Millan A, Ramos Y, Sanchez A, Vizcaíno JA, Wang R, Müller M, Machado YJ, Betancourt LH, González LJ, Padrón G, Besada V.

J Proteomics, 2012

doi:10.1016/j.jprot.2012.01.029.

PRIDE Inspector: a tool to visualize and validate MS proteomics data.

Wang R, Fabregat A, Ríos D, Ovelleiro D, Foster JM, Côté RG, Griss J, Csordas A, Perez-Riverol Y, Reisinger F, Hermjakob H, Martens L, Vizcaíno JA.

Nat Biotechnol, 2012

doi:10.1038/nbt.2112.

Introducing an Asp-Pro linker in the synthesis of random one-bead-one-compound hexapeptide libraries compatible with ESI-MS analysis.

Masforrol Y, Gil J, González LJ, Pérez-Riverol Y, Fernández-de-Cossío J, Sánchez A, Betancourt LH, Garay HE, Cabrales A, Albericio F, Yang H, Zubarev RA, Besada V, Acosta OR.

ACS Comb Sci, 2012

doi:10.1021/co200159r.

In silico analysis of accurate proteomics, complemented by selective isolation of peptides.

Perez-Riverol Y, Sánchez A, Ramos Y, Schmidt A, Müller M, Betancourt L, González LJ, Vera R, Padron G, Besada V.

J Proteomics, 2011

doi:10.1016/j.jprot.2011.05.034.

Charge state-selective separation of peptides by reversible modification of amino groups and strong cation-exchange chromatography: evaluation in proteomic studies using peptide-centric database searches.

Betancourt LH, Sánchez A, Pérez Y, Fernandez de Cossio J, Gil J, Toledo P, Iguchi S, Aimoto S, González LJ, Padrón G, Takao T, Besada V.

J Proteomics, 2011

doi:10.1016/j.jprot.2011.04.029.

Peptide fractionation by acid pH SDS-free electrophoresis.

Ramos Y, Garcia Y, Pérez-Riverol Y, Leyva A, Padrón G, Sánchez A, Castellanos-Serra L, González LJ, Besada V.

Electrophoresis, 2011

doi:10.1002/elps.201000677.

Selective isolation-detection of two different positively charged peptides groups by strong cation exchange chromatography and matrix-assisted laser desorption/ionization mass spectrometry: application to proteomics studies.

Sanchez A, Sun W, Wang L, Ma J, Betancourt L, Gil J, Perez-Riverol Y, Fernandez de-Cossio J, Padron G, Jiang Y, He F, Gonzalez LJ, Besada V.

Eur J Mass Spectrom (Chichester), 2010

doi:10.1255/ejms.1105.

Evaluation of phenylthiocarbamoyl-derivatized peptides by electrospray ionization mass spectrometry: selective isolation and analysis of modified multiply charged peptides for liquid chromatography-tandem mass spectrometry experiments.

Sanchez A, Perez-Riverol Y, González LJ, Noda J, Betancourt L, Ramos Y, Gil J, Vera R, Padrón G, Besada V.

Anal Chem, 2010

doi:10.1021/ac1012738.

Proteomics based on peptide fractionation by SDS-free PAGE.

Ramos Y, Gutierrez E, Machado Y, Sánchez A, Castellanos-Serra L, González LJ, Fernández-de-Cossio J, Pérez-Riverol Y, Betancourt L, Gil J, Padrón G, Besada V.

J Proteome Res, 2008

doi:10.1021/pr700840y.