Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis

Recent advances in mass spectrometry (MS)-based proteomics have enabled tremendous progress in the understanding of cellular mechanisms, disease progression, and the relationship between genotype and phenotype. Though many popular bioinformatics methods in proteomics are derived from other omics stu...

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Bibliographic Details
Published inInternational journal of molecular sciences Vol. 21; no. 8; p. 2873
Main Authors Chen, Chen, Hou, Jie, Tanner, John J., Cheng, Jianlin
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 20.04.2020
MDPI
Subjects
Accuracy
Algorithms
Bioinformatics
Biomedical research
Computational Biology - methods
Data Analysis
Humans
Identification
Machine Learning
Mass Spectrometry
Peptides
Protein Interaction Mapping
Protein Interaction Maps
Proteins
Proteomics
Proteomics - statistics & numerical data
Review
Scientific imaging
Search engines
Search strategies
Software
Support vector machines
Workflow
machine learning
bioinformatics analysis
computational proteomics
Online AccessGet full text
ISSN1422-0067
1661-6596
1422-0067
DOI10.3390/ijms21082873

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Abstract Recent advances in mass spectrometry (MS)-based proteomics have enabled tremendous progress in the understanding of cellular mechanisms, disease progression, and the relationship between genotype and phenotype. Though many popular bioinformatics methods in proteomics are derived from other omics studies, novel analysis strategies are required to deal with the unique characteristics of proteomics data. In this review, we discuss the current developments in the bioinformatics methods used in proteomics and how they facilitate the mechanistic understanding of biological processes. We first introduce bioinformatics software and tools designed for mass spectrometry-based protein identification and quantification, and then we review the different statistical and machine learning methods that have been developed to perform comprehensive analysis in proteomics studies. We conclude with a discussion of how quantitative protein data can be used to reconstruct protein interactions and signaling networks.
AbstractList Recent advances in mass spectrometry (MS)-based proteomics have enabled tremendous progress in the understanding of cellular mechanisms, disease progression, and the relationship between genotype and phenotype. Though many popular bioinformatics methods in proteomics are derived from other omics studies, novel analysis strategies are required to deal with the unique characteristics of proteomics data. In this review, we discuss the current developments in the bioinformatics methods used in proteomics and how they facilitate the mechanistic understanding of biological processes. We first introduce bioinformatics software and tools designed for mass spectrometry-based protein identification and quantification, and then we review the different statistical and machine learning methods that have been developed to perform comprehensive analysis in proteomics studies. We conclude with a discussion of how quantitative protein data can be used to reconstruct protein interactions and signaling networks.
Recent advances in mass spectrometry (MS)-based proteomics have enabled tremendous progress in the understanding of cellular mechanisms, disease progression, and the relationship between genotype and phenotype. Though many popular bioinformatics methods in proteomics are derived from other omics studies, novel analysis strategies are required to deal with the unique characteristics of proteomics data. In this review, we discuss the current developments in the bioinformatics methods used in proteomics and how they facilitate the mechanistic understanding of biological processes. We first introduce bioinformatics software and tools designed for mass spectrometry-based protein identification and quantification, and then we review the different statistical and machine learning methods that have been developed to perform comprehensive analysis in proteomics studies. We conclude with a discussion of how quantitative protein data can be used to reconstruct protein interactions and signaling networks.Recent advances in mass spectrometry (MS)-based proteomics have enabled tremendous progress in the understanding of cellular mechanisms, disease progression, and the relationship between genotype and phenotype. Though many popular bioinformatics methods in proteomics are derived from other omics studies, novel analysis strategies are required to deal with the unique characteristics of proteomics data. In this review, we discuss the current developments in the bioinformatics methods used in proteomics and how they facilitate the mechanistic understanding of biological processes. We first introduce bioinformatics software and tools designed for mass spectrometry-based protein identification and quantification, and then we review the different statistical and machine learning methods that have been developed to perform comprehensive analysis in proteomics studies. We conclude with a discussion of how quantitative protein data can be used to reconstruct protein interactions and signaling networks.
Author Chen, Chen
Cheng, Jianlin
Hou, Jie
Tanner, John J.
AuthorAffiliation 2 Department of Computer Science, Saint Louis University, St. Louis, MO 63103, USA; jie.hou@slu.edu
3 Program in Bioinformatics & Computational Biology, Saint Louis University, St. Louis, MO 63103, USA
1 Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA; ccm3x@mail.missouri.edu
4 Departments of Biochemistry and Chemistry, University of Missouri, Columbia, MO 65211, USA; tannerjj@missouri.edu
AuthorAffiliation_xml – name: 2 Department of Computer Science, Saint Louis University, St. Louis, MO 63103, USA; jie.hou@slu.edu
– name: 1 Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA; ccm3x@mail.missouri.edu
– name: 4 Departments of Biochemistry and Chemistry, University of Missouri, Columbia, MO 65211, USA; tannerjj@missouri.edu
– name: 3 Program in Bioinformatics & Computational Biology, Saint Louis University, St. Louis, MO 63103, USA
Author_xml – sequence: 1
  givenname: Chen
  orcidid: 0000-0002-2973-461X
  surname: Chen
  fullname: Chen, Chen
– sequence: 2
  givenname: Jie
  surname: Hou
  fullname: Hou, Jie
– sequence: 3
  givenname: John J.
  orcidid: 0000-0001-8314-113X
  surname: Tanner
  fullname: Tanner, John J.
– sequence: 4
  givenname: Jianlin
  surname: Cheng
  fullname: Cheng, Jianlin
BackLink https://www.ncbi.nlm.nih.gov/pubmed/32326049$$D View this record in MEDLINE/PubMed
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Snippet Recent advances in mass spectrometry (MS)-based proteomics have enabled tremendous progress in the understanding of cellular mechanisms, disease progression,...
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SubjectTerms Accuracy
Algorithms
Bioinformatics
Biomedical research
Computational Biology - methods
Data Analysis
Humans
Identification
Machine Learning
Mass Spectrometry
Peptides
Protein Interaction Mapping
Protein Interaction Maps
Proteins
Proteomics
Proteomics - statistics & numerical data
Review
Scientific imaging
Search engines
Search strategies
Software
Support vector machines
Workflow
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Title Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis
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