2016
DOI: 10.1021/acs.jproteome.6b00765
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Flexible Data Analysis Pipeline for High-Confidence Proteogenomics

Abstract: Proteogenomics leverages information derived from proteomic data to improve genome annotations. Of particular interest are “novel” peptides that provide direct evidence of protein expression for genomic regions not previously annotated as protein-coding. We present a modular, automated data analysis pipeline aimed at detecting such “novel” peptides in proteomic data sets. This pipeline implements criteria developed by proteomics and genome annotation experts for high-stringency peptide identification and filte… Show more

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Cited by 13 publications
(14 citation statements)
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“…are mostly designed to align peptide sequence (text input) to genomic sequences. Notable exceptions are tools like PGTools [30], PG Nexus [31], iPiG [32], proBAMsuite [33], and other pipelines (for example, the one described in [34]), which were designed not only to provide a genome browser-based visualization of MS-based peptide identifications, but in some cases also to provide scripts for data processing, peptide search, FDR calculations, among others, starting from complete datasets of raw MS files or PSM inputs, on a truly global proteomic approach. However, in our opinion some of such tools decreases the user freedom to apply search engines and approaches that they prefer rather than the one(s) employed at the referred tools (in addition, as we described above, PG Nexus, e.g.…”
Section: Discussionmentioning
confidence: 99%
“…are mostly designed to align peptide sequence (text input) to genomic sequences. Notable exceptions are tools like PGTools [30], PG Nexus [31], iPiG [32], proBAMsuite [33], and other pipelines (for example, the one described in [34]), which were designed not only to provide a genome browser-based visualization of MS-based peptide identifications, but in some cases also to provide scripts for data processing, peptide search, FDR calculations, among others, starting from complete datasets of raw MS files or PSM inputs, on a truly global proteomic approach. However, in our opinion some of such tools decreases the user freedom to apply search engines and approaches that they prefer rather than the one(s) employed at the referred tools (in addition, as we described above, PG Nexus, e.g.…”
Section: Discussionmentioning
confidence: 99%
“…All raw files were searched against a P. falciparum database using an OpenMS pipeline (34) containing the two search engines Mascot and MSGF+, followed by Percolator post-processing and phosphorylation analysis using PhosphoScoring, an implementation of the Ascore algorithm (35). Search parameters were: carbamidomethylation of cysteines was set as a fixed modification, oxidation of methionine, protein N-terminal acetylation, and STY phosphorylation were set as variable modifications.…”
Section: Methodsmentioning
confidence: 99%
“…OpenMS [57] workflow as described by Wright et al [39] and Weisser et al [58] The spectra were search against a sequence database composed of all GENCODE v27 protein coding transcripts and PhyloCSF Candidate Coding Regions [29]; an equally sized decoy database generated using DecoyPYrat [59] was concatenated and used to control FDR. Peptides were filtered to a posterior error probability of less than 0.01 and required to be significant in multiple search engines; a minimum and maximum length of 6 and 30 amino acids respectively was set; a maximum of 2 missed cleavages were allowed, and peptides containing certain modifications, such as deamidation were excluded.…”
Section: Identification Of Peptides Mapping To Orf-ymentioning
confidence: 99%