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J Proteomics. 2015 Nov 3;129:25-32. doi: 10.1016/j.jprot.2015.07.006. Epub 2015 Jul 18.

ProteinInferencer: Confident protein identification and multiple experiment comparison for large scale proteomics projects.

Author information

1
Department of Chemical Physiology, The Scripps Research Institute, La Jolla, CA 92037, USA; Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China. Electronic address: zyy@sioc.ac.cn.
2
Department of Chemical Physiology, The Scripps Research Institute, La Jolla, CA 92037, USA; Dow AgroSciences LLC, Indianapolis, IN 46268, USA. Electronic address: txu2@dow.com.
3
Department of Chemical Physiology, The Scripps Research Institute, La Jolla, CA 92037, USA; Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China. Electronic address: bingshan@scripps.edu.
4
Department of Molecular & Experimental Medicine, The Scripps Research Institute, La Jolla, CA 92037, USA. Electronic address: jhart@scripps.edu.
5
Department of Chemical Physiology, The Scripps Research Institute, La Jolla, CA 92037, USA. Electronic address: aslanian@scripps.edu.
6
Department of Chemical Physiology, The Scripps Research Institute, La Jolla, CA 92037, USA. Electronic address: xmhan@scripps.edu.
7
NHLBI Proteomics Center at UCLA, Departments of Physiology and Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA. Electronic address: zchghno1@gmail.com.
8
NHLBI Proteomics Center at UCLA, Departments of Physiology and Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA. Electronic address: haomin_li@yahoo.com.
9
NHLBI Proteomics Center at UCLA, Departments of Physiology and Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA. Electronic address: cjh9595@ucla.edu.
10
Vanderbilt University Medical Center, Nashville, TN 37232, USA. Electronic address: DWang7@mdanderson.org.
11
Dow AgroSciences LLC, Indianapolis, IN 46268, USA. Electronic address: lacharya@dow.com.
12
Department of Molecular & Experimental Medicine, The Scripps Research Institute, La Jolla, CA 92037, USA. Electronic address: lisa13du@yahoo.com.
13
Department of Molecular & Experimental Medicine, The Scripps Research Institute, La Jolla, CA 92037, USA. Electronic address: pkvogt@scripps.edu.
14
NHLBI Proteomics Center at UCLA, Departments of Physiology and Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA. Electronic address: pping@mednet.ucla.edu.
15
Department of Chemical Physiology, The Scripps Research Institute, La Jolla, CA 92037, USA. Electronic address: jyates@scripps.edu.

Abstract

Shotgun proteomics generates valuable information from large-scale and target protein characterizations, including protein expression, protein quantification, protein post-translational modifications (PTMs), protein localization, and protein-protein interactions. Typically, peptides derived from proteolytic digestion, rather than intact proteins, are analyzed by mass spectrometers because peptides are more readily separated, ionized and fragmented. The amino acid sequences of peptides can be interpreted by matching the observed tandem mass spectra to theoretical spectra derived from a protein sequence database. Identified peptides serve as surrogates for their proteins and are often used to establish what proteins were present in the original mixture and to quantify protein abundance. Two major issues exist for assigning peptides to their originating protein. The first issue is maintaining a desired false discovery rate (FDR) when comparing or combining multiple large datasets generated by shotgun analysis and the second issue is properly assigning peptides to proteins when homologous proteins are present in the database. Herein we demonstrate a new computational tool, ProteinInferencer, which can be used for protein inference with both small- or large-scale data sets to produce a well-controlled protein FDR. In addition, ProteinInferencer introduces confidence scoring for individual proteins, which makes protein identifications evaluable. This article is part of a Special Issue entitled: Computational Proteomics.

KEYWORDS:

Database search; False discovery rate (FDR); Mass spectrometry; Peptide-spectrum match (PSM); Protein inference; Proteomics

PMID:
26196237
PMCID:
PMC4630118
DOI:
10.1016/j.jprot.2015.07.006
[Indexed for MEDLINE]
Free PMC Article

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