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MMCT-Loop: a mix model-based pipeline for calling targeted 3D chromatin loops.
Tang, Li; Liao, Jiaqi; Hill, Matthew C; Hu, Jiaxin; Zhao, Yichao; Ellinor, Patrick T; Li, Min.
  • Tang L; Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
  • Liao J; Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
  • Hill MC; Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02129, USA.
  • Hu J; Cardiovascular Disease Initiative, The Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
  • Zhao Y; Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
  • Ellinor PT; Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
  • Li M; Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02129, USA.
Nucleic Acids Res ; 52(5): e25, 2024 Mar 21.
Article en En | MEDLINE | ID: mdl-38281134
ABSTRACT
Protein-specific Chromatin Conformation Capture (3C)-based technologies have become essential for identifying distal genomic interactions with critical roles in gene regulation. The standard techniques include Chromatin Interaction Analysis by Paired-End Tag (ChIA-PET), in situ Hi-C followed by chromatin immunoprecipitation (HiChIP) also known as PLAC-seq. To identify chromatin interactions from these data, a variety of computational methods have emerged. Although these state-of-art methods address many issues with loop calling, only few methods can fit different data types simultaneously, and the accuracy as well as the efficiency these approaches remains limited. Here we have generated a pipeline, MMCT-Loop, which ensures the accurate identification of strong loops as well as dynamic or weak loops through a mixed model. MMCT-Loop outperforms existing methods in accuracy, and the detected loops show higher activation functionality. To highlight the utility of MMCT-Loop, we applied it to conformational data derived from neural stem cell (NSCs) and uncovered several previously unidentified regulatory regions for key master regulators of stem cell identity. MMCT-Loop is an accurate and efficient loop caller for targeted conformation capture data, which supports raw data or pre-processed valid pairs as input, the output interactions are formatted and easily uploaded to a genome browser for visualization.
Asunto(s)

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Cromatina / Técnicas Genéticas / Genómica Idioma: En Año: 2024 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Cromatina / Técnicas Genéticas / Genómica Idioma: En Año: 2024 Tipo del documento: Article