Literature DB >> 33866359

TAD boundary and strength prediction by integrating sequence and epigenetic profile information.

Yunlong Wang1, Yaqi Liu1, Qian Xu1, Yao Xu1, Kai Cao1, Nan Deng1, Ruimin Wang1, Xueying Zhang1, Ruiqin Zheng1, Guoliang Li2, Yaping Fang2.   

Abstract

Topologically associated domains (TADs) are one of the important higher order chromatin structures with various sizes in the eukaryotic genomes. TAD boundaries, as the flanking regions between adjacent domains, can restrict the interactions of regulatory elements, including enhancers and promoters, and are generally dynamic and variable in different cells. However, the influence of sequence and epigenetic profile-based features in the identification of TAD boundaries is largely unknown. In this work, we proposed a method called pTADS (prediction of TAD boundary and strength), to predict TAD boundaries and boundary strength across multiple cell lines with DNA sequence and epigenetic profile information. The performance was assessed in seven cell lines and three TAD calling methods. The results demonstrate that the TAD boundary can be well predicted by the selected shared features across multiple cell lines. Especially, the model can be transferable to predict the TAD boundary from one cell line to other cell lines. The boundary strength can be characterized by boundary score with good performance. The predicted TAD boundary and TAD boundary strength are further confirmed by three Hi-C contact matrix-based methods across multiple cell lines. The codes and datasets are available at https://github.com/chrom3DEpi/pTADS.
© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Keywords:  TAD boundary; boundary score; boundary strength; epigenetic profile; machine learning

Year:  2021        PMID: 33866359     DOI: 10.1093/bib/bbab139

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  5 in total

1.  preciseTAD: A transfer learning framework for 3D domain boundary prediction at base-pair resolution.

Authors:  Spiro C Stilianoudakis; Maggie A Marshall; Mikhail G Dozmorov
Journal:  Bioinformatics       Date:  2021-11-06       Impact factor: 6.937

2.  Universal annotation of the human genome through integration of over a thousand epigenomic datasets.

Authors:  Ha Vu; Jason Ernst
Journal:  Genome Biol       Date:  2022-01-06       Impact factor: 13.583

3.  Effects of the Zbtb1 Gene on Chromatin Spatial Structure and Lymphatic Development: Combined Analysis of Hi-C, ATAC-Seq and RNA-Seq.

Authors:  Junhong Wang; Chunwei Shi; Mingyang Cheng; Yiyuan Lu; Xiaoyu Zhang; Fengdi Li; Yu Sun; Xiaoxu Li; Xinyang Li; Yan Zeng; Chunfeng Wang; Xin Cao
Journal:  Front Cell Dev Biol       Date:  2022-04-25

Review 4.  Mapping nucleosome and chromatin architectures: A survey of computational methods.

Authors:  Kun Fang; Junbai Wang; Lu Liu; Victor X Jin
Journal:  Comput Struct Biotechnol J       Date:  2022-07-26       Impact factor: 6.155

Review 5.  Insulators in Plants: Progress and Open Questions.

Authors:  Amina Kurbidaeva; Michael Purugganan
Journal:  Genes (Basel)       Date:  2021-09-16       Impact factor: 4.096

  5 in total

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