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

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

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.

Bibliographic Details
Main Authors: Yuichi Shiraishi, Ai Okada, Kenichi Chiba, Asuka Kawachi, Ikuko Omori, Raúl Nicolás Mateos, Naoko Iida, Hirofumi Yamauchi, Kenjiro Kosaki, Akihide Yoshimi
Format: Article
Language:English
Published: Nature Portfolio 2022-09-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-022-32887-9

Internet

https://doi.org/10.1038/s41467-022-32887-9

Similar Items