ArticleNature communications2022
Systematic identification of intron retention associated variants from massive publicly available transcriptome sequencing data.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- SpliceSelectNet: a hierarchical Transformer-based deep learning model for splice site prediction.Nucleic acids research · 2026Article
- Assessment of forensic individual identification and kinship analysis using transcript SNPs derived from public transcriptome sequencing data.BMC genomics · 2026Article
- Germline pathogenic variation impacts somatic alterations and patient outcomes in pediatric central nervous system tumors.Nature communications · 2025Article
- A quantitative, Bayesian-informed approach to gene-specific variant classification: Updated Expert Panel recommendations improve classification of TP53 germline variants for Li-Fraumeni syndrome.Genome medicine · 2025Review
- Intron Retention: A Reemerging Paradigm in RNA Biology and Post-Transcriptional Gene Regulation.Genes · 2025Review
- Germline pathogenic variation impacts somatic alterations and patient outcomes in pediatric CNS tumors.medRxiv : the preprint server for health sciences · 2025Article
- Cross-attention graph neural networks for inferring gene regulatory networks with skewed degree distribution.BMC bioinformatics · 2025Article
- Systematically developing a registry of splice-site creating variants utilizing massive publicly available transcriptome sequence data.Nature communications · 2025Article
- RNA Sequencing in Disease Diagnosis.Annual review of genomics and human genetics · 2024Review
- Clinical and Molecular Characterization ofInternational journal of molecular sciences · 2024Article
- A single-cell strategy for the identification of intronic variants related to mis-splicing in pancreatic cancer.NAR genomics and bioinformatics · 2024Article
- Comprehensive profiling of cancer neoantigens from aberrant RNA splicing.Journal for immunotherapy of cancer · 2024Article
- Assessing the efficacy of target adaptive sampling long-read sequencing through hereditary cancer patient genomes.NPJ genomic medicine · 2024Article
- Association of Genetic Markers with the Risk of Early-Onset Breast Cancer in Kazakh Women.Genes · 2024Article
- RNA-Based Strategies for Cancer Therapy: In Silico Design and Evaluation of ASOs for Targeted Exon Skipping.International journal of molecular sciences · 2023Article
- Multi-omics approach to identifying isoform variants as therapeutic targets in cancer patients.Frontiers in oncology · 2022Review
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
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). In addition, by exploring positional relationships with variants registered in existing disease databases, we extract 3,000 putative disease-associated IRAVs, which range from cancer drivers to variants linked with autosomal recessive disorders. The in-silico screening framework demonstrates the possibility of near-automatically acquiring medical knowledge, making the most of massively accumulated publicly available sequencing data. Collections of IRAVs identified in this study are available through IRAVDB ( https://iravdb.io/ ).
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