MetaCluster 4.0: A novel binning algorithm for NGS reads and huge number of species

Next-generation sequencing (NGS) technologies allow the sequencing of microbial communities directly from the environment without prior culturing. The output of environmental DNA sequencing consists of many reads from genomes of different unknown species, making the clustering together reads from the same (or similar) species (also known as binning) a crucial step. The difficulties of the binning problem are due to the following four factors: (1) the lack of reference genomes; (2) uneven abundance ratio of species; (3) short NGS reads; and (4) a large number of species (can be more than a hundred). None of the existing binning tools can handle all four factors. No tools, including both AbundanceBin and MetaCluster 3.0, have demonstrated reasonable performance on a sample with more than 20 species. In this article, we introduce MetaCluster 4.0, an unsupervised binning algorithm that can accurately (with about 80% precision and sensitivity in all cases and at least 90% in some cases) and efficiently bin short reads with varying abundance ratios and is able to handle datasets with 100 species. The novelty of MetaCluster 4.0 stems from solving a few important problems: how to divide reads into groups by a probabilistic approach, how to estimate the 4-mer distribution of each group, how to estimate the number of species, and how to modify MetaCluster 3.0 to handle a large number of species. We show that Meta Cluster 4.0 is effective for both simulated and real datasets. Supplementary Material is available at www.liebertonline.com/cmb. © 2012 Mary Ann Liebert, Inc.published_or_final_versio

Fast Comparison of Microbial Genomes Using the Chaos Games Representation for Metagenomic Applications

AbstractGenome sequencing technology is generating large databases of sequence at such a rate that advances in computer hardware alone are not adequate to handle them: more efficient algorithms are needed. Here an alignment-free method of sequence comparison and visualisation based on the Chaos Games Representation (CGR) and multifractal analysis is explored as an approach to search and filter through a data set of over 1500 microbial genomes. Whereas BLAST takes 25hours to search this data set with large sequence fragments (e.g. 100 Kb), the method introduced here can reduce this data set by 95% (from 1550 target species to just 50) in about 15minutes, and it is able to predict the exact species correctly in 67% of cases. The results presented here demonstrate that CGR is worth further investigation as a fast method to perform genome sequence comparison on large data sets, and various ways to further develop the method are discussed

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

All proceedings papers are available as open access at: OUP Bioinformatics (http://www.eccb12.org/proceedings-talks)MOTIVATION: Metagenomic binning remains an important topic in metagenomic analysis. Existing unsupervised binning methods for next-generation sequencing (NGS) reads do not perform well on (i) samples with low-abundance species or (ii) samples (even with high abundance) when there are many extremely low-abundance species. These two problems are common for real metagenomic datasets. Binning methods that can solve these problems are desirable. RESULTS: We proposed a two-round binning method (MetaCluster 5.0) that aims at identifying both low-abundance and high-abundance species in the presence of a large amount of noise due to many extremely low-abundance species. In summary, MetaCluster 5.0 uses a filtering strategy to remove noise from the extremely low-abundance species. It separate reads of high-abundance species from those of low-abundance species in two different rounds. To overcome the issue of low coverage for low-abundance species, multiple w values are used to group reads with overlapping w-mers, whereas reads from high-abundance species are grouped with high confidence based on a large w and then binning expands to low-abundance species using a relaxed (shorter) w. Compared to the recent tools, TOSS and MetaCluster 4.0, MetaCluster 5.0 can find more species (especially those with low abundance of say 6x to 10x) and can achieve better sensitivity and specificity using less memory and running time. AVAILABILITY: http://i.cs.hku.hk/alse/MetaCluster/ CONTACT: [email protected]_or_final_versionThe 11th European Conference on Computational Biology (ECCB'12), Basel, Switzerland, 9-12 September 2012. In Bioinformatics, 2012, v. 28 n. 18, p. i356-i36

MetaCluster-TA: taxonomic annotation for metagenomic data based on assembly-assisted binning

