scholarly journals CsoDIAq Software for Direct Infusion Shotgun Proteome Analysis

2021 ◽  
Author(s):  
Caleb W Cranney ◽  
Jesse G Meyer
Keyword(s):  
Mass Spectrometry ◽  
Search Time ◽  
Proteome Analysis ◽  
Peptide Identification ◽  
Automated Analysis ◽  
Original Data ◽  
Mass Spectrometry Data ◽  
Direct Infusion ◽  
New Paradigm ◽  
Analysis Workflow

New mass spectrometry data collection methods require new computational tools. Direct Infusion Shotgun Proteome Analy-sis (DISPA) is a new paradigm for expedited mass spectrometry-based proteomics, but the original data analysis workflow was onerous. Here we introduce CsoDIAq, a user-friendly software package for the identification and quantification of pep-tides and proteins from DISPA data. In addition to establishing a complete and automated analysis workflow with a graph-ical user interface, CsoDIAq introduces algorithmic concepts to improve peptide identification speed and sensitivity. These include spectra pooling to reduce search time complexity, and a new spectrum-spectrum match score called match count and cosine (MaCC), which improves target discrimination in a target-decoy analysis. We further show that reanalysis after fragment mass tolerance correction increased the number of peptide identifications. Finally, we adapt CsoDIAq to standard LC-MS DIA, and show that it outperforms other spectrum-spectrum matching software.

scholarly journals A new processing scheme for ultra-high resolution direct infusion mass spectrometry data

2018 ◽  
Vol 178 ◽  
pp. 129-139 ◽  
Author(s):  
Arthur T. Zielinski ◽  
Ivan Kourtchev ◽  
Claudio Bortolini ◽  
Stephen J. Fuller ◽  
Chiara Giorio ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
High Resolution ◽  
Mass Spectrometry Data ◽  
Direct Infusion ◽  
Processing Scheme ◽  
Direct Infusion Mass Spectrometry ◽  
New Processing

Software for analysing ion mobility mass spectrometry data to improve peptide identification

PROTEOMICS ◽  
2012 ◽  
Vol 12 (12) ◽  
pp. 1912-1916 ◽  
Author(s):  
Dong Xia ◽  
Fawaz Ghali ◽  
Simon J. Gaskell ◽  
Ronan O'Cualain ◽  
Paul F. G. Sims ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Ion Mobility ◽  
Peptide Identification ◽  
Mass Spectrometry Data ◽  
Ion Mobility Mass Spectrometry

PhoPepMass: A database and search tool assisting human phosphorylation peptide identification from mass spectrometry data

2018 ◽  
Vol 45 (7) ◽  
pp. 381-388 ◽  
Author(s):  
Menghuan Zhang ◽  
Hui Cui ◽  
Lanming Chen ◽  
Ying Yu ◽  
Michael O. Glocker ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Peptide Identification ◽  
Mass Spectrometry Data ◽  
Search Tool

Comparison of Probability and Likelihood Models for Peptide Identification from Tandem Mass Spectrometry Data

2005 ◽  
Vol 4 (5) ◽  
pp. 1687-1698 ◽  
Author(s):  
William R. Cannon ◽  
Kristin H. Jarman ◽  
Bobbie-Jo M. Webb-Robertson ◽  
Douglas J. Baxter ◽  
Christopher S. Oehmen ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Tandem Mass Spectrometry ◽  
Peptide Identification ◽  
Mass Spectrometry Data ◽  
Tandem Mass ◽  
Tandem Mass Spectrometry Data

scholarly journals Clipper: p-value-free FDR control on high-throughput data from two conditions

2020 ◽  
Author(s):  
Xinzhou Ge ◽  
Yiling Elaine Chen ◽  
Dongyuan Song ◽  
MeiLu McDermott ◽  
Kyla Woyshner ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
High Throughput ◽  
Peptide Identification ◽  
Biological Data ◽  
Mass Spectrometry Data ◽  
P Values ◽  
High Throughput Data ◽  
Bioinformatics Tools ◽  
Biological Studies

AbstractHigh-throughput biological data analysis commonly involves identifying “interesting” features (e.g., genes, genomic regions, and proteins), whose values differ between two conditions, from numerous features measured simultaneously. The most widely-used criterion to ensure the analysis reliability is the false discovery rate (FDR), the expected proportion of uninteresting features among the identified ones. Existing bioinformatics tools primarily control the FDR based on p-values. However, obtaining valid p-values relies on either reasonable assumptions of data distribution or large numbers of replicates under both conditions, two requirements that are often unmet in biological studies. To address this issue, we propose Clipper, a general statistical framework for FDR control without relying on p-values or specific data distributions. Clipper is applicable to identifying both enriched and differential features from high-throughput biological data of diverse types. In comprehensive simulation and real-data benchmarking, Clipper outperforms existing generic FDR control methods and specific bioinformatics tools designed for various tasks, including peak calling from ChIP-seq data, differentially expressed gene identification from RNA-seq data, differentially interacting chromatin region identification from Hi-C data, and peptide identification from mass spectrometry data. Notably, our benchmarking results for peptide identification are based on the first mass spectrometry data standard with a realistic dynamic range. Our results demonstrate Clipper’s flexibility and reliability for FDR control, as well as its broad applications in high-throughput data analysis.

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