Systematic identification of intron retention associated variants from massive publicly available transcriptome sequencing data
Yuichi Shiraishi, Ai Okada, Kenichi Chiba, Asuka Kawachi, Ikuko Omori, Raúl Nicolás Mateos, Naoko Iida, Hirofumi Yamauchi, Kenjiro Kosaki, Akihide Yoshimi
The paper identifies intron retention associated variants using transcriptome data alone, finding 27,937 variants and 3,077 putative disease-associated ones.
Nature Communications · Sep 29, 2022Peer-reviewedBioinformatics