Using populations of human and microbial genomes for organism detection in metagenomes

Ames, Sasha K.; Gardner, Shea N.; Marti, Jose Manuel; Slezak, Tom R.; Gokhale, Maya B.; Allen, Jonathan E.

doi:10.1101/gr.184879.114

Title: Using populations of human and microbial genomes for organism detection in metagenomes

Journal Article · Wed Apr 29 00:00:00 EDT 2015 · Genome Research

DOI:https://doi.org/10.1101/gr.184879.114· OSTI ID:1263571

Ames, Sasha K. ^[1]; Gardner, Shea N. ^[2]; Marti, Jose Manuel ^[3]; Slezak, Tom R. ^[4]; Gokhale, Maya B. ^[1]; Allen, Jonathan E. ^[4]

Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Center for Applied Scientific Computing
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Global Security Computer Applications Division
Univ. of Valencia, Valencia (Spain). Inst. de Fisica Corpuscular
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Global Security Computer Applications Division

Identifying causative disease agents in human patients from shotgun metagenomic sequencing (SMS) presents a powerful tool to apply when other targeted diagnostics fail. Numerous technical challenges remain, however, before SMS can move beyond the role of research tool. Accurately separating the known and unknown organism content remains difficult, particularly when SMS is applied as a last resort. The true amount of human DNA that remains in a sample after screening against the human reference genome and filtering nonbiological components left from library preparation has previously been underreported. In this study, we create the most comprehensive collection of microbial and reference-free human genetic variation available in a database optimized for efficient metagenomic search by extracting sequences from GenBank and the 1000 Genomes Project. The results reveal new human sequences found in individual Human Microbiome Project (HMP) samples. Individual samples contain up to 95% human sequence, and 4% of the individual HMP samples contain 10% or more human reads. In conclusion, left unidentified, human reads can complicate and slow down further analysis and lead to inaccurately labeled microbial taxa and ultimately lead to privacy concerns as more human genome data is collected.

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Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC52-07NA27344; 33-ER-2012; 08-ER-2011

OSTI ID:: 1263571

Journal Information:: Genome Research, Vol. 25, Issue 7; ISSN 1088-9051

Publisher:: Cold Spring Harbor Laboratory PressCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 24 works

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