@article{Almeida2021, abstract = {Plasma analysis by mass spectrometry-based proteomics remains a challenge due to its large dynamic range of 10 orders in magnitude. We created a methodology for protein identification known as Wise MS Transfer (WiMT). Melanoma plasma samples from biobank archives were directly analyzed using simple sample preparation. WiMT is based on MS1 features between several MS runs together with custom protein databases for ID generation. This entails a multi-level dynamic protein database with different immunodepletion strategies by applying single-shot proteomics. The highest number of melanoma plasma proteins from undepleted and unfractionated plasma was reported, mapping >1200 proteins from >10,000 protein sequences with confirmed significance scoring. Of these, more than 660 proteins were annotated by WiMT from the resulting ~5800 protein sequences. We could verify 4000 proteins by MS1t analysis from HeLA extracts. The WiMT platform provided an output in which 12 previously well-known candidate markers were identified. We also identified low-abundant proteins with functions related to (i) cell signaling, (ii) immune system regulators, and (iii) proteins regulating folding, sorting, and degradation, as well as (iv) vesicular transport proteins. WiMT holds the potential for use in large-scale screening studies with simple sample preparation, and can lead to the discovery of novel proteins with key melanoma disease functions.}, author = {Almeida, Natália and Rodriguez, Jimmy and Parada, Indira Pla and Pla Parada, Indira and Perez-Riverol, Yasset and Woldmar, Nicole and Kim, Yonghyo and Oskolas, Henriett and Betancourt, Lazaro and Valdés, Jeovanis Gil and Barbara Sahlin, K. and Sahlin, K. Barbara and Pizzatti, Luciana and Marcell Szasz, A. and Szasz, A. Marcell and Kárpáti, Sarolta and Appelqvist, Roger and Malm, Johan and B. Domont, Gilberto and Domont, Gilberto B. and C. S. Nogueira, Fábio and Nogueira, Fábio C. S. and Marko-Varga, György and Sanchez, Aniel}, doi = {10.3390/cancers13246224}, journal = {Cancers}, month = {dec}, pages = {6224}, title = {Mapping the Melanoma Plasma Proteome (MPP) Using Single-Shot Proteomics Interfaced with the WiMT Database}, url = {https://doi.org/10.3390/cancers13246224}, volume = {13}, year = {2021} } @article{Audain2021, abstract = {Numerous genetic studies have established a role for rare genomic variants in Congenital Heart Disease (CHD) at the copy number variation (CNV) and de novo variant (DNV) level. To identify novel haploinsufficient CHD disease genes, we performed an integrative analysis of CNVs and DNVs identified in probands with CHD including cases with sporadic thoracic aortic aneurysm. We assembled CNV data from 7,958 cases and 14,082 controls and performed a gene-wise analysis of the burden of rare genomic deletions in cases versus controls. In addition, we performed variation rate testing for DNVs identified in 2,489 parent-offspring trios. Our analysis revealed 21 genes which were significantly affected by rare CNVs and/or DNVs in probands. Fourteen of these genes have previously been associated with CHD while the remaining genes (FEZ1, MYO16, ARID1B, NALCN, WAC, KDM5B and WHSC1) have only been associated in small cases series or show new associations with CHD. In addition, a systems level analysis revealed affected protein-protein interaction networks involved in Notch signaling pathway, heart morphogenesis, DNA repair and cilia/centrosome function. Taken together, this approach highlights the importance of re-analyzing existing datasets to strengthen disease association and identify novel disease genes and pathways.