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

Abstract

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. 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.

Impact of Contaminating DNA in Whole-Genome Amplification Kits Used for Metagenomic Shotgun Sequencing for Infection Diagnosis

“…When the full LMAT database was used for classification of these samples, the reads were identified as human (data not shown). Similar findings with this database have been previously reported (32).…”

“…This highlights a limitation of many databases used for assigning a taxonomic assignment of reads. These tools make use of whole-genome sequencing data sets which, in many cases, can include contaminating human DNA mislabeled as microbial DNA (32). Additionally, we performed WGA in the reaction volumes recommended per the manufacturers' instructions.…”

“…Human and phiX sequences were then prefiltered using BioBloom tools (36) by aligning reads to a library of human and phiX sequences and computationally subtracting any reads matching these libraries. The Livermore metagenomics analysis tool kit (LMAT) was then used for taxonomic assignment of reads (30), utilizing the kMLϩHuman.v4-14.20.g10.db database (32) and setting a minimum identification score of 1.0, which is considered a high-quality identification. Reads identified as human by LMAT (taxonomy identifier number 9,606) were removed.…”

Toward Accurate and Quantitative Comparative Metagenomics

“…Sample collection, storage (Voigt et al, 2015), DNA extraction (Kennedy et al, 2014), and library preparation (Jones et al, 2015) all influence the taxonomic composition of a metagenome and, by extension, the functional composition. Variable amounts of DNA from the host (Ames et al, 2015), reagents (Tanner et al, 1998), and post-sampling environment (Salter et al, 2014) will be sequenced along with microbial DNA and can strongly influence coverage and quantification of microbiota, especially in low biomass body sites and environments (Weiss et al, 2014). Additionally, the amount of DNA extracted per cell depends on growth rate of microbial populations—actively dividing cells will yield more genomic DNA, which accumulates at the origin of replication (Korem et al, 2015).…”

A vision for ubiquitous sequencing

“…Additional mitigation techniques could rely on a built-in suppression of reads that align to the human genome before analysis or storage. While previous work showed that residual human sequences can pass this procedure (Ames et al 2015), implementing it will show that a reasonable effort was made to address privacy concerns. These mitigation techniques should be incorporated as part of the sequencer API by setting standard commands such as "get consent" or "suppress human DNA."…”