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Related Concept Videos

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Genomics

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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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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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SUBATOMIC: a SUbgraph BAsed mulTi-OMIcs clustering framework to analyze integrated multi-edge networks.

Jens Uwe Loers1,2,3, Vanessa Vermeirssen4,5,6

  • 1Lab for Computational Biology, Integromics and Gene Regulation (CBIGR), Cancer Research Institute Ghent (CRIG), Ghent, Belgium.

BMC Bioinformatics
|September 5, 2022
PubMed
Summary
This summary is machine-generated.

SUBATOMIC is a new framework for analyzing multi-omics networks, creating small, interpretable modules to understand gene regulation and disease. It helps identify key regulators and functions, even for unannotated genes, and reveals responses to conditions like hypoxia in cancer.

Keywords:
Composite subgraphsGene function predictionGene regulationGene regulatory networksHypoxiaModulesMulti-edge networksMulti-omicsNetwork analysisTopology

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Area of Science:

  • Systems Biology
  • Bioinformatics
  • Network Medicine

Background:

  • Multi-omics networks offer insights into gene regulation and disease but often form complex, uninterpretable structures.
  • Existing module detection methods struggle with multi-omics data, topological features, and maintaining broader network context.
  • The SUBATOMIC (SUbgraph BAsed mulTi-OMIcs Clustering) framework was developed to address these limitations.

Purpose of the Study:

  • To develop a novel computational framework for inferring small, interpretable modules from integrated multi-omics networks.
  • To address the challenge of network interpretability in multi-omics data by incorporating topological features and broader network context.
  • To enable the identification of condition-specific modules and regulators for mechanistic understanding of biological processes and diseases.

Main Methods:

  • SUBATOMIC clusters composite network subgraphs into topological modules, annotates them functionally, and visualizes them with expression profiles.
  • The framework preserves network context by statistically analyzing inter-module connections and regulator interactions (e.g., miRNAs, transcription factors).
  • Applied to a human multi-omics network integrating various interaction types from public databases.

Main Results:

  • Derived and annotated 5586 modules with distinct topological, functional, and regulatory properties from the human interactome.
  • Generated novel functional hypotheses for previously unannotated genes.
  • Integrated modules with expression data to study hypoxia in cancer cell lines, identifying condition-specific modules using GO term enrichment and activity scores.

Conclusions:

  • The SUBATOMIC framework successfully generates interpretable modules from integrated multi-omics networks, applicable to disease contexts like cancer.
  • SUBATOMIC facilitates module contextualization, identification of condition-specific modules, regulators, and novel gene functions.
  • The software is publicly available, promoting further research in multi-omics network analysis.