}, author = {Audain, Enrique and Wilsdon, Anna and Benson, Woodrow D. and Breckpot, Jeroen and Berger, Felix and Izarzugaza, Jose M. G. and Daehnert, Ingo and Belmont, J. W. and Fitzgerald, Tomas W. and Devriendt, Koenraad and Benson, D. W. and Kahlert, Anne-Karin and Dittrich, Sven and Daubeney, Piers Ef and Sifrim, Alejandro and Garg, Vidu and Hackmann, Karl and Wünnemann, Florian and Hoff, Kirstin and Hofmann, Philipp and Perez-Riverol, Yasset and Dombrowsky, Gregor and Pickardt, Thomas and Bauer, Ulrike and Keavney, Bernard D. and Abdul-Khaliq, Hashim and Klaassen, Sabine and Kramer, Hans-Heiner and Marshall, Christian R. and Bak, Mads and Milewicz, Dianna M. and Lemaire, Scott A. and Coselli, Joseph S. and Mitchell, Michael E. and Bassett, Anne S. and Tomita-Mitchell, Aoy and Prakash, Siddharth K. and Stamm, Karl and Stewart, Alexandre F. R. and Silversides, Candice K. and Siebert, Reiner and Stiller, Brigitte and Rosenfeld, Jill A. and Vater, Inga and Postma, Alex V. and Caliebe, Almuth and Brook, J. David and Andelfinger, Gregor and Hurles, Matthew E. and Thienpont, Bernard and Larsen, Lars Allan and Hitz, Marc-Phillip}, doi = {10.1371/journal.pgen.1009679}, journal = {PLoS Genetics}, month = {jul}, pages = {e1009679}, title = {Integrative analysis of genomic variants reveals new associations of candidate haploinsufficient genes with congenital heart disease}, url = {https://doi.org/10.1371/journal.pgen.1009679}, volume = {17}, year = {2021} } @article{Bittremieux2021, abstract = {The European Bioinformatics Community for Mass Spectrometry (EuBIC‐MS; eubic-ms.org) was founded in 2014 to unite European computational mass spectrometry researchers and proteomics bioinformaticians working in academia and industry. EuBIC‐MS maintains educational resources (proteomics-academy.org) and organises workshops at national and international conferences on proteomics and mass spectrometry. Furthermore, EuBIC‐MS is actively involved in several community initiatives such as the Human Proteome Organization's Proteomics Standards Initiative (HUPO‐PSI). Apart from these collaborations, EuBIC‐MS has organised two Winter Schools and two Developers' Meetings that have contributed to the strengthening of the European mass spectrometry network and fostered international collaboration in this field, even beyond Europe. Moreover, EuBIC‐MS is currently actively developing a community‐driven standard dedicated to mass spectrometry data annotation (SDRF‐Proteomics) that will facilitate data reuse and collaboration. This manuscript highlights what EuBIC‐MS is, what it does, and what it already has achieved. A warm invitation is extended to new researchers at all career stages to join the EuBIC‐MS community on its Slack channel (eubic.slack.com).}, author = {Bittremieux, Wout and Bouyssié, David and Dorfer, Viktoria and Locard‐Paulet, Marie and Perez‐Riverol, Yasset and Schwämmle, Veit and Uszkoreit, Julian and Van Den Bossche, Tim}, doi = {10.1002/rcm.9087}, journal = {Rapid Communications in Mass Spectrometry}, month = {apr}, title = {The European Bioinformatics Community for Mass Spectrometry (EuBIC‐MS): an open community for bioinformatics training and research}, url = {https://oadoi.org/10.1002/rcm.9087}, year = {2021} } @article{Choi2020, author = {Choi, Meena and Carver, Jeremy and Chiva, Cristina and Tzouros, Manuel and Huang, Ting and Tsai, Tsung-Heng and Pullman, Benjamin and Bernhardt, Oliver M. and