Xiang-He Meng1,2,3, Hong-Mei Xiao1, Hong-Wen Deng1,2,3. 1. Centers of System Biology, Data Information and Reproductive Health, School of Basic Medical Science, Central South University, Changsha, Hunan 410008, China. 2. Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA. 3. Centers of System Biology, Data Information and Reproductive Health, Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan 410081, China.
Abstract
MOTIVATION: Although genome-wide association studies (GWASs) have identified thousands of variants for various traits, the causal variants and the mechanisms underlying the significant loci are largely unknown. In this study, we aim to predict non-coding variants that may functionally affect translation initiation through long-range chromatin interaction. RESULTS: By incorporating the Hi-C data, we propose a novel and powerful deep learning model of artificial intelligence to classify interacting and non-interacting fragment pairs and predict the functional effects of sequence alteration of single nucleotide on chromatin interaction and thus on gene expression. The changes in chromatin interaction probability between the reference sequence and the altered sequence reflect the degree of functional impact for the variant. The model was effective and efficient with the classification of interacting and non-interacting fragment pairs. The predicted causal SNPs that had a larger impact on chromatin interaction were more likely to be identified by GWAS and eQTL analyses. We demonstrate that an integrative approach combining artificial intelligence-deep learning with high throughput experimental evidence of chromatin interaction leads to prioritizing the functional variants in disease- and phenotype-related loci and thus will greatly expedite uncover of the biological mechanism underlying the association identified in genomic studies. AVAILABILITY AND IMPLEMENTATION: Source code used in data preparing and model training is available at the GitHub website (https://github.com/biocai/DeepHiC). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Although genome-wide association studies (GWASs) have identified thousands of variants for various traits, the causal variants and the mechanisms underlying the significant loci are largely unknown. In this study, we aim to predict non-coding variants that may functionally affect translation initiation through long-range chromatin interaction. RESULTS: By incorporating the Hi-C data, we propose a novel and powerful deep learning model of artificial intelligence to classify interacting and non-interacting fragment pairs and predict the functional effects of sequence alteration of single nucleotide on chromatin interaction and thus on gene expression. The changes in chromatin interaction probability between the reference sequence and the altered sequence reflect the degree of functional impact for the variant. The model was effective and efficient with the classification of interacting and non-interacting fragment pairs. The predicted causal SNPs that had a larger impact on chromatin interaction were more likely to be identified by GWAS and eQTL analyses. We demonstrate that an integrative approach combining artificial intelligence-deep learning with high throughput experimental evidence of chromatin interaction leads to prioritizing the functional variants in disease- and phenotype-related loci and thus will greatly expedite uncover of the biological mechanism underlying the association identified in genomic studies. AVAILABILITY AND IMPLEMENTATION: Source code used in data preparing and model training is available at the GitHub website (https://github.com/biocai/DeepHiC). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Matthew T Maurano; Richard Humbert; Eric Rynes; Robert E Thurman; Eric Haugen; Hao Wang; Alex P Reynolds; Richard Sandstrom; Hongzhu Qu; Jennifer Brody; Anthony Shafer; Fidencio Neri; Kristen Lee; Tanya Kutyavin; Sandra Stehling-Sun; Audra K Johnson; Theresa K Canfield; Erika Giste; Morgan Diegel; Daniel Bates; R Scott Hansen; Shane Neph; Peter J Sabo; Shelly Heimfeld; Antony Raubitschek; Steven Ziegler; Chris Cotsapas; Nona Sotoodehnia; Ian Glass; Shamil R Sunyaev; Rajinder Kaul; John A Stamatoyannopoulos Journal: Science Date: 2012-09-05 Impact factor: 47.728
Authors: Sven Heinz; Christopher Benner; Nathanael Spann; Eric Bertolino; Yin C Lin; Peter Laslo; Jason X Cheng; Cornelis Murre; Harinder Singh; Christopher K Glass Journal: Mol Cell Date: 2010-05-28 Impact factor: 17.970