Recent advances in mass-spectrometry based proteomics software, tools and databases.
暂无分享,去创建一个
Ankit Halder | Ayushi Verma | Deeptarup Biswas | Sanjeeva Srivastava | Sanjeeva Srivastava | Deeptarup Biswas | A. Verma | Ankit Halder
[1] Chunjie Luo,et al. pDeep: Predicting MS/MS Spectra of Peptides with Deep Learning. , 2017, Analytical chemistry.
[2] Juhani Aakko,et al. A Data Analysis Protocol for Quantitative Data-Independent Acquisition Proteomics. , 2018, Methods in molecular biology.
[3] D. Park,et al. Proteogenomic Characterization of Human Early-Onset Gastric Cancer. , 2019, Cancer cell.
[4] Shisheng Wang,et al. pseudoQC: A Regression‐Based Simulation Software for Correction and Normalization of Complex Metabolomics and Proteomics Datasets , 2019, Proteomics.
[5] Lennart Martens,et al. PRIDE: The proteomics identifications database , 2005, Proteomics.
[6] Christoph B. Messner,et al. DIA-NN: Neural networks and interference correction enable deep proteome coverage in high throughput , 2019, Nature Methods.
[7] Nichole L. King,et al. The PeptideAtlas Project , 2010, Proteome Bioinformatics.
[8] Luis Mendoza,et al. Processing Shotgun Proteomics Data on the Amazon Cloud with the Trans-Proteomic Pipeline* , 2014, Molecular & Cellular Proteomics.
[9] Saicharan Ghantasala,et al. An Integrated Quantitative Proteomics Workflow for Cancer Biomarker Discovery and Validation in Plasma , 2020, Frontiers in Oncology.
[10] Jesper V Olsen,et al. Rapid and deep proteomes by faster sequencing on a benchtop quadrupole ultra-high-field Orbitrap mass spectrometer. , 2014, Journal of proteome research.
[11] Chih-Chiang Tsou,et al. DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics , 2015, Nature Methods.
[12] Luis Mendoza,et al. Trans‐Proteomic Pipeline, a standardized data processing pipeline for large‐scale reproducible proteomics informatics , 2015, Proteomics. Clinical applications.
[13] Matus Medo,et al. ProtRank: bypassing the imputation of missing values in differential expression analysis of proteomic data , 2019, BMC Bioinformatics.
[14] Mehdi Mesri,et al. Connecting genomic alterations to cancer biology with proteomics: the NCI Clinical Proteomic Tumor Analysis Consortium. , 2013, Cancer discovery.
[15] Xi Chen,et al. QuantPipe: A User-Friendly Pipeline Software Tool for DIA Data Analysis Based on the OpenSWATH-PyProphet-TRIC Workflow. , 2020, Journal of proteome research.
[16] Oliver M. Bernhardt,et al. Extending the Limits of Quantitative Proteome Profiling with Data-Independent Acquisition and Application to Acetaminophen-Treated Three-Dimensional Liver Microtissues* , 2015, Molecular & Cellular Proteomics.
[17] Yuanyue Li,et al. Group-DIA: analyzing multiple data-independent acquisition mass spectrometry data files , 2015, Nature Methods.
[18] Bin Zhang,et al. PhosphoSitePlus, 2014: mutations, PTMs and recalibrations , 2014, Nucleic Acids Res..
[19] Lukas Krasny,et al. Data-independent acquisition mass spectrometry (DIA-MS) for proteomic applications in oncology. , 2020, Molecular omics.
[20] Michael J MacCoss,et al. Thesaurus: quantifying phosphopeptide positional isomers , 2019, Nature Methods.
[21] Haiyan Tan,et al. JUMP: A Tag-based Database Search Tool for Peptide Identification with High Sensitivity and Accuracy* , 2014, Molecular & Cellular Proteomics.
[22] M. Mann,et al. Deep and Highly Sensitive Proteome Coverage by LC-MS/MS Without Prefractionation* , 2011, Molecular & Cellular Proteomics.
[23] V. Marx. Targeted proteomics , 2013, Nature Methods.