Hüttenhain, Ruth and Teo, Guo Ci and Perez-Riverol, Yasset and Muntel, Jan and Müller, Maik and Goetze, Sandra and Pavlou, Maria and Verschueren, Erik and Wollscheid, Bernd and Nesvizhskii, Alexey I. and Reiter, Lukas and Dunkley, Tom and Sabidó, Eduard and Bandeira, Nuno and Vitek, Olga}, doi = {10.1038/s41592-020-0955-0}, journal = {Nature Methods}, month = {sep}, pages = {981-984}, title = {MassIVE.quant: a community resource of quantitative mass spectrometry–based proteomics datasets}, url = {https://www.nature.com/articles/s41592-020-0955-0.pdf}, volume = {17}, year = {2020} } @article{Claeys2023, abstract = {AbstractPublic proteomics data often lack essential metadata, limiting its potential. To address this, we present lesSDRF, a tool to simplify the process of metadata annotation, thereby ensuring that data leave a lasting, impactful legacy well beyond its initial publication.}, author = {Claeys, Tine and Van Den Bossche, Tim and Perez-Riverol, Yasset and Gevaert, Kris and Vizcaíno, Juan Antonio and Martens, Lennart}, doi = {10.1038/s41467-023-42543-5}, journal = {Nature Communications}, month = {oct}, title = {lesSDRF is more: maximizing the value of proteomics data through streamlined metadata annotation}, url = {https://doi.org/10.1038/s41467-023-42543-5}, volume = {14}, year = {2023} } @article{Dai2021, abstract = {AbstractThe amount of public proteomics data is rapidly increasing but there is no standardized format to describe the sample metadata and their relationship with the dataset files in a way that fully supports their understanding or reanalysis. Here we propose to develop the transcriptomics data format MAGE-TAB into a standard representation for proteomics sample metadata. We implement MAGE-TAB-Proteomics in a crowdsourcing project to manually curate over 200 public datasets. We also describe tools and libraries to validate and submit sample metadata-related information to the PRIDE repository. We expect that these developments will improve the reproducibility and facilitate the reanalysis and integration of public proteomics datasets.}, author = {Dai, Chengxin and Füllgrabe, Anja and Pfeuffer, Julianus and Solovyeva, Elizaveta M. and Deng, Jingwen and Moreno, Pablo and Kamatchinathan, Selvakumar and Kundu, Deepti Jaiswal and George, Nancy and Fexova, Silvie and Grüning, Björn and Föll, Melanie Christine and Griss, Johannes and Vaudel, Marc and Audain, Enrique and Locard-Paulet, Marie and Turewicz, Michael and Eisenacher, Martin and Uszkoreit, Julian and Van Den Bossche, Tim and Schwämmle, Veit and Webel, Henry and Schulze, Stefan and Bouyssié, David and Jayaram, Savita and Duggineni, Vinay Kumar and Samaras, Patroklos and Wilhelm, Mathias and Choi, Meena and Wang, Mingxun and Kohlbacher, Oliver and Brazma, Alvis and Papatheodorou, Irene and Bandeira, Nuno and Deutsch, Eric W. and Vizcaíno, Juan Antonio and Bai, Mingze and Sachsenberg, Timo and Levitsky, Lev I. and Perez-Riverol, Yasset}, doi = {10.1038/s41467-021-26111-3}, journal = {Nature Communications}, month = {oct}, title = {A proteomics sample metadata representation for multiomics integration and big data analysis}, url = {https://doi.org/10.1038/s41467-021-26111-3}, volume = {12}, year = {2021} } @article{Deutsch2016, author = {Deutsch, Eric W. and Csordas, Attila and Sun, Zhi and Jarnuczak, Andrew and Perez-Riverol, Yasset and Ternent, Tobias and Campbell, David S. and Bernal-Llinares, Manuel and Okuda, Shujiro and Kawano, Shin and Wang, Mingxun and Moritz, Robert L. and Carver, Jeremy J. and Ishihama, Yasushi and Bandeira, Nuno and Hermjakob, Henning and Vizcaíno, Juan Antonio}, doi = {10.1093/nar/gkw936}, journal = {Nucleic Acids Research}, month = {oct}, pages = {D1100-D1106}, title = {The ProteomeXchange consortium in 2017: supporting the cultural change in proteomics public data deposition.