Identification of Protein-Coding Gene Structure and Protein-Related Genes and Their Splicing Sites in Kidney Stone Disease: A Protein Big Data Analysis.
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| Abstract |
The study of protein-coding gene structure and protein-related genes in kidney stone disease is used for accurate identification of splicing sites and accurate location of gene exon boundaries, which is one of the difficulties and key problems in understanding the genome and discovering new genes. Prediction techniques based on signal characteristics of conserved sequences around splicing sites, such as the weighted array model (WAM), are widely used. On this basis, several other features that can be used for splicing site recognition (such as the base composition of splicing site upstream and downstream sequences, the change of signal and base composition of upstream and downstream sequences with the C + G content of adjacent sequences) were mined further, and a model was developed to describe these features. In this study, a log-linear model that can effectively integrate these features for splicing site recognition was designed, and a SpliceKey programme was developed. The findings reveal that SpliceKey's splicing site identification accuracy is not only much better than the WAM approach, but also better than DGSplice.
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| Authors | Shiyu Wang, Xiangmei Chen |
| Journal | Applied biochemistry and biotechnology (Appl Biochem Biotechnol) Vol. 195 Issue 10 Pg. 6020-6031 (Oct 2023) ISSN: 1559-0291 [Electronic] United States |
| PMID | 36763230 (Publication Type: Journal Article) |
| Copyright | © 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. |
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