In-depth analysis of protein inference algorithms using multiple search engines and well-defined metrics.
暂无分享,去创建一个
Martin Eisenacher | Yasset Perez-Riverol | Knut Reinert | Henning Hermjakob | Oliver Kohlbacher | Julian Uszkoreit | Timo Sachsenberg | David L Tabb | Julianus Pfeuffer | Xiao Liang | Enrique Audain | Aniel Sanchez | K. Reinert | H. Hermjakob | O. Kohlbacher | D. Tabb | Timo Sachsenberg | J. Pfeuffer | M. Eisenacher | Yasset Pérez-Riverol | Aniel Sánchez | E. Audain | J. Uszkoreit | X. Liang
[1] William Stafford Noble,et al. Efficient marginalization to compute protein posterior probabilities from shotgun mass spectrometry data. , 2010, Journal of proteome research.
[2] K. Resing,et al. IsoformResolver: A Peptide-Centric Algorithm for Protein Inference , 2011, Journal of proteome research.
[3] M. Mann,et al. Andromeda: a peptide search engine integrated into the MaxQuant environment. , 2011, Journal of proteome research.
[4] John R Yates,et al. Search engine processor: Filtering and organizing peptide spectrum matches , 2012, Proteomics.
[5] Markus Müller,et al. In silico analysis of accurate proteomics, complemented by selective isolation of peptides. , 2011, Journal of proteomics.
[6] B. Searle. Scaffold: A bioinformatic tool for validating MS/MS‐based proteomic studies , 2010, Proteomics.
[7] Markus Müller,et al. Isoelectric point optimization using peptide descriptors and support vector machines. , 2012, Journal of proteomics.
[8] Juan Antonio Vizcaíno,et al. HI-bone: a scoring system for identifying phenylisothiocyanate-derivatized peptides based on precursor mass and high intensity fragment ions. , 2013, Analytical chemistry.
[9] Zengyou He,et al. Protein inference: a review , 2012, Briefings Bioinform..
[10] Lennart Martens,et al. Bioinformatics challenges in mass spectrometry-driven proteomics. , 2011, Methods in molecular biology.
[11] Oliver Kohlbacher,et al. Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics , 2007, BMC Bioinformatics.
[12] J. Yates,et al. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database , 1994, Journal of the American Society for Mass Spectrometry.
[13] Robert Burke,et al. ProteoWizard: open source software for rapid proteomics tools development , 2008, Bioinform..
[14] Eystein Oveland,et al. PeptideShaker enables reanalysis of MS-derived proteomics data sets , 2015, Nature Biotechnology.
[15] Juan Antonio Vizcaíno,et al. A survey of molecular descriptors used in mass spectrometry based proteomics. , 2014, Current topics in medicinal chemistry.
[16] J. Buhmann,et al. Generic Comparison of Protein Inference Engines* , 2011, Molecular & Cellular Proteomics.
[17] Juan Antonio Vizcaíno,et al. ms-data-core-api: an open-source, metadata-oriented library for computational proteomics , 2015, Bioinform..
[18] Knut Reinert,et al. OpenMS and TOPP: open source software for LC-MS data analysis. , 2011, Methods in molecular biology.
[19] J. Buhmann,et al. Protein Identification False Discovery Rates for Very Large Proteomics Data Sets Generated by Tandem Mass Spectrometry* , 2009, Molecular & Cellular Proteomics.
[20] Oliver Serang. Concerning the accuracy of Fido and parameter choice , 2013, Bioinform..
[21] K. Gevaert,et al. SCX charge state selective separation of tryptic peptides combined with 2D-RP-HPLC allows for detailed proteome mapping. , 2013, Journal of proteomics.
[22] Chris F. Taylor,et al. A common open representation of mass spectrometry data and its application to proteomics research , 2004, Nature Biotechnology.
[23] A. Nesvizhskii,et al. Metrics for the Human Proteome Project 2015: Progress on the Human Proteome and Guidelines for High-Confidence Protein Identification. , 2015, Journal of proteome research.
[24] R. Aebersold,et al. A statistical model for identifying proteins by tandem mass spectrometry. , 2003, Analytical chemistry.
[25] William Stafford Noble,et al. Assigning significance to peptides identified by tandem mass spectrometry using decoy databases. , 2008, Journal of proteome research.
[26] William Stafford Noble,et al. Improved False Discovery Rate Estimation Procedure for Shotgun Proteomics , 2015, Journal of proteome research.
[27] Akhilesh Pandey,et al. Proteogenomic analysis of human chromosome 9-encoded genes from human samples and lung cancer tissues. , 2014, Journal of proteome research.
[28] Olga Vitek,et al. A statistical model-building perspective to identification of MS/MS spectra with PeptideProphet , 2012, BMC Bioinformatics.
[29] Natalie I. Tasman,et al. iProphet: Multi-level Integrative Analysis of Shotgun Proteomic Data Improves Peptide and Protein Identification Rates and Error Estimates* , 2011, Molecular & Cellular Proteomics.