}, url = {https://doi.org/10.1093/nar/gkw936}, volume = {45}, year = {2016} } @article{Deutsch2018, author = {Deutsch, Eric W. and Perez-Riverol, Yasset and Chalkley, Robert J. and Wilhelm, Mathias and Tate, Stephen and Sachsenberg, Timo and Walzer, Mathias and Käll, Lukas and Delanghe, Bernard and Böcker, Sebastian and Schymanski, Emma L. and Wilmes, Paul and Dorfer, Viktoria and Kuster, Bernhard and Volders, Pieter-Jan and Jehmlich, Nico and Vissers, Johannes P. C. and Wolan, Dennis W. and Wang, Ana Y. and Mendoza, Luis and Shofstahl, Jim and Dowsey, Andrew W. and Griss, Johannes and Salek, Reza M. and Neumann, Steffen and Binz, Pierre-Alain and Lam, Henry and Vizcaíno, Juan Antonio and Bandeira, Nuno and Röst, Hannes}, doi = {10.1021/acs.jproteome.8b00485}, journal = {Journal of Proteome Research}, month = {oct}, pages = {4051-4060}, title = {Expanding the Use of Spectral Libraries in Proteomics}, url = {https://oadoi.org/10.1021/acs.jproteome.8b00485}, volume = {17}, year = {2018} } @article{Deutsch2021, author = {Deutsch, Eric W. and Perez-Riverol, Yasset and Carver, Jeremy and Kawano, Shin and Mendoza, Luis and Van Den Bossche, Tim and Gabriels, Ralf and Binz, Pierre-Alain and Pullman, Benjamin and Sun, Zhi and Shofstahl, Jim and Bittremieux, Wout and Mak, Tytus D. and Klein, Joshua and Zhu, Yunping and Lam, Henry and Vizcaíno, Juan Antonio and Bandeira, Nuno}, doi = {10.1038/s41592-021-01184-6}, journal = {Nature Methods}, month = {jun}, pages = {768-770}, title = {Universal Spectrum Identifier for mass spectra}, url = {http://www.nature.com/articles/s41592-021-01184-6.pdf}, volume = {18}, year = {2021} } @article{Jiménez2017, abstract = {Scientific research relies on computer software, yet software is not always developed following practices that ensure its quality and sustainability. This manuscript does not aim to propose new software development best practices, but rather to provide simple recommendations that encourage the adoption of existing best practices. Software development best practices promote better quality software, and better quality software improves the reproducibility and reusability of research. These recommendations are designed around Open Source values, and provide practical suggestions that contribute to making research software and its source code more discoverable, reusable and transparent. This manuscript is aimed at developers, but also at organisations, projects, journals and funders that can increase the quality and sustainability of research software by encouraging the adoption of these recommendations.}, author = {Jiménez, Rafael C. and Kuzak, Mateusz and Alhamdoosh, Monther and Barker, Michelle and Batut, Bérénice and Borg, Mikael and Capella-Gutierrez, Salvador and Chue Hong, Neil and Cook, Martin and Corpas, Manuel and Flannery, Madison and Garcia, Leyla and Gelpí, Josep L.