A sequence-based deep learning approach to predict CTCF-mediated chromatin loop. Issue 5 (25th February 2021)

• Record Type: Journal Article
• Authors: Lv, Hao; Dao, Fu-Ying; Zulfiqar, Hasan; Su, Wei; Ding, Hui; Liu, Li; Lin, Hao
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 5(2021)
• Publication Date: 2021-02-25
• Subjects: chromosome conformation -- loop -- CTCF -- deep learning -- sequence feature; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbab031
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Three-dimensional (3D) architecture of the chromosomes is of crucial importance for transcription regulation and DNA replication. Various high-throughput chromosome conformation capture-based methods have revealed that CTCF-mediated chromatin loops are a major component of 3D architecture. However, CTCF-mediated chromatin loops are cell type specific, and most chromatin interaction capture techniques are time-consuming and labor-intensive, which restricts their usage on a very large number of cell types. Genomic sequence-based computational models are sophisticated enough to capture important features of chromatin architecture and help to identify chromatin loops. In this work, we develop Deep-loop, a convolutional neural network model, to integrate k -tuple nucleotide frequency component, nucleotide pair spectrum encoding, position conservation, position scoring function and natural vector features for the prediction of chromatin loops. By a series of examination based on cross-validation, Deep-loop shows excellent performance in the identification of the chromatin loops from different cell types. The source code of Deep-loop is freely available at the repository https://github.com/linDing-group/Deep-loop .