[30] Benjamin A. Garcia,et al. Evaluation of Proteomic Search Engines for the Analysis of Histone Modifications , 2014, Journal of proteome research.
[31] Jun Fan,et al. The mzTab Data Exchange Format: Communicating Mass-spectrometry-based Proteomics and Metabolomics Experimental Results to a Wider Audience* , 2014, Molecular & Cellular Proteomics.
[32] M. Mann,et al. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification , 2008, Nature Biotechnology.
[33] Lukas Käll,et al. Mass fingerprinting of complex mixtures: protein inference from high-resolution peptide masses and predicted retention times. , 2013, Journal of proteome research.
[34] John R Yates,et al. Validation of Tandem Mass Spectrometry Database Search Results Using DTASelect , 2006, Current protocols in bioinformatics.
[35] Thorsten Meinl,et al. KNIME: The Konstanz Information Miner , 2007, GfKl.
[36] Lukas Käll,et al. Solution to Statistical Challenges in Proteomics Is More Statistics, Not Less. , 2015, Journal of proteome research.
[37] Predrag Radivojac,et al. Computational approaches to protein inference in shotgun proteomics , 2012, BMC Bioinformatics.
[38] Norman W. Paton,et al. Improving sensitivity in proteome studies by analysis of false discovery rates for multiple search engines , 2009, Proteomics.
[39] Gabriel Padrón,et al. Peptide fractionation by acid pH SDS‐free electrophoresis , 2011, Electrophoresis.
[40] Predrag Radivojac,et al. The importance of peptide detectability for protein identification, quantification, and experiment design in MS/MS proteomics. , 2010, Journal of proteome research.
[41] O. Kohlbacher,et al. Probabilistic consensus scoring improves tandem mass spectrometry peptide identification. , 2011, Journal of proteome research.
[42] Yasset Perez-Riverol,et al. Bioinformatics tools for the functional interpretation of quantitative proteomics results. , 2014, Current topics in medicinal chemistry.
[43] Gabriel Padrón,et al. Proteomics based on peptide fractionation by SDS-free PAGE. , 2008, Journal of proteome research.
[44] Martin Eisenacher,et al. A standardized framing for reporting protein identifications in mzIdentML 1.2 , 2014, Proteomics.
[45] Martin Eisenacher,et al. PRIDE Inspector Toolsuite: Moving Toward a Universal Visualization Tool for Proteomics Data Standard Formats and Quality Assessment of ProteomeXchange Datasets , 2015, Molecular & Cellular Proteomics.
[46] P. Pevzner,et al. False discovery rates of protein identifications: a strike against the two-peptide rule. , 2009, Journal of proteome research.
[47] J. Eng,et al. Comet: An open‐source MS/MS sequence database search tool , 2013, Proteomics.
[48] D. N. Perkins,et al. Probability‐based protein identification by searching sequence databases using mass spectrometry data , 1999, Electrophoresis.
[49] Pavel A. Pevzner,et al. Universal database search tool for proteomics , 2014, Nature Communications.
[50] Eric W. Deutsch,et al. Combining Results of Multiple Search Engines in Proteomics* , 2013, Molecular & Cellular Proteomics.
[51] Knut Reinert,et al. OpenMS – An open-source software framework for mass spectrometry , 2008, BMC Bioinformatics.
[52] Martin Eisenacher,et al. PIA: An Intuitive Protein Inference Engine with a Web-Based User Interface. , 2015, Journal of proteome research.
[53] Yasset Perez-Riverol,et al. Open source libraries and frameworks for mass spectrometry based proteomics: A developer's perspective , 2014, Biochimica et biophysica acta.
[54] A. Heck,et al. Next-generation proteomics: towards an integrative view of proteome dynamics , 2012, Nature Reviews Genetics.
[55] Robertson Craig,et al. TANDEM: matching proteins with tandem mass spectra. , 2004, Bioinformatics.
[56] Edward L. Huttlin,et al. A mass-tolerant database search identifies a large proportion of unassigned spectra in shotgun proteomics as modified peptides , 2015, Nature Biotechnology.
[57] Lennart Martens,et al. Computational proteomics pitfalls and challenges: HavanaBioinfo 2012 workshop report. , 2013, Journal of proteomics.
[58] Steven P Gygi,et al. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry , 2007, Nature Methods.
[59] Kai A Reidegeld,et al. An easy‐to‐use Decoy Database Builder software tool, implementing different decoy strategies for false discovery rate calculation in automated MS/MS protein identifications , 2008, Proteomics.
[60] Knut Reinert,et al. OpenMS and TOPP: Open Source Software for LC-MS Data Analysis , 2010, Proteome Bioinformatics.
[61] A. Nesvizhskii. A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics. , 2010, Journal of proteomics.
[62] Zengyou He,et al. A linear programming model for protein inference problem in shotgun proteomics , 2012, Bioinform..