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 (Nature Communications, 2022)
This paper proposed a novel in-silico framework for automatically screening disease-related variants and applied it to over 200,000 transcriptomes, providing an example to acquire medically relevant knowledge from publicly available sequence data.