[24] Hongmei Lu,et al. Deep MS/MS-Aided Structural-Similarity Scoring for Unknown Metabolite Identification. , 2019, Analytical chemistry.
[25] Ruedi Aebersold,et al. Quantitative proteomics by stable isotope labeling and mass spectrometry. , 2007, Methods in molecular biology.
[26] Qi Zhao,et al. qPhos: a database of protein phosphorylation dynamics in humans , 2018, Nucleic Acids Res..
[27] Subha Madhavan,et al. Proteogenomic Analysis of Human Colon Cancer Reveals New Therapeutic Opportunities , 2019, Cell.
[28] Robert Petryszak,et al. Discovering and linking public omics data sets using the Omics Discovery Index , 2017, Nature Biotechnology.
[29] Lisa M. Chung,et al. Review of software tools for design and analysis of large scale MRM proteomic datasets. , 2013, Methods.
[30] Jingchun Chen,et al. ATAQS: A computational software tool for high throughput transition optimization and validation for selected reaction monitoring mass spectrometry , 2011, BMC Bioinformatics.
[31] Paolo Cifani,et al. ProteomeGenerator: A framework for comprehensive proteomics based on de novo transcriptome assembly and high-accuracy peptide mass spectral matching , 2017, bioRxiv.
[32] Mathias Wilhelm,et al. Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning , 2019, Nature Methods.
[33] J. Yates,et al. Isobaric Labeling-Based Relative Quantification in Shotgun Proteomics , 2014, Journal of proteome research.
[34] Rebekah L. Gundry,et al. A high-stringency blueprint of the human proteome , 2020, Nature Communications.
[35] P. Wild,et al. Missing value imputation in proximity extension assay-based targeted proteomics data , 2020, PloS one.
[36] Yasset Perez-Riverol,et al. MassIVE.quant: a community resource of quantitative mass spectrometry-based proteomics datasets , 2020, Nature Methods.
[37] Marco Y. Hein,et al. The Perseus computational platform for comprehensive analysis of (prote)omics data , 2016, Nature Methods.
[38] B. F. Francis Ouellette,et al. ActiveDriverDB: human disease mutations and genome variation in post-translational modification sites of proteins , 2017, bioRxiv.
[39] Cheng Chang,et al. In-depth method assessments of differentially expressed protein detection for shotgun proteomics data with missing values , 2017, Scientific Reports.
[40] John R Yates,et al. Recent technical advances in proteomics , 2019, F1000Research.
[41] Samuel H Payne,et al. PECAN: Library Free Peptide Detection for Data-Independent Acquisition Tandem Mass Spectrometry Data , 2017, Nature Methods.
[42] Xue Cai,et al. Data‐Independent Acquisition Mass Spectrometry‐Based Proteomics and Software Tools: A Glimpse in 2020 , 2020, Proteomics.
[43] Xiaohui Liu,et al. In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics , 2020, Nature Communications.
[44] M. Albert,et al. Phosphotyrosine profiling of human cerebrospinal fluid , 2018, Clinical Proteomics.
[45] Michael Riffle,et al. Proteomics data repositories , 2009, Proteomics.
[46] Eva Friedel,et al. Simulating ComBat: how batch correction can lead to the systematic introduction of false positive results in DNA methylation microarray studies , 2020, BMC Bioinformatics.
[47] Bin Ma,et al. PEAKS DB: De Novo Sequencing Assisted Database Search for Sensitive and Accurate Peptide Identification* , 2011, Molecular & Cellular Proteomics.
[48] P. Pevzner,et al. InsPecT: identification of posttranslationally modified peptides from tandem mass spectra. , 2005, Analytical chemistry.
[49] Lennart Martens,et al. Database Search Engines: Paradigms, Challenges and Solutions. , 2016, Advances in experimental medicine and biology.
[50] Lennart Martens,et al. ProteoCloud: a full-featured open source proteomics cloud computing pipeline. , 2013, Journal of proteomics.
[51] A. Makarov,et al. The Orbitrap: a new mass spectrometer. , 2005, Journal of mass spectrometry : JMS.
