Genometa - A Fast and Accurate Classifier for Short Metagenomic Shotgun Reads

Abstract

SummaryMetagenomic studies use high-throughput sequence data to investigate microbial communities in situ. However, considerable challenges remain in the analysis of these data, particularly with regard to speed and reliable analysis of microbial species as opposed to higher level taxa such as phyla. We here present Genometa, a computationally undemanding graphical user interface program that enables identification of bacterial species and gene content from datasets generated by inexpensive high-throughput short read sequencing technologies. Our approach was first verified on two simulated metagenomic short read datasets, detecting 100% and 94% of the bacterial species included with few false positives or false negatives. Subsequent comparative benchmarking analysis against three popular metagenomic algorithms on an Illumina human gut dataset revealed Genometa to attribute the most reads to bacteria at species level (i.e. including all strains of that species) and demonstrate similar or better accuracy than the other programs. Lastly, speed was demonstrated to be many times that of BLAST due to the use of modern short read aligners. Our method is highly accurate if bacteria in the sample are represented by genomes in the reference sequence but cannot find species absent from the reference. This method is one of the most user-friendly and resource efficient approaches and is thus feasible for rapidly analysing millions of short reads on a personal computer.AvailabilityThe Genometa program, a step by step tutorial and Java source code are freely available from http://genomics1.mh-hannover.de/genometa/ and on http://code.google.com/p/genometa/. This program has been tested on Ubuntu Linux and Windows XP/7.

Scalable metagenomic taxonomy classification using a reference genome database

“…To compare performance with existing state-of-the-art published tools, accuracy was compared with PhymmBL (Brady and Salzberg, 2011), MetaPhlAn (Segata et al , 2012) and Genometa (Davenport et al , 2012). PhymmBL balances classifying known species with classifying novel organisms but uses BLAST, which does not scale well with sequencer output (Angiuoli et al , 2011).…”

“…This approach was shown to speed up BLAST and BLAT genome database searches (Loh et al , 2012). Faster database search methods apply larger search seeds, and examples include BLAT (Sharma et al , 2012), BWA (Davenport et al , 2012) and other read mapping tools (Martin et al , 2012), but analyzing the search results remains a challenge with some approaches selecting the lowest common ancestor (LCA) of multiple matches and others using variants of a best match selection procedure to improve rank specificity of the reported taxonomic label. Moreover, parameter settings of the search tools can dramatically alter the outcome of the reported label and must be considered carefully (Mande et al , 2012).…”

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.…”

metaBEETL: high-throughput analysis of heterogeneous microbial populations from shotgun DNA sequences

“…A similar bottleneck arises when NGS data sets are aligned to a reference genome sequence and has been met by a new generation of alignment tools, many of which achieve their efficiency by converting the reference genome to an index data structure based on the Burrows-Wheeler Transform (BWT). The tool Genometa [10] leverages these advances by first using either Bowtie [11] or BWA [12] to align ESS reads to a set of bacterial genome sequences and then post-processing the resulting alignments into taxonomic assignments.…”

“…To test the accuracy of metaBEETL we simulated an artificial metagenome and classified its reads using metaBEETL, comparing the results against classifications from CARMA3 [8], MEGAN [7] and Genometa [10]. …”