Abstract
Abstract: Liquid chromatography based mass spectrometry (LC-MS) is a key technology for analyzing highly complex anddynamic proteome samples. With highly accurate and sensitive LC-MS analysis of complex proteome samples, efficient dataprocessing is another critical issue to obtain more information from LC-MS data. A typical proteomic data processing starts wit hprotein database search engine which assigns peptide sequences to MS/MS spectra and finds proteins. Although several searchengines, such as SEQUEST and MASCOT, have been widely used, there is no unique standard way to interpret MS/MS spectraof peptides. Each search engine has pros and cons depending on types of mass spectrometers and physicochemical properties ofpeptides. In this study, we describe a novel data process pipeline which identifies more peptides and proteins by correcting pre-cursor ion mass numbers and unifying multi search engines results. The pipeline utilizes two open-source software, i PE-MMRfor mass number correction, and iProphet to combine several search results. The integrated pipeline identified 25% more pro-teins in mouse epididymal adipose tissue compared with the conventional method. Also the pipeline was validated using controland colitis induced colon tissue. The results of the present study shows that the integrated pipeline can efficiently identifyincreased number of proteins compared to the conventional method which can be a breakthrough in identification of a potentialbiomarker candidate.Key words: iPE-MMR, iProphet, Q-TOF, TPP
Highlights
Proteomics aims at comprehensive profiling of protein in tissues or cells utilizing various technical platforms such as proteome separation techniques, mass spectrometry (MS), and bioinformatics tools for data processing
The results of the present study shows that the integrated pipeline can efficiently identify increased number of proteins compared to the conventional method which can be a breakthrough in identification of a potential biomarker candidate
All spectra were searched against International Protein Index (IPI) using three different search engines - SEQUEST, X!Tandem and MASCOT
Summary
Proteomics aims at comprehensive profiling of protein in tissues or cells utilizing various technical platforms such as proteome separation techniques, mass spectrometry (MS), and bioinformatics tools for data processing. Under this license, authors reserve the copyright for their content; they permit anyone to unrestrictedly use, distribute, and reproduce the content in any medium as far as the original authors and source are cited. The above process faces two typical (1) error of monoisotopic mass determination and (2) loss of information due to single database search.[1] Assignments of precise precursor ion masses to MS/MS spectra is frequently debatable even when using high resolution mass spectrometers. The second problem can be combated with the help of various advanced
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