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Evolutionary Relationships through Genome Comparisons

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

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Comparing Mitochondrial, Chloroplast, and Prokaryotic Genomes

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Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

Published on: May 9, 2017

Cost-effective cloud computing: a case study using the comparative genomics tool, roundup.

Parul Kudtarkar¹, Todd F Deluca, Vincent A Fusaro

¹Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115.

Evolutionary Bioinformatics Online

|January 25, 2011

Summary

This summary is machine-generated.

Optimizing comparative genomics with cloud computing significantly reduces costs. This study demonstrates efficient ortholog computation using Amazon

Keywords:

Amazon Roundup cloud computing comparative genomics elastic computing cloud high performance computing orthologs

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Published on: November 30, 2014

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Comparative genomics resources are growing in scale and complexity.
Cloud computing offers a dynamic alternative for large bioinformatics tools.
Optimizing computational efficiency and cost-effectiveness is crucial for large-scale analyses.

Purpose of the Study:

To optimize the computation of the comparative genomics resource Roundup using cloud computing.
To describe principles for achieving computational efficiency on the cloud.
To detail procedures for improving cost-effectiveness in cloud-based bioinformatics.

Main Methods:

Utilized Roundup to compute orthologs among 902 genomes on Amazon's Elastic Compute Cloud.
Deployed Elastic MapReduce for managing ortholog processes and minimizing costs.
Developed a predictive model for cloud runtime to optimize job submission order.

Main Results:

Computed 245,323 genome-to-genome orthologous relationships in just over 200 hours.
Achieved an $8,000 USD cost, representing at least a 40% reduction compared to random job submission.
Developed a cost-reduction model adaptable for other comparative genomics tools.

Conclusions:

Cloud computing, when optimized, provides a cost-effective and efficient alternative to local infrastructure for comparative genomics.
The developed strategy and cost-reduction model offer significant benefits for researchers utilizing cloud platforms.
This approach maximizes computation at minimal costs, enhancing accessibility to large-scale genomic analyses.