AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Article Link
Collect
Submit Manuscript
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Regular Paper

SEMQuant: A Computational Platform for Accurate and Comprehensive Quantitative Mewtaproteomics Analysis

Department of Computer Science and Engineering, University of North Texas, Texas 76201, U.S.A.
School of Biological Sciences, University of Oklahoma, Oklahoma 73019, U.S.A.
School of Computer Science, University of Oklahoma, Oklahoma 73019, U.S.A.
Show Author Information

Abstract

Metaproteomics, utilizing high-throughput liquid chromatography-mass spectrometry (LC-MS), offers a profound understanding of microbial communities. Quantitative metaproteomics further enriches this understanding by measuring relative protein abundance and revealing dynamic changes under different conditions. However, the challenge of missing peptide quantification persists in metaproteomics analysis, particularly in data-dependent acquisition (DDA) mode, where high-intensity precursors for MS2 scans are selected. To tackle this issue, the match-between-runs (MBR) technique is used to transfer peptides between LC-MS runs. Inspired by the benefits of MBR and the need for streamlined metaproteomics data analysis, we develop SEMQuant, an end-to-end software integrating Sipros-Ensemble's robust peptide identifications with IonQuant's MBR function. The experiments show that SEMQuant consistently obtains the highest or second highest number of quantified proteins, with notable precision and accuracy. This demonstrates SEMQuant's effectiveness in conducting comprehensive and accurate quantitative metaproteomics analyses across diverse datasets and highlights its potential to propel advancements in microbial community studies. SEMQuant is freely available under the GNU GPL license at https://github.com/Biocomputing-Research-Group/SEMQuant.

Electronic Supplementary Material

Download File(s)
JCST-2412-15122-Highlights.pdf (239.3 KB)

References

【1】
【1】
 
 
Journal of Computer Science and Technology
Pages 1087-1100

{{item.num}}

Comments on this article

Go to comment

< Back to all reports

Review Status: {{reviewData.commendedNum}} Commended , {{reviewData.revisionRequiredNum}} Revision Required , {{reviewData.notCommendedNum}} Not Commended Under Peer Review

Review Comment

Close
Close
Cite this article:
Zhang B, Feng S, Xiong Y, et al. SEMQuant: A Computational Platform for Accurate and Comprehensive Quantitative Mewtaproteomics Analysis. Journal of Computer Science and Technology, 2026, 41(3): 1087-1100. https://doi.org/10.1007/s11390-026-5122-3

6

Views

0

Crossref

0

Web of Science

0

Scopus

0

CSCD

Received: 24 December 2024
Accepted: 05 February 2026
Published: 01 May 2026
© Institute of Computing Technology, Chinese Academy of Sciences 2026