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Underlying causes for prevalent false positives and false negatives in STARR-seq data.
Ni, Pengyu; Wu, Siwen; Su, Zhengchang.
Afiliação
  • Ni P; Department of Bioinformatics and Genomics, The University of North Carolina at Charlotte, Charlotte, NC 28223, USA.
  • Wu S; Department of Bioinformatics and Genomics, The University of North Carolina at Charlotte, Charlotte, NC 28223, USA.
  • Su Z; Department of Bioinformatics and Genomics, The University of North Carolina at Charlotte, Charlotte, NC 28223, USA.
NAR Genom Bioinform ; 5(3): lqad085, 2023 Sep.
Article em En | MEDLINE | ID: mdl-37745976
ABSTRACT
Self-transcribing active regulatory region sequencing (STARR-seq) and its variants have been widely used to characterize enhancers. However, it has been reported that up to 87% of STARR-seq peaks are located in repressive chromatin and are not functional in the tested cells. While some of the STARR-seq peaks in repressive chromatin might be active in other cell/tissue types, some others might be false positives. Meanwhile, many active enhancers may not be identified by the current STARR-seq methods. Although methods have been proposed to mitigate systematic errors caused by the use of plasmid vectors, the artifacts due to the intrinsic limitations of current STARR-seq methods are still prevalent and the underlying causes are not fully understood. Based on predicted cis-regulatory modules (CRMs) and non-CRMs in the human genome as well as predicted active CRMs and non-active CRMs in a few human cell lines/tissues with STARR-seq data available, we reveal prevalent false positives and false negatives in STARR-seq peaks generated by major variants of STARR-seq methods and possible underlying causes. Our results will help design strategies to improve STARR-seq methods and interpret the results.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article