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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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How to interpret and integrate multi-omics data at systems level.

Gun Tae Jung1, Kwang-Pyo Kim1,2, Kwoneel Kim3

  • 1Department of Biomedical Science and Technology, Kyung Hee University, Seoul, Republic of Korea.

Animal Cells and Systems
|March 12, 2020
PubMed
Summary
This summary is machine-generated.

Omics data reveals complex biological regulation, requiring systems-level network analysis. Integrating multi-omics data within appropriate networks, centered on transcriptional core regulators, provides a comprehensive view of biological phenotypes.

Keywords:
Multi-omicsco-expression networkprotein interactome networktranscriptional core regulatortranscriptional regulatory network

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Area of Science:

  • Genomics and Systems Biology
  • Bioinformatics and Computational Biology

Background:

  • Parallel sequencing generates vast omics data, revealing intricate regulatory complexity in biological phenotypes.
  • Understanding this complexity necessitates systems-level approaches, particularly network analysis, to interpret interconnected biological layers.

Purpose of the Study:

  • To review methods for systematic interpretation and integration of multi-omics data.
  • To propose a novel analysis framework for multi-omics interpretation centered on transcriptional core regulators.

Main Methods:

  • Systematic review of recent studies on multi-omics data interpretation and integration.
  • Development of an analysis framework applicable across different omics networks.

Main Results:

  • Multi-omics data integration is crucial for a complete understanding of the regulatory landscape.
  • Individual omics data require specific biological networks for appropriate systematic analysis.

Conclusions:

  • A comprehensive regulatory landscape is achieved by integrating multi-omics data within adequate networks.
  • The proposed framework, focusing on transcriptional core regulators, facilitates systematic multi-omics interpretation.