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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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BioMOBS: A multi-omics visual analytics workflow for biomolecular insight generation.

Dries Heylen^1,2, Jannes Peeters³, Jan Aerts⁴

¹Theory Lab, Data Science Institute (DSI), Hasselt University, Diepenbeek, Belgium.

|December 14, 2023

Summary

BioMOBS is a new workflow for multi-omics data analysis in precision medicine. It helps discover molecular interactions and generate clinically relevant insights for disease pathway analysis.

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Area of Science:

Bioinformatics
Computational Biology
Systems Biology

Background:

Multi-omics data analysis is crucial for understanding complex diseases in precision medicine.
Efficiently exploring undiscovered molecular interactions remains a significant challenge.
Integrating diverse omics data with existing biological knowledge is essential for clinical relevance.

Purpose of the Study:

To present BioMOBS, a novel workflow for multi-omics data analysis.
To enable efficient exploration of molecular interactions and disease pathways.
To provide clinically relevant insights for precision medicine.

Main Methods:

BioMOBS integrates two data visualization tools with an open-source molecular information database.
The workflow performs exploratory pathway analysis on multi-omics data.
It visually integrates data-driven findings with established pathway knowledge.

Main Results:

BioMOBS successfully reproduced recent findings in type 2 diabetes UK biobank data.
The workflow demonstrated the ability to generate relevant molecular hypotheses.
A central visualization tool allows integration of data-driven and literature-based findings.

Conclusions:

BioMOBS offers a robust procedure for clinically relevant insights in disease pathway analyses.
The workflow is applicable to various types of omics data.
It facilitates a deeper understanding of molecular interactions in disease processes.