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Related Concept Videos

Next-generation Sequencing03:00

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
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Evolutionary Relationships through Genome Comparisons

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RNA-seq

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Molecular taxonomy has revolutionized the understanding and classification of bacteria, providing precise insights into their diversity, evolutionary relationships, and ecological roles. By utilizing molecular techniques such as DNA sequencing and fingerprinting, researchers have made significant strides in various fields related to bacterial studies.Resolving Taxonomic AmbiguitiesMolecular taxonomy has been instrumental in distinguishing closely related bacterial species initially thought to...

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Resources and costs for microbial sequence analysis evaluated using virtual machines and cloud computing.

Samuel V Angiuoli1, James R White, Malcolm Matalka

  • 1Institute for Genome Sciences, University of Maryland Baltimore, Baltimore, Maryland, United States of America. angiuoli@umiacs.umd.edu

Plos One
|October 27, 2011
PubMed
Summary
This summary is machine-generated.

Cloud computing offers a cost-effective solution for microbial genomics bioinformatics, significantly reducing costs and analysis time for common applications like 16S rRNA and whole-genome shotgun sequencing.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cloud Computing

Background:

  • The growing demand for next-generation sequencing analysis faces a "bioinformatics bottleneck" due to unclear costs and infrastructure needs.
  • Cloud computing presents a potential solution for bioinformatics support but requires thorough evaluation.

Purpose of the Study:

  • To benchmark costs and runtimes for common microbial genomics applications using cloud computing.
  • To evaluate the feasibility of cloud-based bioinformatics support for small- to midsize sequencing facilities.

Main Methods:

  • Utilized automated analysis pipelines (CloVR virtual machine) for transparency and reproducibility.
  • Employed Amazon Elastic Compute Cloud (EC2) to benchmark costs and runtimes for various microbial genomics analyses.
  • Selected sequence dataset types and sizes representative of 454 and Illumina platforms.

Main Results:

  • Significant variations in computational requirements, runtimes, and costs were observed across different microbial genomics applications.
  • Microbial genome and metagenome analyses on EC2, utilizing up to 120 CPUs, completed in under 24 hours for less than $60.
  • 16S rRNA analyses finished on a single-CPU desktop in under three hours.

Conclusions:

  • Cloud-based bioinformatics support using CloVR and Amazon EC2 is a cost-efficient alternative to local computing centers for smaller sequencing facilities.
  • The choice of bioinformatics infrastructure depends on specific dataset characteristics and analysis protocols.
  • This approach supports 16S rRNA amplicon sequencing, microbial single-genome, and metagenomics whole-genome shotgun projects.