CLARK: fast and accurate classification of metagenomic and genomic sequences using discriminative k-mers.

BackgroundThe problem of supervised DNA sequence classification arises in several fields of computational molecular biology. Although this problem has been extensively studied, it is still computationally challenging due to size of the datasets that modern sequencing technologies can produce.ResultsWe introduce CLARK a novel approach to classify metagenomic reads at the species or genus level with high accuracy and high speed. Extensive experimental results on various metagenomic samples show that the classification accuracy of CLARK is better or comparable to the best state-of-the-art tools and it is significantly faster than any of its competitors. In its fastest single-threaded mode CLARK classifies, with high accuracy, about 32 million metagenomic short reads per minute. CLARK can also classify BAC clones or transcripts to chromosome arms and centromeric regions.ConclusionsCLARK is a versatile, fast and accurate sequence classification method, especially useful for metagenomics and genomics applications. It is freely available at http://clark.cs.ucr.edu/

Pseudoalignment for metagenomic read assignment

Motivation: Read assignment is an important first step in many metagenomic analysis workflows, providing the basis for identification and quantification of species. However ambiguity among the sequences of many strains makes it difficult to assign reads at the lowest level of taxonomy, and reads are typically assigned to taxonomic levels where they are unambiguous. We explore connections between metagenomic read assignment and the quantification of transcripts from RNA-Seq data in order to develop novel methods for rapid and accurate quantification of metagenomic strains. Results: We find that the recent idea of pseudoalignment introduced in the RNA-Seq context is highly applicable in the metagenomics setting. When coupled with the Expectation-Maximization (EM) algorithm, reads can be assigned far more accurately and quickly than is currently possible with state of the art software, making it possible and practical for the first time to analyze abundances of individual genomes in metagenomics projects

MTR: taxonomic annotation of short metagenomic reads using clustering at multiple taxonomic ranks

Motivation: Metagenomics is a recent field of biology that studies microbial communities by analyzing their genomic content directly sequenced from the environment. A metagenomic dataset consists of many short DNA or RNA fragments called reads. One interesting problem in metagenomic data analysis is the discovery of the taxonomic composition of a given dataset. A simple method for this task, called the Lowest Common Ancestor (LCA), is employed in state-of-the-art computational tools for metagenomic data analysis of very short reads (about 100 bp). However LCA has two main drawbacks: it possibly assigns many reads to high taxonomic ranks and it discards a high number of reads

Taxonomic classification of metagenomic sequences

Gerlach W. Taxonomic classification of metagenomic sequences. Bielefeld: Universität; 2012.Bacteria, archaea and microeukaryotes can be found in almost every habitat present in nature, in particular in soil, sediments and sea water. They typically live in complex communities with different kinds of symbiotic associations which include relationships with larger organisms like animals or plants. Examples are microbial communities in the gut or on the skin of animals and humans, or bacteria that live in symbiosis with plants. The vast majority of such microbes are unculturable and thus cannot be sequenced by means of traditional methods. The recently upcoming discipline of metagenomics provides various in vivo- and in silico-tools to overcome this limitation. In particular, high-throughput sequencing techniques like 454 or Solexa-Illumina make it possible to explore those microbes by studying whole natural microbial communities and analysing their biological diversity as well as the underlying metabolic pathways. A current limitation of theses technologies is that they can sequence only DNA fragments of a limited length. With this limitation it is usually not possible to recover complete microbial genomes. In addition, the DNA fragments are drawn randomly from the microbial communities and the exact species of origin is unknown. Over the past few years, different methods have been developed for the taxonomic and functional characterization of metagenomic shotgun sequences. However, the taxonomic classification of metagenomic sequences from novel species without close homologues in the biological sequence databases poses a challenge due to the high number of wrong taxonomic predictions on lower taxonomic ranks. In this thesis we present CARMA3, a novel method for the taxonomic classification of assembled and unassembled metagenomic sequences that has been adapted to work with both BLAST and HMMER3 homology searches. CARMA3 accepts protein-encoding DNA sequences, protein sequences, and 16S-rDNA sequences as input. In addition, we present WebCARMA, a web application for the analysis of protein-encoding DNA sequences with CARMA3 without the need for a local installation. We evaluate our novel method in different experiments using simulated and real shotgun metagenomes and show that CARMA3 makes fewer wrong taxonomic predictions (at the same sensitivity) than other BLAST-based methods. In the last experiment we show that also very short reads can, in principle, be used to describe the taxonomic content of a metagenome

Evaluating techniques for metagenome annotation using simulated sequence data

The advent of next-generation sequencing has allowed huge amounts of DNA sequence data to be produced, advancing the capabilities of microbial ecosystem studies. The current challenge is identifying from which microorganisms and genes the DNA originated. Several tools and databases are available for annotating DNA sequences. The tools, databases and parameters used can have a significant impact on the results: naïve choice of these factors can result in a false representation of community composition and function. We use a simulated metagenome to show how different parameters affect annotation accuracy by evaluating the sequence annotation performances of MEGAN, MG-RAST, One Codex and Megablast. This simulated metagenome allowed the recovery of known organism and function abundances to be quantitatively evaluated, which is not possible for environmental metagenomes. The performance of each program and database varied, e.g. One Codex correctly annotated many sequences at the genus level, whereas MG-RAST RefSeq produced many false positive annotations. This effect decreased as the taxonomic level investigated increased. Selecting more stringent parameters decreases the annotation sensitivity, but increases precision. Ultimately, there is a trade-off between taxonomic resolution and annotation accuracy. These results should be considered when annotating metagenomes and interpreting results from previous studies

Selection of Appropriate Metagenome Taxonomic Classifiers for Ancient Microbiome Research

Metagenomics enables the study of complex microbial communities from myriad sources, including the remains of oral and gut microbiota preserved in archaeological dental calculus and paleofeces, respectively. While accurate taxonomic assignment is essential to this process, DNA damage, characteristic to ancient samples (e.g. reduction in fragment size), may reduce the accuracy of read taxonomic assignment. Using a set of in silico-generated metagenomic datasets we investigated how the addition of ancient DNA (aDNA) damage patterns influences microbial taxonomic assignment by five widely-used profilers: QIIME/UCLUST, MetaPhlAn2, MIDAS, CLARK-S, and MALT (BLAST-X-mode). In silico-generated datasets were designed to mimic dental plaque, consisting of 40, 100, and 200 microbial species/strains, both with and without simulated aDNA damage patterns. Following taxonomic assignment, the profiles were evaluated for species presence/absence, relative abundance, alpha-diversity, beta-diversity, and specific taxonomic assignment biases. Unifrac metrics indicated that both MIDAS and MetaPhlAn2 provided the most accurate community structure reconstruction. QIIME/UCLUST, CLARK-S, and MALT had the highest number of inaccurate taxonomic assignments; however, filtering out species present at lt;0.1% abundance greatly increased the accuracy of CLARK-S and MALT. All programs except CLARK-S failed to detect some species from the input file that were in their databases. Ancient DNA damage resulted in minimal differences in species detection and relative abundance between simulated ancient and modern datasets for most programs. In conclusion, taxonomic profiling biases are program-specific rather than damage-dependent, and the choice of taxonomic classification program to use should be tailored to the research question