SOrt-ITEMS: Sequence orthology based approach for improved taxonomic estimation of metagenomic sequences

Haque, Mohammed Monzoorul; Ghosh, Tarini Shankar; Komanduri, Dinakar; Mande, Sharmila S.

doi:10.1093/bioinformatics/btp317

Cited by 115 publications

(92 citation statements)

References 15 publications

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“…The similarity-based approaches classify DNA fragments based on sequence homology, which is determined by searching reference databases using tools like the Basic Local Alignment Search Tool (BLAST) (52,78). Examples of bioinformatic tools employing similarity-based binning are the Metagenome Analyzer (MEGAN) (52), CARMA (62), or the sequence ortholog-based approach for binning and improved taxonomic estimation of metagenomic sequences (Sort-ITEMS) (79). CARMA assigns environmental sequences to taxonomic categories based on similarities to protein families and domains included in the protein family database (Pfam) (30), whereas MEGAN and Sort-ITEMS classify sequences by performing comparisons against the NCBI nonredundant and NCBI nucleotide databases (101).…”

Section: Assessment Of Taxonomic and Functional Diversity Of Microbiamentioning

confidence: 99%

Metagenomic Analyses: Past and Future Trends

Simon

Daniel

2011

Appl Environ Microbiol

631

349

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Metagenomics has revolutionized microbiology by paving the way for a cultivation-independent assessment and exploitation of microbial communities present in complex ecosystems. Metagenomics comprising construction and screening of metagenomic DNA libraries has proven to be a powerful tool to isolate new enzymes and drugs of industrial importance. So far, the majority of the metagenomically exploited habitats comprised temperate environments, such as soil and marine environments. Recently, metagenomes of extreme environments have also been used as sources of novel biocatalysts. The employment of next-generation sequencing techniques for metagenomics resulted in the generation of large sequence data sets derived from various environments, such as soil, the human body, and ocean water. Analyses of these data sets opened a window into the enormous taxonomic and functional diversity of environmental microbial communities. To assess the functional dynamics of microbial communities, metatranscriptomics and metaproteomics have been developed. The combination of DNA-based, mRNA-based, and protein-based analyses of microbial communities present in different environments is a way to elucidate the compositions, functions, and interactions of microbial communities and to link these to environmental processes.

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Section: Assessment Of Taxonomic and Functional Diversity Of Microbiamentioning

confidence: 99%

Metagenomic Analyses: Past and Future Trends

Simon

Daniel

2011

Appl Environ Microbiol

631

349

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“…Current methods for classifying metagenomic samples rely on one or more of three general approaches: composition or pattern matching (McHardy et al 2007;Brady and Salzberg 2009;Segata et al 2012), taxonomic mapping (Huson et al 2007;Meyer et al 2008;Monzoorul Haque et al 2009;Gerlach and Stoye 2011;Patil et al 2012;Segata et al 2012), and whole-genome assembly (Kostic et al 2011;Bhaduri et al 2012). Composition and patternmatching algorithms use predetermined patterns in the data, such as taxonomic clade markers (Segata et al 2012), k-mer frequency, or GC content, often coupled with sophisticated classification algorithms such as support vector machines (McHardy et al 2007;Patil et al 2012) or interpolated Markov Models (Brady and Salzberg 2009) to classify reads to the species of interest.…”

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confidence: 99%

Pathoscope: Species identification and strain attribution with unassembled sequencing data

et al. 2013

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Emerging next-generation sequencing technologies have revolutionized the collection of genomic data for applications in bioforensics, biosurveillance, and for use in clinical settings. However, to make the most of these new data, new methodology needs to be developed that can accommodate large volumes of genetic data in a computationally efficient manner. We present a statistical framework to analyze raw next-generation sequence reads from purified or mixed environmental or targeted infected tissue samples for rapid species identification and strain attribution against a robust database of known biological agents. Our method, Pathoscope, capitalizes on a Bayesian statistical framework that accommodates information on sequence quality, mapping quality, and provides posterior probabilities of matches to a known database of target genomes. Importantly, our approach also incorporates the possibility that multiple species can be present in the sample and considers cases when the sample species/strain is not in the reference database. Furthermore, our approach can accurately discriminate between very closely related strains of the same species with very little coverage of the genome and without the need for multiple alignment steps, extensive homology searches, or genome assembly-which are time-consuming and labor-intensive steps. We demonstrate the utility of our approach on genomic data from purified and in silico ''environmental'' samples from known bacterial agents impacting human health for accuracy assessment and comparison with other approaches.

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“…The SOrt-ITEMS (Monzoorul Haque et al 2009) and CARMA3 (Gerlach & Stoye 2011) methods extended the LCA using a reciprocal BLAST search to reduce false positives in assignments. CARMA3 introduced the concept of the mutation rate into the LCA algorithm, and reinforced the reciprocal BLAST search to identify a novel taxon, relatives of which are numbered (Gerlach & Stoye 2011).…”

Section: Introductionmentioning

confidence: 99%

ELM: Enhanced lowest common ancestor based method for detecting a pathogenic virus from a large sequence dataset

Ueno

Ishii

Ito

2014

Preprint

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Emerging viral diseases, most of which are caused by the transmission of viruses from animals to humans, pose a threat to public health. Discovering pathogenic viruses through surveillance is the key to preparedness for this potential threat. Next generation sequencing (NGS) helps us to identify viruses without the design of a specific PCR primer. The major task in NGS data analysis is taxonomic identification for vast numbers of sequences. However, taxonomic identification via a BLAST search against all the known sequences is a computational bottleneck. Here we propose an enhanced lowest-common-ancestor based method (ELM) to effectively identify viruses from massive sequence data. To reduce the computational cost, ELM uses a customized database composed only of viral sequences for the BLAST search. At the same time, ELM adopts a novel criterion to suppress the rise in false positive assignments caused by the small database. As a result, identification by ELM is more than 1,000 times faster than the conventional methods without loss of accuracy. We anticipate that ELM will contribute to direct diagnosis of viral infections. The web server and the customized viral database are freely available at

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SOrt-ITEMS: Sequence orthology based approach for improved taxonomic estimation of metagenomic sequences

Abstract: SOrt-ITEMS software is available for download from: http://metagenomics.atc.tcs.com/binning/SOrt-ITEMS. No license is needed for academic and nonprofit use.

Cited by 115 publications

References 15 publications

Metagenomic Analyses: Past and Future Trends

Metagenomic Analyses: Past and Future Trends

Pathoscope: Species identification and strain attribution with unassembled sequencing data

ELM: Enhanced lowest common ancestor based method for detecting a pathogenic virus from a large sequence dataset

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