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Multi-Q 2 software facilitates isobaric labeling quantitation analysis with improved accuracy and coverage

Authors :
Ching-Tai Chen
Jen-Hung Wang
Cheng-Wei Cheng
Wei-Che Hsu
Chu-Ling Ko
Wai-Kok Choong
Ting-Yi Sung
Source :
Scientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
Publication Year :
2021
Publisher :
Nature Portfolio, 2021.

Abstract

Abstract Mass spectrometry-based proteomics using isobaric labeling for multiplex quantitation has become a popular approach for proteomic studies. We present Multi-Q 2, an isobaric-labeling quantitation tool which can yield the largest quantitation coverage and improved quantitation accuracy compared to three state-of-the-art methods. Multi-Q 2 supports identification results from several popular proteomic data analysis platforms for quantitation, offering up to 12% improvement in quantitation coverage for accepting identification results from multiple search engines when compared with MaxQuant and PatternLab. It is equipped with various quantitation algorithms, including a ratio compression correction algorithm, and results in up to 336 algorithmic combinations. Systematic evaluation shows different algorithmic combinations have different strengths and are suitable for different situations. We also demonstrate that the flexibility of Multi-Q 2 in customizing algorithmic combination can lead to improved quantitation accuracy over existing tools. Moreover, the use of complementary algorithmic combinations can be an effective strategy to enhance sensitivity when searching for biomarkers from differentially expressed proteins in proteomic experiments. Multi-Q 2 provides interactive graphical interfaces to process quantitation and to display ratios at protein, peptide, and spectrum levels. It also supports a heatmap module, enabling users to cluster proteins based on their abundance ratios and to visualize the clustering results. Multi-Q 2 executable files, sample data sets, and user manual are freely available at http://ms.iis.sinica.edu.tw/COmics/Software_Multi-Q2.html .

Subjects

Subjects :
Medicine
Science

Details

Language :
English
ISSN :
20452322
Volume :
11
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Scientific Reports
Publication Type :
Academic Journal
Accession number :
edsdoj.b627d80c53c64e4898b25d0d9adcf2e2
Document Type :
article
Full Text :
https://doi.org/10.1038/s41598-021-81740-4