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Evolutionary Relationships through Genome Comparisons

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Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms
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Published on: May 9, 2017

Cloud computing for comparative genomics.

Dennis P Wall1, Parul Kudtarkar, Vincent A Fusaro

  • 1Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA. dpwall@hms.harvard.edu

BMC Bioinformatics
|May 21, 2010
PubMed
Summary
This summary is machine-generated.

Cloud computing offers a cost-effective solution for large-scale comparative genomics. Redesigning the reciprocal smallest distance algorithm (RSD) for the cloud enabled rapid ortholog calculations across numerous genomes.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Comparative genomics is becoming increasingly compute-intensive due to the growing number of genome sequences.
  • Local computing infrastructure costs and capacity limitations pose challenges for large-scale genomic analyses.
  • Cloud computing environments offer a potential solution for efficient and cost-effective comparative genomics.

Purpose of the Study:

  • To evaluate the feasibility of cloud computing for large-scale comparative genomics.
  • To adapt the reciprocal smallest distance (RSD) algorithm for execution on cloud infrastructure.
  • To perform ortholog calculations across a diverse set of sequenced genomes using a cloud-based approach.

Main Methods:

  • Redesigned the reciprocal smallest distance (RSD) algorithm to operate within Amazon's Elastic Computing Cloud (EC2).
  • Utilized Amazon Web Services Elastic MapReduce for parallel processing across 100 compute nodes.
  • Executed over 300,000 RSD-cloud processes for ortholog identification.

Main Results:

  • The RSD-cloud successfully performed ortholog calculations on a wide range of genome sizes.
  • The entire computation completed in under 70 hours.
  • The total cost for the large-scale computation was $6,302 USD.

Conclusions:

  • Migrating comparative genomics algorithms to the cloud can significantly enhance speed and flexibility.
  • Cloud computing provides a manageable and cost-effective alternative to traditional local infrastructures for large-scale genomic analyses.
  • The methodology developed for adapting the RSD algorithm is applicable to other comparative genomics challenges.