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Genomics

Genomics

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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Related Experiment Video

Updated: Jun 25, 2025

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A guided network estimation approach using multi-omic information.

Georgios Bartzis¹, Carel F W Peeters², Wilco Ligterink³

¹Mathematical and Statistical Methods Group - Biometris, Wageningen University and Research, Wageningen, The Netherlands.

BMC Bioinformatics

|May 30, 2024

Summary

This summary is machine-generated.

This study introduces a new method for reconstructing integrative biological networks. The approach uses existing network structures from related data to guide the analysis, revealing metabolite groups with shared genetic or transcriptomic underpinnings.

Keywords:

Multi-omics Network integration Network reconstruction

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Last Updated: Jun 25, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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Area of Science:

Systems Biology
Network Biology
Bioinformatics

Background:

Organisms are viewed as interconnected molecular systems.
Understanding organism function requires integrating molecular concentration data.
Few methods exist for reconstructing integrative biological networks.

Purpose of the Study:

Propose an integrative network reconstruction method.
Utilize network structure from upstream omics data to guide downstream network organization.
Allow for known or estimated guiding network structures.

Main Methods:

Provide a network structure for guiding data.
Regress target data on guiding data predictors using penalized regression (Lasso and L2).
Reconstruct the target network conditioned on the guiding network structure.

Main Results:

Illustrate the approach with two examples in Arabidopsis.
Detect groups of metabolites with similar genetic or transcriptomic bases.

Conclusions:

The proposed method enables integrative network reconstruction.
It effectively identifies metabolite groups based on upstream molecular data.
Applicable to understanding complex biological systems.