Bioinformatics toolbox for exploring target mutation-induced drug resistance

Abstract Drug resistance is increasingly among the main issues affecting human health and threatening agriculture and food security. In particular, developing approaches to overcome target mutation-induced drug resistance has long been an essential part of biological research. During the past decade...

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Bibliographic Details
Published in:Briefings in bioinformatics 2023-03, Vol.24 (2)
Main Authors: Huang, Yuan-Qin, Sun, Ping, Chen, Yi, Liu, Huan-Xiang, Hao, Ge-Fei, Song, Bao-An
Format: Article
Language:English
Subjects:
Bioinformatics
Biological research
Computational Biology
Drug Resistance
Food security
Humans
Mutation
Pesticides
Software
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Summary:Abstract Drug resistance is increasingly among the main issues affecting human health and threatening agriculture and food security. In particular, developing approaches to overcome target mutation-induced drug resistance has long been an essential part of biological research. During the past decade, many bioinformatics tools have been developed to explore this type of drug resistance, and they have become popular for elucidating drug resistance mechanisms in a low cost, fast and effective way. However, these resources are scattered and underutilized, and their strengths and limitations have not been systematically analyzed and compared. Here, we systematically surveyed 59 freely available bioinformatics tools for exploring target mutation-induced drug resistance. We analyzed and summarized these resources based on their functionality, data volume, data source, operating principle, performance, etc. And we concisely discussed the strengths, limitations and application examples of these tools. Specifically, we tested some predictive tools and offered some thoughts from the clinician’s perspective. Hopefully, this work will provide a useful toolbox for researchers working in the biomedical, pesticide, bioinformatics and pharmaceutical engineering fields, and a good platform for non-specialists to quickly understand drug resistance prediction.
ISSN:1467-5463
1477-4054
DOI:10.1093/bib/bbad033