Review of Computational Methods and Database Sources for Predicting the Effects of Coding Frameshift Small Insertion and Deletion Variations
Issued Date
2024-01-16
Resource Type
eISSN
24701343
Scopus ID
2-s2.0-85182012718
Journal Title
ACS Omega
Volume
9
Issue
2
Start Page
2032
End Page
2047
Rights Holder(s)
SCOPUS
Bibliographic Citation
ACS Omega Vol.9 No.2 (2024) , 2032-2047
Suggested Citation
Ge F., Arif M., Yan Z., Alahmadi H., Worachartcheewan A., Shoombuatong W. Review of Computational Methods and Database Sources for Predicting the Effects of Coding Frameshift Small Insertion and Deletion Variations. ACS Omega Vol.9 No.2 (2024) , 2032-2047. 2047. doi:10.1021/acsomega.3c07662 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/95685
Title
Review of Computational Methods and Database Sources for Predicting the Effects of Coding Frameshift Small Insertion and Deletion Variations
Corresponding Author(s)
Other Contributor(s)
Abstract
Genetic variations (including substitutions, insertions, and deletions) exert a profound influence on DNA sequences. These variations are systematically classified as synonymous, nonsynonymous, and nonsense, each manifesting distinct effects on proteins. The implementation of high-throughput sequencing has significantly augmented our comprehension of the intricate interplay between gene variations and protein structure and function, as well as their ramifications in the context of diseases. Frameshift variations, particularly small insertions and deletions (indels), disrupt protein coding and are instrumental in disease pathogenesis. This review presents a succinct review of computational methods, databases, current challenges, and future directions in predicting the consequences of coding frameshift small indels variations. We analyzed the predictive efficacy, reliability, and utilization of computational methods and variant account, reliability, and utilization of database. Besides, we also compared the prediction methodologies on GOF/LOF pathogenic variation data. Addressing the challenges pertaining to prediction accuracy and cross-species generalizability, nascent technologies such as AI and deep learning harbor immense potential to enhance predictive capabilities. The importance of interdisciplinary research and collaboration cannot be overstated for devising effective diagnosis, treatment, and prevention strategies concerning diseases associated with coding frameshift indels variations.