[52] Ronald J. Moore,et al. Reproducible workflow for multiplexed deep-scale proteome and phosphoproteome analysis of tumor tissues by liquid chromatography–mass spectrometry , 2018, Nature Protocols.
[53] Florian Gnad,et al. PHOSIDA 2011: the posttranslational modification database , 2010, Nucleic Acids Res..
[54] Amos Bairoch,et al. The neXtProt knowledgebase in 2020: data, tools and usability improvements , 2019, Nucleic Acids Res..
[55] Jüergen Cox,et al. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics , 2016, Nature Protocols.
[56] Lennart Martens,et al. Anatomy and evolution of database search engines-a central component of mass spectrometry based proteomic workflows. , 2020, Mass spectrometry reviews.
[57] Conrad Bessant,et al. MRMaid: The SRM Assay Design Tool for Arabidopsis and Other Species , 2012, Front. Plant Sci..
[58] Edward L. Huttlin,et al. An ultra-tolerant database search reveals that a myriad of modified peptides contributes to unassigned spectra in shotgun proteomics , 2015, Nature Biotechnology.
[59] David S. Wishart,et al. MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis , 2018, Nucleic Acids Res..
[60] Brendan MacLean,et al. Bioinformatics Applications Note Gene Expression Skyline: an Open Source Document Editor for Creating and Analyzing Targeted Proteomics Experiments , 2022 .
[61] George E Karniadakis,et al. Omics, big data and machine learning as tools to propel understanding of biological mechanisms and to discover novel diagnostics and therapeutics. , 2018, Metabolism: clinical and experimental.
[62] Jingqiu Cheng,et al. NAguideR: performing and prioritizing missing value imputations for consistent bottom-up proteomic analyses , 2020, Nucleic acids research.
[63] Jeffrey R. Whiteaker,et al. Proteogenomic Characterization Reveals Therapeutic Vulnerabilities in Lung Adenocarcinoma , 2020, Cell.
[64] B. Searle,et al. “Plug-and-play” investigation of the human phosphoproteome by targeted high-resolution mass spectrometry , 2016, Nature Methods.
[65] Nelson Perdigão,et al. Dark Proteome Database: Studies on Dark Proteins , 2019, High-throughput.
[66] Philipp E. Geyer,et al. Ultra-deep and quantitative saliva proteome reveals dynamics of the oral microbiome , 2016, Genome Medicine.
[67] Ben C. Collins,et al. OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data , 2014, Nature Biotechnology.
[68] Ivan Merelli,et al. High-Performance Computing and Big Data in Omics-Based Medicine , 2014, BioMed research international.
[69] Martin Kircher,et al. Deep proteome and transcriptome mapping of a human cancer cell line , 2011, Molecular systems biology.
[70] Ngoc Hieu Tran,et al. Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry , 2018, Nature Methods.
[71] Manuel A. S. Santos,et al. De novo sequencing of proteins by mass spectrometry , 2020, Expert review of proteomics.
[72] Hsien-Da Huang,et al. dbPTM in 2019: exploring disease association and cross-talk of post-translational modifications , 2018, Nucleic Acids Res..
[73] Jianlin Cheng,et al. Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis , 2020, International journal of molecular sciences.
[74] Juan Antonio Vizcaíno,et al. The ProteomeXchange consortium in 2017: supporting the cultural change in proteomics public data deposition , 2016, Nucleic Acids Res..
[75] Gary D Bader,et al. A draft map of the human proteome , 2014, Nature.
[76] Normalization of mass spectrometry data (NOMAD). , 2017, Advances in biological regulation.
[77] Fredrik Levander,et al. Normalyzer: A Tool for Rapid Evaluation of Normalization Methods for Omics Data Sets , 2014, Journal of proteome research.
[78] David L Tabb,et al. DirecTag: accurate sequence tags from peptide MS/MS through statistical scoring. , 2008, Journal of proteome research.
[79] Nuno Bandeira,et al. Human Proteome Project Mass Spectrometry Data Interpretation Guidelines 3.0. , 2019, Journal of proteome research.
[80] A. Marshall,et al. High-resolution mass spectrometers. , 2008, Annual review of analytical chemistry.