Accurate read-based metagenome characterization using a hierarchical suite of unique signatures

Abstract

A major challenge in the field of shotgun metagenomics is the accurate identification of organisms present within a microbial community, based on classification of short sequence reads. Though existing microbial community profiling methods have attempted to rapidly classify the millions of reads output from modern sequencers, the combination of incomplete databases, similarity among otherwise divergent genomes, errors and biases in sequencing technologies, and the large volumes of sequencing data required for metagenome sequencing has led to unacceptably high false discovery rates (FDR). Here, we present the application of a novel, gene-independent and signature-based metagenomic taxonomic profiling method with significantly and consistently smaller FDR than any other available method. Our algorithm circumvents false positives using a series of non-redundant signature databases and examines Genomic Origins Through Taxonomic CHAllenge (GOTTCHA). GOTTCHA was tested and validated on 20 synthetic and mock datasets ranging in community composition and complexity, was applied successfully to data generated from spiked environmental and clinical samples, and robustly demonstrates superior performance compared with other available tools.

Comprehensive Benchmarking and Ensemble Approaches for Metagenomic Classifiers

“…We compared the characteristics and parameters of a set of 11 metagenomic tools [14,[33][34][35][36][37][38][39][40][41][42][43][44] (Additional file 1: Table S1) representing a variety of classification approaches (k-mer composition, alignment, marker). We also present a comprehensive evaluation of their performance, using 35 simulated and biological metagenomes, across a wide range of GC content (14.5-74.8%), size (0.4-13.1 Mb), and species similarity characteristics (Additional file 2: Table S2).…”

A novel data structure to support ultra-fast taxonomic classification of metagenomic sequences with k-mer signatures

“…Besides the newly published tool Kaiju, Kraken and Clark were chosen based on the recommendation of a recent benchmarking paper (Lindgreen et al, 2016), which evaluated 14 tools using six datasets and subsequently declared Kraken and Clark the best performers over Genometa (Davenport et al, 2012), GOTTCHA (Freitas et al, 2015), LMAT (Ames et al, 2013), MEGAN (Huson et al, 2007(Huson et al, , 2011, MG-RAST (Meyer et al, 2008), the One Codex webserver, taxatortk (Drö ge et al, 2015), MetaPhlAn (Segata et al, 2012), MetaPhyler (Liu et al, 2010), mOTU (Sunagawa et al, 2013) and QIIME (Caporaso et al, 2010). The comparison was benchmarked against three publicly available datasets: HiSeq, MiSeq and SimBA5.…”

Survey of (Meta)genomic Approaches for Understanding Microbial Community Dynamics

“…Most of the mapping techniques as discussed above depend on 16S rRNA gene databases or essential genes requiring a read length on a higher side. Most recently Freitas et al [37] used hierarchical array of unique signatures. Current taxonomic profiling methodologies stay biased as gene based approach depends heavily on correct coding orientations which is not achievable while analyzing metagenome short reads data.…”

“…Current taxonomic profiling methodologies stay biased as gene based approach depends heavily on correct coding orientations which is not achievable while analyzing metagenome short reads data. GOTTCHA pipeline uses machine learning to determine the unique genomic region followed by deciphering the distribution and coverage of these specific regions [37]. Hence, depending on the type of raw data and system configuration available at our end, we can decide on the software to be used for metagenomic recruitment (Table 2).…”