Comparative Analysis of Functional Metagenomic Annotation and the Mappability of Short Reads

Abstract

To assess the functional capacities of microbial communities, including those inhabiting the human body, shotgun metagenomic reads are often aligned to a database of known genes. Such homology-based annotation practices critically rely on the assumption that short reads can map to orthologous genes of similar function. This assumption, however, and the various factors that impact short read annotation, have not been systematically evaluated. To address this challenge, we generated an extremely large database of simulated reads (totaling 15.9 Gb), spanning over 500,000 microbial genes and 170 curated genomes and including, for many genomes, every possible read of a given length. We annotated each read using common metagenomic protocols, fully characterizing the effect of read length, sequencing error, phylogeny, database coverage, and mapping parameters. We additionally rigorously quantified gene-, genome-, and protocol-specific annotation biases. Overall, our findings provide a first comprehensive evaluation of the capabilities and limitations of functional metagenomic annotation, providing crucial goal-specific best-practice guidelines to inform future metagenomic research.

Toward Accurate and Quantitative Comparative Metagenomics

“…Commonly used sequencers have different error rates and patterns (Quail et al, 2012), but their effects on taxonomic (Sinha et al, 2015) and functional (Nayfach et al, 2015a) composition are surprisingly minimal (O’Sullivan et al, 2014). Read length, on the other hand, is a source of bias on its own (Carr and Borenstein, 2014; Nayfach and Pollard, 2015), in large part because it is more difficult to detect homology for short reads, especially when sampled from a taxonomic group that is poorly represented in reference databases (Wommack et al, 2008). Long-read technologies help with homology detection but are currently much lower throughput and also prone to insertion and deletion (indel) errors (Carneiro et al, 2012) that can affect read-mapping accuracy (Nguyen et al, 2014).…”

“…Studies examine different levels of taxonomic resolution, including individual strains (Box 2). As methods are rigorously benchmarked (Carr and Borenstein, 2014; Lindgreen et al, 2016; Nayfach et al, 2015a), iterative improvements and new approaches should soon enable accurate quantification of the abundances of individual taxa, genes, or pathways in a single metagenome.…”

Automated and accurate estimation of gene family abundance from shotgun metagenomes

“…Here, we build upon previous work [7,[19][20][21][22] and use mock communities and simulated metagenomes to systematically evaluate and optimize metagenome annotation (Fig 1). To our knowledge, our approach represents the first end-to-end evaluation and optimization of metagenome annotation.…”

High-resolution characterization of the human microbiome

“…Sample processing and library preparation can, for example, bias the predicted functional profile of a metagenomic sample 127 . A recent study systematically evaluated such homology-based annotation practices and demonstrated that variation introduced by computational protocol selection could completely mask true biological variation between samples, suggesting goal-specific best-practice guidelines for metagenomic annotation 128 . Moreover, once the samples’ functional profiles have been determined, rigorous normalization and calibration of samples are still required to allow accurate comparison across samples ( e.g.…”