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Large scale microbiome profiling in the cloud.

Camilo Valdes1, Vitalii Stebliankin1, Giri Narasimhan1,2

  • 1Bioinformatics Research Group (BioRG), School of Computing and Information Sciences, Florida International University, Miami, FL, USA.

Bioinformatics (Oxford, England)
|September 13, 2019
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Summary
This summary is machine-generated.

Flint, a new metagenomics profiling pipeline, rapidly analyzes bacterial populations using large genome collections. This scalable, efficient big data solution makes advanced analysis accessible to more labs.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Metagenomics profiling typically aligns sequencing reads to reference genomes.
  • Existing tools struggle with large reference genome collections, limiting accuracy and completeness.
  • Scalable and efficient solutions are needed for comprehensive bacterial population analysis.

Purpose of the Study:

  • To develop a scalable and efficient metagenomics profiling pipeline.
  • To enable fast, real-time analysis against extensive bacterial genome collections.
  • To provide an affordable big data solution for laboratories with limited resources.

Main Methods:

  • Developed Flint, a metagenomics profiling pipeline utilizing Apache Spark.
  • Leveraged Spark's parallelism and streaming architecture for rapid read mapping.
  • Deployed on Amazon Elastic MapReduce for cloud-based processing.

Main Results:

  • Flint profiles 1 million reads against over 40,000 genomes in 67 seconds.
  • Achieved mapping rates of 55 million reads per hour at a low cost ($8.00 USD/hour).
  • Demonstrated an order-of-magnitude speed improvement over existing methods using a larger reference set.

Conclusions:

  • Flint offers a scalable, efficient, and affordable approach to bacterial metagenomics profiling.
  • The pipeline enables fast and accurate analysis with large reference genome collections.
  • Open-source availability promotes wider adoption and accessibility in research.