Separating Metagenomic Short Reads into Genomes via Clustering

Abstract

Background:The metagenomics approach allows the simultaneous sequencing of all genomes in an environmental sample. This results in high complexity datasets, where in addition to repeats and sequencing errors, the number of genomes and their abundance ratios are unknown. Recently developed next-generation sequencing (NGS) technologies significantly improve the sequencing efficiency and cost. On the other hand, they result in shorter reads, which makes the separation of reads from different species harder. Among the existing computational tools for metagenomic analysis, there are similarity-based methods that use reference databases to align reads and composition-based methods that use composition patterns (i.e., frequencies of short words or l-mers) to cluster reads. Similarity-based methods are unable to classify reads from unknown species without close references (which constitute the majority of reads). Since composition patterns are preserved only in significantly large fragments, composition-based tools cannot be used for very short reads, which becomes a significant limitation with the development of NGS. A recently proposed algorithm, AbundanceBin, introduced another method that bins reads based on predicted abundances of the genomes sequenced. However, it does not separate reads from genomes of similar abundance levels. Results: In this work, we present a two-phase heuristic algorithm for separating short paired-end reads from different genomes in a metagenomic dataset. We use the observation that most of the l-mers belong to unique genomes when l is sufficiently large. The first phase of the algorithm results in clusters of l-mers each of which belongs to one genome. During the second phase, clusters are merged based on l-mer repeat information. These final clusters are used to assign reads. The algorithm could handle very short reads and sequencing errors. It is initially designed for genomes with similar abundance levels and then extended to handle arbitrary abundance ratios. The software can be download for free at http://www.cs.ucr.edu/ ∼ tanaseio/toss.htm. Conclusions: Our tests on a large number of simulated metagenomic datasets concerning species at various phylogenetic distances demonstrate that genomes can be separated if the number of common repeats is smaller than the number of genome-specific repeats. For such genomes, our method can separate NGS reads with a high precision and sensitivity.

MetaCluster 5.0: a two-round binning approach for metagenomic data for low-abundance species in a noisy sample

“…AbundanceBin ( Wu and Ye, 2011 ) groups reads based on Observation (A) but fails when the species in the sample have similar abundance. TOSS ( Tanaseichuk et al , 2011 ) bins reads based on Observations (A) and (B), and since TOSS relies on AbundanceBin to handle genomes with different abundances, it carries all the shortcomings of AbundanceBin. MetaCluster 4.0 ( Wang et al , 2012 ) has three phases: Phase 1 groups reads together based on Observation (B); Phase 2 derives the q -mer distribution of each group and Phase 3 merges the groups of reads based on Observation (C) by the well-known K -means clustering approach.…”

Shared Nearest Neighbor clustering in a Locality Sensitive Hashing framework

“…Sequence similarities are typically identified by comparing occurence patterns of relatively short DNA substrings of length l between the sequences [50,55]. Two broad scenarii can be used to assess l-mer-based similarities: abundance-based methods make use of relatively large l values (l ≥ 20) in order to ensure the uniqueness of most l-mers [50], while composition-based methods rely on smaller l values. Since DNA is a combination of four different types of nucleotides (A,T,G,C), there are at most 4 l l-mer combinations forming the feature vector.…”

The future is now: single-cell genomics of bacteria and archaea

“…Composite genomes can be amassed from metagenomic contigs by classifying (or 'binning') reads according to the abundance of related reads and lineage-specific signatures such as nucleotide content signatures (Tyson et al, 2004;Woyke et al, 2006;Dick et al, 2009;Hess et al, 2011;Luo et al, 2011;Tanaseichuk et al, 2011;Wang et al, 2012b;Fig. 1a).…”