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Related Concept Videos

Genomics02:02

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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Proteomics01:33

Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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Multi-omics Data Integration, Interpretation, and Its Application.

Indhupriya Subramanian1, Srikant Verma1, Shiva Kumar1

  • 1LABS, Persistent Systems, Pune, India.

Bioinformatics and Biology Insights
|February 21, 2020
PubMed
Summary
This summary is machine-generated.

Integrating multi-omics data is crucial for understanding complex biological systems. This review highlights tools and methods for analyzing multiple omics datasets for applications like disease subtyping and biomarker discovery.

Keywords:
biomarker predictiondata integrationdata repositoriesdisease subtypingmulti-omics

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Systems biology

Background:

  • Complex biological processes require holistic study using integrative approaches.
  • Multi-omics data integration is essential for understanding biomolecule interrelationships and functions.
  • High-throughput techniques have increased the availability of multi-omics data.

Purpose of the Study:

  • To review and summarize tools and methods for multi-omics data integration.
  • To highlight the applications of integrative multi-omics analysis, including disease subtyping and biomarker prediction.
  • To provide insights into the methodology, use-cases, limitations, and challenges of multi-omics data integration.

Main Methods:

  • Literature review of tools and methods for multi-omics data integration.
  • Categorization of tools based on their approach and applications.
  • Summary of data repositories, visualization portals, and integration challenges.

Main Results:

  • A curated collection of integrative multi-omics analysis tools and methods.
  • Overview of applications in disease subtyping, biomarker prediction, and biological insight generation.
  • Discussion of tool methodologies, use-cases, limitations, and associated challenges.

Conclusions:

  • Integrative multi-omics approaches are vital for advancing biological research and clinical applications.
  • The reviewed tools and methods offer valuable resources for researchers analyzing complex biological data.
  • Addressing challenges in multi-omics data integration is key to unlocking its full potential.