Systematic identification of intron retention associated variants from massive publicly available transcriptome sequencing data.

ABSTRACT: Many disease-associated genomic variants disrupt gene function through abnormal splicing. With the advancement of genomic medicine, identifying disease-associated splicing associated variants has become more important than ever. Most bioinformatics approaches to detect splicing associated variants require both genome and transcriptomic data. However, there are not many datasets where both of them are available. In this study, we develop a methodology to detect genomic variants that cause splicing changes (more specifically, intron retention), using transcriptome sequencing data alone. After evaluating its sensitivity and precision, we apply it to 230,988 transcriptome sequencing data from the publicly available repository and identified 27,049 intron retention associated variants (IRAVs).

SUBMITTER: Shiraishi Y 

PROVIDER: S-EPMC9522810 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature