Literature DB >> 28976510

Gene co-opening network deciphers gene functional relationships.

Wenran Li1, Meng Wang, Jinghao Sun, Yong Wang, Rui Jiang.   

Abstract

Genome sequencing technology has generated a vast amount of genomic and epigenomic data, and has provided us a great opportunity to study gene functions on a global scale from an epigenomic view. In the last decade, network-based studies, such as those based on PPI networks and co-expression networks, have shown good performance in capturing functional relationships between genes. However, the functions of a gene and the mechanism of interaction of genes with each other to elucidate their functions are still not entirely clear. Here, we construct a gene co-opening network based on chromatin accessibility of genes. We show that genes related to a specific biological process or the same disease tend to be clustered in the co-opening network. This understanding allows us to detect functional clusters from the network and to predict new functions for genes. We further apply the network to prioritize disease genes for Psoriasis, and demonstrate the power of the joint analysis of the co-opening network and GWAS data in identifying disease genes. Taken together, the co-opening network provides a new viewpoint for the elucidation of gene associations and the interpretation of disease mechanisms.

Entities:  

Mesh:

Year:  2017        PMID: 28976510     DOI: 10.1039/c7mb00430c

Source DB:  PubMed          Journal:  Mol Biosyst        ISSN: 1742-2051


  6 in total

1.  OpenAnnotate: a web server to annotate the chromatin accessibility of genomic regions.

Authors:  Shengquan Chen; Qiao Liu; Xuejian Cui; Zhanying Feng; Chunquan Li; Xiaowo Wang; Xuegong Zhang; Yong Wang; Rui Jiang
Journal:  Nucleic Acids Res       Date:  2021-07-02       Impact factor: 16.971

2.  DeepTACT: predicting 3D chromatin contacts via bootstrapping deep learning.

Authors:  Wenran Li; Wing Hung Wong; Rui Jiang
Journal:  Nucleic Acids Res       Date:  2019-06-04       Impact factor: 16.971

3.  Facilitating Anti-Cancer Combinatorial Drug Discovery by Targeting Epistatic Disease Genes.

Authors:  Yuan Quan; Meng-Yuan Liu; Ye-Mao Liu; Li-Da Zhu; Yu-Shan Wu; Zhi-Hui Luo; Xiu-Zhen Zhang; Shi-Zhong Xu; Qing-Yong Yang; Hong-Yu Zhang
Journal:  Molecules       Date:  2018-03-23       Impact factor: 4.411

4.  EnContact: predicting enhancer-enhancer contacts using sequence-based deep learning model.

Authors:  Mingxin Gan; Wenran Li; Rui Jiang
Journal:  PeerJ       Date:  2019-09-13       Impact factor: 2.984

5.  Automatic detection of genomic regions with informative epigenetic patterns.

Authors:  Florencio Pazos; Adrian Garcia-Moreno; Juan C Oliveros
Journal:  BMC Genomics       Date:  2018-11-28       Impact factor: 3.969

6.  A method for scoring the cell type-specific impacts of noncoding variants in personal genomes.

Authors:  Wenran Li; Zhana Duren; Rui Jiang; Wing Hung Wong
Journal:  Proc Natl Acad Sci U S A       Date:  2020-08-17       Impact factor: 11.205

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.