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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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Computational strategies for single-cell multi-omics integration.

Nigatu Adossa1, Sofia Khan1, Kalle T Rytkönen1,2

  • 1Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland.

Computational and Structural Biotechnology Journal
|May 24, 2021
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Summary

Single-cell multi-omics integrates diverse biological data for new discoveries. This review categorizes computational integration strategies, aiding researchers in analyzing complex cellular information.

Keywords:
ClusteringIntegrationMulti-omicsSingle-cell

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Area of Science:

  • Biotechnology and Bioinformatics
  • Genomics and Molecular Biology

Background:

  • Single-cell omics technologies reveal cellular heterogeneity, differentiation, and communication.
  • Advances in single-cell multi-omics offer potential for significant biological and medical breakthroughs.

Purpose of the Study:

  • To review recent developments in single-cell multi-omics.
  • To categorize and describe computational data integration strategies for single-cell multi-omics.

Main Methods:

  • Categorization of integration strategies into early, intermediate, and late approaches.
  • Description of conceptual principles, characteristics, and tools for each integration category.
  • Review of applications in analyzing single-cell multi-omics data.

Main Results:

  • Overview of current single-cell multi-omics technologies and their applications.
  • Detailed explanation of three main data integration strategies: early, intermediate, and late.
  • Examples of computational tools and their use in analyzing multi-omics data.

Conclusions:

  • Single-cell multi-omics data integration is crucial for advancing biological insights.
  • Exploration of challenges and future directions, including multi-view analysis adoption.
  • Highlights the need for robust computational tools to harness the potential of multi-omics data.