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A Customizable Analysis Flow in Integrative Multi-Omics.

Samuel M Lancaster1,2, Akshay Sanghi1,2, Si Wu1,2

  • 1Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA.

Biomolecules
|December 2, 2020
PubMed
Summary
This summary is machine-generated.

Multi-omics integrates diverse biological data for deeper systems biology insights. This review presents a workflow for analyzing vast multi-omic datasets, enhancing biological question resolution.

Keywords:
analysis flowbioinformaticsmachine learningmulti-omicsmulti-omics analysisstudy design

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Area of Science:

  • Systems Biology
  • Bioinformatics

Background:

  • The use of multi-omics is increasing due to decreasing costs and enhanced biological insights.
  • Multi-omics provides an unprecedented depth of understanding in systems biology.

Purpose of the Study:

  • To present an analysis workflow for multi-omic data, including six different omic measurements.
  • To equip researchers with tools for analyzing vast multi-omic datasets.

Main Methods:

  • The workflow covers analysis of genomic, epigenomic, transcriptomic, metagenomic, proteomic, and metabolomic data.
  • It includes best practices for data integration, machine learning, and validation of findings.

Main Results:

  • The presented workflow facilitates the analysis of complex, large-scale multi-omic datasets.
  • It enables a more refined resolution for answering biological questions.

Conclusions:

  • Multi-omic integration offers a comprehensive view of systems biology and molecular interactions.
  • The workflow provides a valuable resource for both novice and experienced multi-omic researchers.