4.6 Article

TADreg: a versatile regression framework for TAD identification, differential analysis and rearranged 3D genome prediction

Journal

BMC BIOINFORMATICS
Volume 23, Issue 1, Pages -

Publisher

BMC
DOI: 10.1186/s12859-022-04614-0

Keywords

Chromatin interaction; Hi-C; ChIP-seq; Insulator binding protein; Generalized linear model

Funding

  1. University of Toulouse
  2. CNRS

Ask authors/readers for more resources

This study proposes a versatile regression framework for accurately identifying and analyzing the three-dimensional organization of the genome, including differential borders and rearrangements. The results highlight the significance of this method in identifying new genomic features and transcription factors and comparing favorably with current state-of-the-art methods.
Background/Aim In higher eukaryotes, the three-dimensional (3D) organization of the genome is intimately related to numerous key biological functions including gene expression, DNA repair and DNA replication regulations. Alteration of 3D organization, in particular topologically associating domains (TADs), is detrimental to the organism and can give rise to a broad range of diseases such as cancers. Methods Here, we propose a versatile regression framework which not only identifies TADs in a fast and accurate manner, but also detects differential TAD borders across conditions for which few methods exist, and predicts 3D genome reorganization after chromosomal rearrangement. Moreover, the framework is biologically meaningful, has an intuitive interpretation and is easy to visualize. Result and conclusion The novel regression ranks among top TAD callers. Moreover, it identifies new features of the genome we called TAD facilitators, and that are enriched with specific transcription factors. It also unveils the importance of cell-type specific transcription factors in establishing novel TAD borders during neuronal differentiation. Lastly, it compares favorably with the state-of-the-art method for predicting rearranged 3D genome.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available