Literature DB >> 28299940

Epsilon-Q: An Automated Analyzer Interface for Mass Spectral Library Search and Label-Free Protein Quantification.

Jin-Young Cho1, Hyoung-Joo Lee1, Seul-Ki Jeong1, Young-Ki Paik1.   

Abstract

Mass spectrometry (MS) is a widely used proteome analysis tool for biomedical science. In an MS-based bottom-up proteomic approach to protein identification, sequence database (DB) searching has been routinely used because of its simplicity and convenience. However, searching a sequence DB with multiple variable modification options can increase processing time, false-positive errors in large and complicated MS data sets. Spectral library searching is an alternative solution, avoiding the limitations of sequence DB searching and allowing the detection of more peptides with high sensitivity. Unfortunately, this technique has less proteome coverage, resulting in limitations in the detection of novel and whole peptide sequences in biological samples. To solve these problems, we previously developed the "Combo-Spec Search" method, which uses manually multiple references and simulated spectral library searching to analyze whole proteomes in a biological sample. In this study, we have developed a new analytical interface tool called "Epsilon-Q" to enhance the functions of both the Combo-Spec Search method and label-free protein quantification. Epsilon-Q performs automatically multiple spectral library searching, class-specific false-discovery rate control, and result integration. It has a user-friendly graphical interface and demonstrates good performance in identifying and quantifying proteins by supporting standard MS data formats and spectrum-to-spectrum matching powered by SpectraST. Furthermore, when the Epsilon-Q interface is combined with the Combo-Spec search method, called the Epsilon-Q system, it shows a synergistic function by outperforming other sequence DB search engines for identifying and quantifying low-abundance proteins in biological samples. The Epsilon-Q system can be a versatile tool for comparative proteome analysis based on multiple spectral libraries and label-free quantification.

Keywords:  label-free quantification; mass spectrometry; proteomics; sequence database search; spectral library

Mesh:

Substances:

Year:  2017        PMID: 28299940     DOI: 10.1021/acs.jproteome.6b01019

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  5 in total

Review 1.  Advances in the Chromosome-Centric Human Proteome Project: looking to the future.

Authors:  Young-Ki Paik; Gilbert S Omenn; William S Hancock; Lydie Lane; Christopher M Overall
Journal:  Expert Rev Proteomics       Date:  2017-11-10       Impact factor: 3.940

Review 2.  Expanding the Use of Spectral Libraries in Proteomics.

Authors:  Eric W Deutsch; Yasset Perez-Riverol; Robert J Chalkley; Mathias Wilhelm; Stephen Tate; Timo Sachsenberg; Mathias Walzer; Lukas Käll; Bernard Delanghe; Sebastian Böcker; Emma L Schymanski; Paul Wilmes; Viktoria Dorfer; Bernhard Kuster; Pieter-Jan Volders; Nico Jehmlich; Johannes P C Vissers; Dennis W Wolan; Ana Y Wang; Luis Mendoza; Jim Shofstahl; Andrew W Dowsey; Johannes Griss; Reza M Salek; Steffen Neumann; Pierre-Alain Binz; Henry Lam; Juan Antonio Vizcaíno; Nuno Bandeira; Hannes Röst
Journal:  J Proteome Res       Date:  2018-10-11       Impact factor: 4.466

3.  Deep learning embedder method and tool for mass spectra similarity search.

Authors:  Chunyuan Qin; Xiyang Luo; Chuan Deng; Kunxian Shu; Weimin Zhu; Johannes Griss; Henning Hermjakob; Mingze Bai; Yasset Perez-Riverol
Journal:  J Proteomics       Date:  2020-12-08       Impact factor: 3.855

4.  Calibr improves spectral library search for spectrum-centric analysis of data independent acquisition proteomics.

Authors:  Jen-Hung Wang; Wai-Kok Choong; Ching-Tai Chen; Ting-Yi Sung
Journal:  Sci Rep       Date:  2022-02-07       Impact factor: 4.379

Review 5.  Review of Liquid Chromatography-Mass Spectrometry-Based Proteomic Analyses of Body Fluids to Diagnose Infectious Diseases.

Authors:  Hayoung Lee; Seung Il Kim
Journal:  Int J Mol Sci       Date:  2022-02-16       Impact factor: 5.923

  5 in total

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