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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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Ultra-long Read Sequencing for Whole Genomic DNA Analysis
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Cloud computing for genomic data analysis and collaboration.

Ben Langmead1, Abhinav Nellore2

  • 1Department of Computer Science, Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.

Nature Reviews. Genetics
|January 31, 2018
PubMed
Summary
This summary is machine-generated.

Cloud computing offers scalable and reproducible solutions for genomics research, enabling large-scale analysis of growing sequencing data archives. Its features are ideal for reanalyzing public and sensitive data efficiently.

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Next-generation sequencing technologies have rapidly advanced, leading to exponential growth in sequencing data.
  • Public archives for raw sequencing data are expanding significantly, necessitating robust computational infrastructure.
  • Genomics research increasingly relies on large-scale computational resources to process and analyze vast datasets.

Purpose of the Study:

  • To describe the application of cloud computing in genomics research.
  • To highlight the benefits of cloud computing for large-scale genomics collaborations and data reanalysis.
  • To argue for the suitability of cloud computing for handling sensitive and publicly archived genomic data.

Main Methods:

  • Review of cloud computing adoption in genomics research.
  • Discussion of cloud computing features relevant to genomics: elasticity, reproducibility, and privacy.
  • Exploration of use cases for large-scale data reanalysis in public archives.

Main Results:

  • Cloud computing provides a scalable solution for the computational demands of genomics.
  • Elasticity, reproducibility, and privacy are key advantages of cloud platforms for genomic data analysis.
  • Cloud computing facilitates large-scale collaborations and the reanalysis of archived sequencing data.

Conclusions:

  • Cloud computing is a powerful and increasingly adopted tool for modern genomics research.
  • The features of cloud computing make it exceptionally well-suited for managing and analyzing large, complex genomic datasets.
  • Cloud platforms enable efficient and secure reanalysis of both public and privacy-protected genomic data.