-L. and Gladman, Simon and Goble, Carole and González Ferreiro, Montserrat and Gonzalez-Beltran, Alejandra and Griffin, Philippa C. and Grüning, Björn and Hagberg, Jonas and Holub, Petr and Hooft, Rob and Ison, Jon and Katz, Daniel S. and Leskošek, Brane and López Gómez, Federico and Oliveira, Luis J. and Mellor, David and Mosbergen, Rowland and Mulder, Nicola and Perez-Riverol, Yasset and Pergl, Robert and Pichler, Horst and Pope, Bernard and Sanz, Ferran and Schneider, Maria V. and Stodden, Victoria and Suchecki, Radosław and Svobodová Vařeková, Radka and Talvik, Harry-Anton and Todorov, Ilian and Treloar, Andrew and Tyagi, Sonika and van Gompel, Maarten and Vaughan, Daniel and Via, Allegra and Wang, Xiaochuan and Watson-Haigh, Nathan S. and Crouch, Steve}, doi = {10.12688/f1000research.11407.1}, journal = {F1000Research}, month = {jun}, pages = {876}, title = {Four simple recommendations to encourage best practices in research software}, url = {https://f1000research.com/articles/6-876/v1/pdf}, volume = {6}, year = {2017} } @article{Katayama2019, abstract = {Publishing databases in the Resource Description Framework (RDF) model is becoming widely accepted to maximize the syntactic and semantic interoperability of open data in life sciences. Here we report advancements made in the 6th and 7th annual BioHackathons which were held in Tokyo and Miyagi respectively. This review consists of two major sections covering: 1) improvement and utilization of RDF data in various domains of the life sciences and 2) meta-data about these RDF data, the resources that store them, and the service quality of SPARQL Protocol and RDF Query Language (SPARQL) endpoints. The first section describes how we developed RDF data, ontologies and tools in genomics, proteomics, metabolomics, glycomics and by literature text mining. The second section describes how we defined descriptions of datasets, the provenance of data, and quality assessment of services and service discovery. By enhancing the harmonization of these two layers of machine-readable data and knowledge, we improve the way community wide resources are developed and published. Moreover, we outline best practices for the future, and prepare ourselves for an exciting and unanticipatable variety of real world applications in coming years.}, author = {Katayama, Toshiaki and Kawashima, Shuichi and Micklem, Gos and Kawano, Shin and Kim, Jin-Dong and Kocbek, Simon and Okamoto, Shinobu and Wang, Yue and Wu, Hongyan and Yamaguchi, Atsuko and Yamamoto, Yasunori and Antezana, Erick and Aoki-Kinoshita, Kiyoko F. and Arakawa, Kazuharu and Banno, Masaki and Baran, Joachim and Bolleman, Jerven T. and Bonnal, Raoul J. P. and Bono, Hidemasa and Fernández-Breis, Jesualdo T. and Buels, Robert and Campbell, Matthew P. and Chiba, Hirokazu and Cock, Peter J. A. and Cohen, Kevin B. and Dumontier, Michel and Fujisawa, Takatomo and Fujiwara, Toyofumi and Garcia, Leyla and Gaudet, Pascale and Hattori, Emi and Hoehndorf, Robert and Itaya, Kotone and Ito, Maori and Jamieson, Daniel and Jupp, Simon and Juty, Nick and Kalderimis, Alex and Kato, Fumihiro and Kawaji, Hideya and Kawashima, Takeshi and Kinjo, Akira R. and Komiyama, Yusuke and Kotera, Masaaki and Kushida, Tatsuya and Malone, James and Matsubara, Masaaki and Mizuno, Satoshi and Mizutani, Sayaka and Mori, Hiroshi and Moriya, Yuki and Murakami, Katsuhiko and Nakazato, Takeru and Nishide, Hiroyo and Nishimura, Yosuke and Ogishima, Soichi and Ohta, Tazro and Okuda, Shujiro and Ono, Hiromasa and Perez-Riverol, Yasset and Shinmachi, Daisuke and Splendiani, Andrea and Strozzi, Francesco and Suzuki, Shinya and Takehara, Junichi and Thompson, Mark and Tokimatsu, Toshiaki and Uchiyama, Ikuo and Verspoor, Karin and Wilkinson, Mark D. and Wimalaratne, Sarala and Yamada, Issaku and Yamamoto, Nozomi and Yarimizu, Masayuki and Kawamoto, Shoko and Takagi, Toshihisa}, doi = {10.12688/f1000research.18238.1}, journal = {F1000Research}, month = {sep}, pages = {1677}, title = {BioHackathon series in 2013 and 2014: improvements of semantic interoperability in life science data and services}, url = {https://doi.org/10.12688/f1000research.18238.1}, volume = {8}, year = {2019} } @article{LeDuc2022, author = {LeDuc, Richard D. and Deutsch, Eric W. and Binz, Pierre-Alain and Fellers, Ryan T. and Cesnik, Anthony J. and Klein, Joshua A. and Van Den Bossche, Tim and Gabriels, Ralf and Yalavarthi, Arshika and Perez-Riverol, Yasset and Carver, Jeremy and Bittremieux, Wout and Kawano, Shin and Pullman, Benjamin and Bandeira, Nuno and Kelleher, Neil L. and Thomas, Paul M. and Vizcaíno, Juan Antonio}, doi = {10.1021/acs.jproteome.1c00771}, journal = {Journal of Proteome Research}, month = {mar}, pages = {1189-1195}, title = {Proteomics Standards Initiative’s ProForma 2.0: Unifying the Encoding of Proteoforms and Peptidoforms}, url = {https://oadoi.org/10.1021/acs.jproteome.1c00771}, volume = {21}, year = {2022} } @article{Luo2022, author = {Luo, Xiyang and Bittremieux, Wout and Griss, Johannes and Deutsch, Eric W. and Sachsenberg, Timo and Levitsky, Lev I. and Ivanov, Mark V. and Bubis, Julia A. and Gabriels, Ralf and Webel, Henry and Sanchez, Aniel and Bai, Mingze and Käll, Lukas and Perez-Riverol, Yasset}, doi = {10.1021/acs.jproteome.2c00069}, journal = {Journal of Proteome Research}, month = {may}, pages = {1566-1574}, title = {A Comprehensive Evaluation of Consensus Spectrum Generation Methods in Proteomics}, url = {https://pubs.acs.org/doi/pdf/10.1021/acs.jproteome.2c00069}, volume = {21}, year = {2022} } @article{Moreno2018, author = {Moreno, Pablo and Pireddu, Luca and Roger, Pierrick and Goonasekera, Nuwan and Afgan, Enis and van den Beek, Marius and He, Sijin and Larsson, Anders and Ruttkies, Christoph and Schober, Daniel and Johnson, David and Rocca-Serra, Philippe and Weber, Ralf Jm M. and Gruening, Bjoern and Salek, Reza M. and Kale, Namrata and Perez-Riverol, Yasset and Papatheodorou, Irene and Spjuth, Ola and Neumann, Steffen}, month = {dec}, title = {Galaxy-Kubernetes integration: scaling bioinformatics workflows in the cloud}, year = {2018} } @article{Perez-Riverol2016, author = {Perez-Riverol, Yasset and Griss, Johannes and Lewis, Steve and Tabb, David L. and del-Toro, Noemi and Dianes, José A. and Walzer, Mathias and Rurik, Marc and Kohlbacher, Oliver and Hermjakob, Henning and Wang, Rui and Vizcaíno, Juan Antonio}, doi = {10.1038/nmeth.3902}, journal = {Nature Methods}, month = {jun}, pages = {651-656}, title = {Recognizing millions of consistently unidentified spectra across hundreds of shotgun proteomics datasets.}, url = {http://europepmc.org/articles/pmc4968634?pdf=render}, volume = {13}, year = {2016} } @article{Perez-Riverol2017, author = {Perez-Riverol, Yasset and Bai, Mingze and da Veiga Leprevost, Felipe and Squizzato, Silvano and Park, Young Mi and Haug, Kenneth and Carroll, Adam J. and Spalding, Dylan and Paschall, Justin and Wang, Mingxun and del-Toro, Noemi and Ternent, Tobias and Zhang, Peng and Buso, Nicola and Bandeira, Nuno and Deutsch, Eric W. and Campbell, David S. and Beavis, Ronald C. and Salek, Reza M. and Sarkans, Ugis and Petryszak, Robert and Keays, Maria and Fahy, Eoin and Sud, Manish and Subramaniam, Shankar and Barbera, Ariana and Jiménez, Rafael C. and Nesvizhskii, Alexey I. and Sansone, Susanna-Assunta and Steinbeck, Christoph and Lopez, Rodrigo and Vizcaíno, Juan A. and Ping, Peipei and Hermjakob, Henning}, doi = {10.1038/nbt.3790}, journal = {Nature Biotechnology}, month = {may}, pages = {406-409}, title = {Discovering and linking public omics data sets using the Omics Discovery Index}, url = {http://europepmc.org/articles/pmc5831141?pdf=render}, volume = {35}, year = {2017} } @article{Perez-Riverol2017_2, author = {Perez-Riverol, Yasset and Kuhn, Max and Kun, Max and Vizcaino, Juan Antonio and Hitz, Marc-Phillip and Audain, Enrique}, doi = {10.1101/144162}, journal = {PLoS ONE}, month = {jun}, pages = {e0189875}, title = {Accurate And Fast Feature Selection Workflow For High-Dimensional Omics Data}, url = {https://doi.org/10.1371/journal.pone.0189875}, volume = {12}, year = {2017} } @article{Ramos2017, author = {Ramos, Yassel and Huerta, Vivian and Martín, Dayron and Palomares, Sucel and Yero, Alexis and Pupo, Dianne and Gallien, Sebastien and Martín, Alejandro M. and Pérez-Riverol, Yasset and Sarría, Mónica and Guirola, Osmany and Chinea, Glay and Domon, Bruno and González, Luis Javier}, doi = {10.1016/j.jprot.2017.07.004}, journal = {Journal of Proteomics}, month = {jul}, title = {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}, url = {https://oadoi.org/10.1016/j.jprot.2017.07.004}, year = {2017} } @article{Schmidt2020, author = {Schmidt, Tobias and Samaras, Patroklos and Dorfer, Viktoria and Panse, Christian and Kockmann, Tobias and Bichmann, Leon and van Puyvelde, Bart and Perez-Riverol, Yasset and Deutsch, Eric W. and Kuster, Bernhard and Wilhelm, Mathias}, doi = {10.1021/acs.jproteome.1c00096}, journal = {Journal of Proteome Research}, month = {sep}, pages = {3388-3394}, title = {Universal Spectrum Explorer: A standalone (web-)application for cross-resource spectrum comparison}, url = {https://oadoi.org/10.1021/acs.jproteome.1c00096}, volume = {20}, year = {2020} } @article{Sinitcyn2021, abstract = {AbstractMaxDIA is a software platform for analyzing data-independent acquisition (DIA) proteomics data within the MaxQuant software environment. Using spectral libraries, MaxDIA achieves deep proteome coverage with substantially better coefficients of variation in protein quantification than other software. MaxDIA is equipped with accurate false discovery rate (FDR) estimates on both library-to-DIA match and protein levels, including when using whole-proteome predicted spectral libraries. This is the foundation of discovery DIA—hypothesis-free analysis of DIA samples without library and with reliable FDR control. MaxDIA performs three- or four-dimensional feature detection of fragment data, and scoring of matches is augmented by machine learning on the features of an identification. MaxDIA’s bootstrap DIA workflow performs multiple rounds of matching with increasing quality of recalibration and stringency of matching to the library. Combining MaxDIA with two new technologies—BoxCar acquisition and trapped ion mobility spectrometry—both lead to deep and accurate proteome quantification.}, author = {Sinitcyn, Pavel and Hamzeiy, Hamid and Salinas Soto, Favio and Itzhak, Daniel and McCarthy, Frank and Wichmann, Christoph and Steger, Martin and Ohmayer, Uli and Distler, Ute and Kaspar-Schoenefeld, Stephanie and Prianichnikov, Nikita and Yılmaz, Şule and Rudolph, Jan Daniel and Tenzer, Stefan and Perez-Riverol, Yasset and Nagaraj, Nagarjuna and Humphrey, Sean J. and Cox, Jürgen}, doi = {10.1038/s41587-021-00968-7}, journal = {Nature Biotechnology}, month = {jul}, pages = {1563-1573}, title = {MaxDIA enables library-based and library-free data-independent acquisition proteomics}, url = {https://oadoi.org/10.1038/s41587-021-00968-7}, volume = {39}, year = {2021} }