Статья

SEanalysis 2.0: a comprehensive super-enhancer regulatory network analysis tool for human and mouse

Fengcui QianInsititute of Biochemistry and Molecular Biology, Hengyang Medical College, University of South China , Hengyang , Hunan 421001, ChinaLi‐Wei ZhouSchool of Medical Informatics, Daqing Campus, Harbin Medical University , Daqing 163319, ChinaYanyu LiSchool of Medical Informatics, Daqing Campus, Harbin Medical University , Daqing 163319, ChinaZhengmin YuThe First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China , Hengyang , Hunan 421001, ChinaLidong LiSchool of Computer, University of South China , Hengyang , Hunan 421001, ChinaYuezhu WangSchool of Medical Informatics, Daqing Campus, Harbin Medical University , Daqing 163319, ChinaMingcong XuThe First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China , Hengyang , Hunan 421001, ChinaQiuyu WangInsititute of Biochemistry and Molecular Biology, Hengyang Medical College, University of South China , Hengyang , Hunan 421001, ChinaChunquan LiInsititute of Biochemistry and Molecular Biology, Hengyang Medical College, University of South China , Hengyang , Hunan 421001, China

2023en

ABI

Аннотация

Super-enhancers (SEs) play an essential regulatory role in various biological processes and diseases through their specific interaction with transcription factors (TFs). Here, we present the release of SEanalysis 2.0 (http://licpathway.net/SEanalysis), an updated version of the SEanalysis web server for the comprehensive analyses of transcriptional regulatory networks formed by SEs, pathways, TFs, and genes. The current version added mouse SEs and further expanded the scale of human SEs, documenting 1 167 518 human SEs from 1739 samples and 550 226 mouse SEs from 931 samples. The SE-related samples in SEanalysis 2.0 were more than five times that in version 1.0, which significantly improved the ability of original SE-related network analyses ('pathway downstream analysis', 'upstream regulatory analysis' and 'genomic region annotation') for understanding context-specific gene regulation. Furthermore, we designed two novel analysis models, 'TF regulatory analysis' and 'Sample comparative analysis' for supporting more comprehensive analyses of SE regulatory networks driven by TFs. Further, the risk SNPs were annotated to the SE regions to provide potential SE-related disease/trait information. Hence, we believe that SEanalysis 2.0 has significantly expanded the data and analytical capabilities of SEs, which helps researchers in an in-depth understanding of the regulatory mechanisms of SEs.

Перевод пока недоступен

Идентификаторы

DOI: 10.1093/nar/gkad408

Цитирования и источники

Цитирований: 2Использованных источников: 0

Показатели — AkademScholar