Proceedings of the EuBIC-MS 2020 Developers’ Meeting

EuPA Open Proteomics - Tập 24 - Trang 1-6 - 2020
Christopher Ashwood1,2
1European Bioinformatics Community for Mass Spectrometry ([email protected]), Denmark
2Department of Cellular and Integrative Physiology, University of Nebraska Medical Center, Omaha, USA

Tài liệu tham khảo

Kopczynski, 2019, Proceedings of the EuBIC Winter School 2019, EuPA Open Proteom., 22-23, 4, 10.1016/j.euprot.2019.07.002 Willems, 2017, Proceedings of the EuBIC Winter School 2017, J. Proteomics, 161, 78, 10.1016/j.jprot.2017.04.001 Willems, 2018, Proceedings of the EuBIC developer's meeting 2018, J. Proteomics, 187, 25, 10.1016/j.jprot.2018.05.015 Pfeuffer, 2017, OpenMS – a platform for reproducible analysis of mass spectrometry data, J. Biotechnol., 261, 142, 10.1016/j.jbiotec.2017.05.016 Ferries, 2017, Evaluation of parameters for confident phosphorylation site localization using an orbitrap fusion tribrid mass spectrometer, J. Proteome Res., 16, 3448, 10.1021/acs.jproteome.7b00337 Dogu, 2019, MSstatsQC 2.0: R/Bioconductor package for statistical quality control of mass spectrometry-based proteomics experiments, J. Proteome Res., 18, 678, 10.1021/acs.jproteome.8b00732 Tvardovskiy, 2017, Accumulation of histone variant H3.3 with age is associated with profound changes in the histone methylation landscape, Nucleic Acids Res., 45, 9272, 10.1093/nar/gkx696 Kirsch, 2020, Visualization of the dynamics of histone modifications and their crosstalk using PTM-CrossTalkMapper, Methods, 10.1016/j.ymeth.2020.01.012 Shliaha, 2018, Maximizing sequence coverage in top-down proteomics by automated multimodal gas-phase protein fragmentation, Anal. Chem., 90, 12519, 10.1021/acs.analchem.8b02344 Zahn-Zabal, 2020, The neXtProt knowledgebase in 2020: data, tools and usability improvements, Nucleic Acids Res., 48, D328 Duek, 2018, Exploring the uncharacterized human proteome using neXtProt, J. Proteome Res., 17, 4211, 10.1021/acs.jproteome.8b00537 Schaeffer, 2017, The neXtProt peptide uniqueness checker: a tool for the proteomics community, Bioinformatics, 33, 3471, 10.1093/bioinformatics/btx318 Hulstaert, 2020, ThermoRawFileParser: modular, scalable, and cross-platform RAW file conversion, J. Proteome Res., 19, 537, 10.1021/acs.jproteome.9b00328 Shannon, 2003, Cytoscape: a software environment for integrated models of biomolecular interaction networks, Genome Res., 13, 2498, 10.1101/gr.1239303 Otasek, 2019, Cytoscape Automation: empowering workflow-based network analysis, Genome Biol., 20, 185, 10.1186/s13059-019-1758-4 Doncheva, 2019, Cytoscape StringApp: network analysis and visualization of proteomics data, J. Proteome Res., 18, 623, 10.1021/acs.jproteome.8b00702 Legeay, 2020, Visualize omics data on networks with Omics Visualizer, a Cytoscape App, F1000Res, 9, 157, 10.12688/f1000research.22280.1 Szklarczyk, 2019, STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets, Nucleic Acids Res., 47, D607, 10.1093/nar/gky1131 Palasca, 2018, TISSUES 2.0: an integrative web resource on mammalian tissue expression, Database (Oxford), 2018, 10.1093/database/bay003 Sajulga, 2020, Survey of metaproteomics software tools for functional microbiome analysis, bioRxiv Schiebenhoefer, 2019, Challenges and promise at the interface of metaproteomics and genomics: an overview of recent progress in metaproteogenomic data analysis, Expert Rev. Proteomics, 16, 375, 10.1080/14789450.2019.1609944 Muth, 2015, The MetaProteomeAnalyzer: a powerful open-source software suite for metaproteomics data analysis and interpretation, J. Proteome Res., 14, 1557, 10.1021/pr501246w Gurdeep Singh, 2019, Unipept 4.0: functional analysis of metaproteome data, J. Proteome Res., 18, 606, 10.1021/acs.jproteome.8b00716 Van Den Bossche, 2020, Connecting MetaProteomeAnalyzer and PeptideShaker to unipept for seamless end-to-end metaproteomics data analysis, J. Proteome Res., 19, 3562, 10.1021/acs.jproteome.0c00136 Schlicker, 2006, A new measure for functional similarity of gene products based on Gene Ontology, BMC Bioinformatics, 7, 302, 10.1186/1471-2105-7-302 Schwenk, 2017, The human plasma proteome draft of 2017: building on the human plasma PeptideAtlas from mass spectrometry and complementary assays, J. Proteome Res., 16, 4299, 10.1021/acs.jproteome.7b00467 Wang, 2018, Assembling the community-scale discoverable human proteome, Cell Syst., 7, 412, 10.1016/j.cels.2018.08.004 Samaras, 2020, ProteomicsDB: a multi-omics and multi-organism resource for life science research, Nucleic Acids Res., 48, D1153 Schmidt, 2018, ProteomicsDB, Nucleic Acids Res., 46, D1271, 10.1093/nar/gkx1029 Wilkinson, 2016, The FAIR Guiding Principles for scientific data management and stewardship, Sci. Data, 3, 160018, 10.1038/sdata.2016.18 Gessulat, 2019, Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning, Nat. Methods, 16, 509, 10.1038/s41592-019-0426-7 Brademan, 2019, Interactive peptide spectral annotator: a versatile web-based tool for proteomic applications, Mol. Cell Proteomics, 18, S193, 10.1074/mcp.TIR118.001209 Schwammle, 2015, Computational and statistical methods for high-throughput analysis of post-translational modifications of proteins, J. Proteomics, 129, 3, 10.1016/j.jprot.2015.07.016 Jorgensen, 2012, Analysing signalling networks by mass spectrometry, Amino Acids, 43, 1061, 10.1007/s00726-012-1293-z Perez-Riverol, 2018, Future Prospects of Spectral Clustering Approaches in Proteomics, Proteomics, 18, e1700454, 10.1002/pmic.201700454 Griss, 2016, Recognizing millions of consistently unidentified spectra across hundreds of shotgun proteomics datasets, Nat. Methods, 13, 651, 10.1038/nmeth.3902 Griss, 2019, Spectral clustering improves label-free quantification of low-abundant proteins, J. Proteome Res., 18, 1477, 10.1021/acs.jproteome.8b00377 The, 2020, Focus on the spectra that matter by clustering of quantification data in shotgun proteomics, Nat. Commun., 11, 3234, 10.1038/s41467-020-17037-3 Verschaffelt, 2020, MegaGO: a fast yet powerful approach to assess functional similarity across meta-omics data sets, bioRxiv