Large Scale Microbiome Profiling in the Cloud

• NCJ Number: 254079
• Journal: Bioinformatics Volume: 35 Issue: 14 Dated: 2019 Pages: 113-122
• Author(s): Camilo Valdes; Vitalii Stebliankin; Giri Narasimhan
• Date Published: 2019
• Length: 10 pages
• Grant Number(s): 2017-NE-BX-0001
• Agencies: NIJ-Sponsored
• Publication Type: Research (Applied/Empirical); Report (Study/Research); Report (Grant Sponsored); Program/Project Description
• Award: Implementation of epigenetics into forensic science: Development of a PCR based multiplex for the simultaneous analysis of age and body fluid identification

Annotation

Since large reference genome collections are capable of providing a more complete and accurate profile of the bacterial population in a metagenomics dataset, this article reports on a project that developed a scalable, efficient, and affordable approach that brings big data solutions within the reach of laboratories with modest resources.

Abstract

Bacterial metagenomics profiling for metagenomic whole sequencing (mWGS) usually starts by aligning sequencing reads to a collection of reference genomes. Current profiling tools are designed to work against a small representative collection of genomes, and do not scale very well to larger reference genome collections. In addressing this issue, the current project developed FLINT, a metagenomics profiling pipeline that is built on top of the Apache Spark framework, and is designed for fast real-time profiling of metagenomic samples against a large collection of reference genomes. FLINT takes advantage of Spark's built-in parallelism and streaming engine architecture to quickly map reads against a large (170 GB) reference collection of 43,552 bacterial genomes from Ensembl. FLINT runs on Amazon's Elastic MapReduce service, and is able to profile 1 million Illumina paired-end reads against over 40K genomes on 64 machines in 67san order of magnitude faster than the state of the art, while using a much larger reference collection. Streaming the sequencing reads allows this approach to sustain mapping rates of 55 million reads per hour, at an hourly cluster cost of $8.00 USD, while avoiding the necessity of storing large quantities of intermediate alignments. (publisher abstract modified)