This article is part of the supplement: Selected articles from the Twelfth Asia Pacific Bioinformatics Conference (APBC 2014): GenomicsBackground Taxonomic annotation of reads is an important problem in metagenomic analysis. Existing annotation tools, which rely on the approach of aligning each read to the taxonomic structure, are unable to annotate many reads efficiently and accurately as reads (100 bp) are short and most of them come from unknown genomes. Previous work has suggested assembling the reads to make longer contigs before annotation. More reads/contigs can be annotated as a longer contig (in Kbp) can be aligned to a taxon even if it is from an unknown species as long as it contains a conserved region of that taxon. Unfortunately existing metagenomic assembly tools are not mature enough to produce long enough contigs. Binning tries to group reads/contigs of similar species together. Intuitively, reads in the same group (cluster) should be annotated to the same taxon and these reads altogether should cover a significant portion of the genome alleviating the problem of short contigs if the quality of binning is high. However, no existing work has tried to use binning results to help solve the annotation problem. This work explores this direction. Results In this paper, we describe MetaCluster-TA, an assembly-assisted binning-based annotation tool which relies on an innovative idea of annotating binned reads instead of aligning each read or contig to the taxonomic structure separately. We propose the novel concept of the 'virtual contig' (which can be up to 10 Kb in length) to represent a set of reads and then represent each cluster as a set of 'virtual contigs' (which together can be total up to 1 Mb in length) for annotation. MetaCluster-TA can outperform widely-used MEGAN4 and can annotate (1) more reads since the virtual contigs are much longer; (2) more accurately since each cluster of long virtual contigs contains global information of the sampled genome which tends to be more accurate than short reads or assembled contigs which contain only local information of the genome; and (3) more efficiently since there are much fewer long virtual contigs to align than short reads. MetaCluster-TA outperforms MetaCluster 5.0 as a binning tool since binning itself can be more sensitive and precise given long virtual contigs and the binning results can be improved using the reference taxonomic database. Conclusions MetaCluster-TA can outperform widely-used MEGAN4 and can annotate more reads with higher accuracy and higher efficiency. It also outperforms MetaCluster 5.0 as a binning tool.published_or_final_versio

Computational tools for viral metagenomics and their application in clinical research

AbstractThere are 100 times more virions than eukaryotic cells in a healthy human body. The characterization of human-associated viral communities in a non-pathological state and the detection of viral pathogens in cases of infection are essential for medical care and epidemic surveillance. Viral metagenomics, the sequenced-based analysis of the complete collection of viral genomes directly isolated from an organism or an ecosystem, bypasses the “single-organism-level” point of view of clinical diagnostics and thus the need to isolate and culture the targeted organism. The first part of this review is dedicated to a presentation of past research in viral metagenomics with an emphasis on human-associated viral communities (eukaryotic viruses and bacteriophages). In the second part, we review more precisely the computational challenges posed by the analysis of viral metagenomes, and we illustrate the problem of sequences that do not have homologs in public databases and the possible approaches to characterize them

MetaCluster 4.0: A Novel Binning Algorithm for NGS Reads and Huge Number of Species

Next-generation sequencing (NGS) technologies allow the sequencing of microbial communities directly from the environment without prior culturing. The output of environmental DNA sequencing consists of many reads from genomes of different unknown species, making the clustering together reads from the same (or similar) species (also known as binning) a crucial step. The difficulties of the binning problem are due to the following four factors: (1) the lack of reference genomes; (2) uneven abundance ratio of species; (3) short NGS reads; and (4) a large number of species (can be more than a hundred). None of the existing binning tools can handle all four factors. No tools, including both AbundanceBin and MetaCluster 3.0, have demonstrated reasonable performance on a sample with more than 20 species. In this article, we introduce MetaCluster 4.0, an unsupervised binning algorithm that can accurately (with about 80 % precision and sensitivity in all cases and at least 90 % in some cases) and efficiently bin short reads with varying abundance ratios and is able to handle datasets with 100 species. The novelty of MetaCluster 4.0 stems from solving a few important problems: how to divide reads into groups by a probabilistic approach, how to estimate the 4-mer distribution of each group, how to estimate the number of species, and how to modify MetaCluster 3.0 to handle a large number of species. We show that Meta Cluster 4.0 is effective for both simulated and real datasets. Supplementary Material is available at www.liebertonline.com/cmb. Key words: binning, environmental genomics, metagenomics. 1

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

Interest in the expanding catalog of uncultivated microorganisms, increasing recognition of heterogeneity among seemingly similar cells, and technological advances in whole-genome amplification and single-cell manipulation are driving considerable progress in single-cell genomics. Here, the spectrum of applications for single-cell genomics, key advances in the development of the field, and emerging methodology for single-cell genome sequencing are reviewed by example with attention to the diversity of approaches and their unique characteristics. Experimental strategies transcending specific methodologies are identified and organized as a road map for future studies in single-cell genomics of environmental microorganisms. Over the next decade, increasingly powerful tools for single-cell genome sequencing and analysis will play key roles in accessing the genomes of uncultivated organisms, determining the basis of microbial community functions, and fundamental aspects of microbial population biology.National Institutes of Health (U.S.) (R01 HG004863)Burroughs Wellcome Fun

A metagenomic insight into the role of wastewater treatment plants as potential hotspot for antibiotic resistant bacteria and antibiotic resistance genes.

Master of Science in Microbiology. University of KwaZulu-Natal, Durban 2016.Abstract available in PDF file