Advances in translational bioinformatics facilitate revealing the landscape of complex disease mechanisms

Jack Y. Yang, A. Keith Dunker, Jun S. Liu, Xiang Qin, Hamid R. Arabnia, William Yang, Andrzej Niemierko, Zhongxue Chen, Zuojie Luo, Liangjiang Wang, Yunlong Liu, Dong Xu, Youping Deng, Weida Tong, Mary Qu Yang

Research output: Contribution to journalEditorialpeer-review

8 Scopus citations

Abstract

Advances of high-throughput technologies have rapidly produced more and more data from DNAs and RNAs to proteins, especially large volumes of genome-scale data. However, connection of the genomic information to cellular functions and biological behaviours relies on the development of effective approaches at higher systems level. In particular, advances in RNA-Seq technology has helped the studies of transcriptome, RNA expressed from the genome, while systems biology on the other hand provides more comprehensive pictures, from which genes and proteins actively interact to lead to cellular behaviours and physiological phenotypes. As biological interactions mediate many biological processes that are essential for cellular function or disease development, it is important to systematically identify genomic information including genetic mutations from GWAS (genome-wide association study), differentially expressed genes, bidirectional promoters, intrinsic disordered proteins (IDP) and protein interactions to gain deep insights into the underlying mechanisms of gene regulations and networks. Furthermore, bidirectional promoters can co-regulate many biological pathways, where the roles of bidirectional promoters can be studied systematically for identifying co-regulating genes at interactive network level. Combining information from different but related studies can ultimately help revealing the landscape of molecular mechanisms underlying complex diseases such as cancer.

Original languageEnglish (US)
Article numberI1
JournalBMC bioinformatics
Volume15
Issue number17
DOIs
StatePublished - Dec 16 2014

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

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