De novo identification of expressed cancer somatic mutations from single-cell RNA sequencing data

Autor:	Tianyun Zhang, Hanying Jia, Tairan Song, Lin Lv, Doga C. Gulhan, Haishuai Wang, Wei Guo, Ruibin Xi, Hongshan Guo, Ning Shen
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	Somatic mutations Single-cell RNA sequencing data Recurrently Expressed SNV Analysis High precision Medicine Genetics QH426-470
Zdroj:	Genome Medicine, Vol 15, Iss 1, Pp 1-18 (2023)
Druh dokumentu:	article
ISSN:	1756-994X
DOI:	10.1186/s13073-023-01269-1
Popis:	Abstract Identifying expressed somatic mutations from single-cell RNA sequencing data de novo is challenging but highly valuable. We propose RESA – Recurrently Expressed SNV Analysis, a computational framework to identify expressed somatic mutations from scRNA-seq data. RESA achieves an average precision of 0.77 on three in silico spike-in datasets. In extensive benchmarking against existing methods using 19 datasets, RESA consistently outperforms them. Furthermore, we applied RESA to analyze intratumor mutational heterogeneity in a melanoma drug resistance dataset. By enabling high precision detection of expressed somatic mutations, RESA substantially enhances the reliability of mutational analysis in scRNA-seq. RESA is available at https://github.com/ShenLab-Genomics/RESA .
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/ad06594af069403b885f96af7a6cbad2 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
Nepřihlášeným uživatelům se plný text nezobrazuje	K zobrazení výsledku je třeba se přihlásit.