Phylogenetic classification of short environmental DNA fragments

Krause, Lutz; Diaz, Naryttza N.; Goesmann, Alexander; Kelley, Scott T.; Nattkemper, Tim Wilhelm; Rohwer, Forest; Edwards, Robert A.; Stoye, Jens

doi:10.1093/nar/gkn038

Cited by 255 publications

(204 citation statements)

References 31 publications

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“…S3). The phylogenetic composition [bacterial phyla (32,33)] for both 16S rRNA gene and environmental gene tags (EGTs) (34) followed a similar distribution for metagenomic libraries (Fig. 1A), again highlighting the randomness of the sequenced libraries, as expected for a random sampling of genes.…”

Section: Resultsmentioning

confidence: 70%

“…1 A). Whereas classifying EGTs from short pyrosequencing reads has been challenging, a recent report demonstrates that EGTs as short as 27 aa can accurately be classified with an average specificity ranging from 97% for Superkingdom to 93% for Order (33). Bacterial specific EGTs represented from 91.3% to 95.2% of total EGTs and the distribution of phylotypes fell predominantly into the Bacteriodetes, Firmicutes, and Proteobacteria phyla, regardless of the microbiome analyzed.…”

Section: Resultsmentioning

confidence: 99%

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Gene-centric metagenomics of the fiber-adherent bovine rumen microbiome reveals forage specific glycoside hydrolases

Brulc

Antonopoulos

Miller

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

643

563

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The complex microbiome of the rumen functions as an effective system for the conversion of plant cell wall biomass to microbial protein, short chain fatty acids, and gases. As such, it provides a unique genetic resource for plant cell wall degrading microbial enzymes that could be used in the production of biofuels. The rumen and gastrointestinal tract harbor a dense and complex microbiome. To gain a greater understanding of the ecology and metabolic potential of this microbiome, we used comparative metagenomics (phylotype analysis and SEED subsystems-based annotations) to examine randomly sampled pyrosequence data from 3 fiber-adherent microbiomes and 1 pooled liquid sample (a mixture of the liquid microbiome fractions from the same bovine rumens). Even though the 3 animals were fed the same diet, the community structure, predicted phylotype, and metabolic potentials in the rumen were markedly different with respect to nutrient utilization. A comparison of the glycoside hydrolase and cellulosome functional genes revealed that in the rumen microbiome, initial colonization of fiber appears to be by organisms possessing enzymes that attack the easily available side chains of complex plant polysaccharides and not the more recalcitrant main chains, especially cellulose. Furthermore, when compared with the termite hindgut microbiome, there are fundamental differences in the glycoside hydrolase content that appear to be diet driven for either the bovine rumen (forages and legumes) or the termite hindgut (wood).CAZymes ͉ cellulases ͉ plant cell wall ͉ pyrosequencing H erbivores carry out a foregut fermentation that digests plant cell wall materials by a complex and efficient microbial process. The microbiome inhabiting the rumen is characterized by its high population density, wide diversity, and complexity of interactions. Bacteria predominate the rumen, with a variety of anaerobic protozoa and fungi (1), and the associated occurrence of bacteriophage is well documented (2). The use of small subunit (SSU) rRNA sequence analysis has allowed for a more complete description of the rumen microbiome and these inventories have demonstrated that a large microbial component remains uncultured (3)(4)(5)(6)(7)(8)(9)(10)(11)(12) and that a high proportion of the fibrolytic population has not been thoroughly described (7,8,13,14). The rumen habitat contains a consortium of microbes that harbor the complex lignocellulosic degradation system for the microbial attachment and digestion of plant biomass. However, the complex chemical processes required to break down the plant cell wall are rarely carried out by a single species. Evidence also suggests that the most important organisms and gene sets involved in the most efficient hydrolysis of plant cell walls are associated with the fiber portion of the rumen digesta (15). Because we continue to investigate the community structure of the rumen, it is also clear that the system is not fully characterized with respect to the metabolic potential, especially as the system relates to plant...

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Section: Resultsmentioning

confidence: 70%

Section: Resultsmentioning

confidence: 99%

Gene-centric metagenomics of the fiber-adherent bovine rumen microbiome reveals forage specific glycoside hydrolases

Brulc

Antonopoulos

Miller

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

643

563

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“…Pfam hits were post-processed with CARMA, an algorithm proposed by Krause et al (2008) that infers taxonomic affiliations from the alignments underlying Pfam Hidden Markov models. Here, we used a rewritten and improved version of the original algorithm.…”

Section: Data Sets For Som Constructionmentioning

confidence: 99%

“…An introduction to gene-level taxonomic classification is beyond the scope of this article. In brief, they are either based on the post-processing of BLASTP (Altschul et al, 1990) searches as in Phylogena (Hanekamp et al, 2007) or MEGAN (Huson et al, 2007), or on the post-processing of Pfam searches (Sonnhammer et al, 1997) as in CARMA (Krause et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Practical application of self-organizing maps to interrelate biodiversity and functional data in NGS-based metagenomics

et al. 2010

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Next-generation sequencing (NGS) technologies have enabled the application of broad-scale sequencing in microbial biodiversity and metagenome studies. Biodiversity is usually targeted by classifying 16S ribosomal RNA genes, while metagenomic approaches target metabolic genes. However, both approaches remain isolated, as long as the taxonomic and functional information cannot be interrelated. Techniques like self-organizing maps (SOMs) have been applied to cluster metagenomes into taxon-specific bins in order to link biodiversity with functions, but have not been applied to broad-scale NGS-based metagenomics yet. Here, we provide a novel implementation, demonstrate its potential and practicability, and provide a web-based service for public usage. Evaluation with published data sets mimicking varyingly complex habitats resulted into classification specificities and sensitivities of close to 100% to above 90% from phylum to genus level for assemblies exceeding 8 kb for low and medium complexity data. When applied to five real-world metagenomes of medium complexity from direct pyrosequencing of marine subsurface waters, classifications of assemblies above 2.5 kb were in good agreement with fluorescence in situ hybridizations, indicating that biodiversity was mostly retained within the metagenomes, and confirming high classification specificities. This was validated by two protein-based classifications (PBCs) methods. SOMs were able to retrieve the relevant taxa down to the genus level, while surpassing PBCs in resolution. In order to make the approach accessible to a broad audience, we implemented a feature-rich web-based SOM application named TaxSOM, which is freely available at http://www.megx.net/toolbox/taxsom. TaxSOM can classify reads or assemblies exceeding 2.5 kb with high accuracy and thus assists in linking biodiversity and functions in metagenome studies, which is a precondition to study microbial ecology in a holistic fashion.

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“…These methods take advantage of small number of fragments containing marker genes and require reads to have at least 1000 bps. Two other tools handle a larger number of fragments: MEGAN [13] and CARMA [14].…”

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confidence: 99%

Separating Metagenomic Short Reads into Genomes via Clustering

Tanaseichuk

Borneman

Jiang

2011

Lecture Notes in Computer Science

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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.

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Phylogenetic classification of short environmental DNA fragments

Cited by 255 publications

References 31 publications

Gene-centric metagenomics of the fiber-adherent bovine rumen microbiome reveals forage specific glycoside hydrolases

Gene-centric metagenomics of the fiber-adherent bovine rumen microbiome reveals forage specific glycoside hydrolases

Practical application of self-organizing maps to interrelate biodiversity and functional data in NGS-based metagenomics

Separating Metagenomic Short Reads into Genomes via Clustering

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