Taxonomic classification of metagenomic shotgun sequences with CARMA3

Gerlach, Wolfgang; Stoye, Jens

doi:10.1093/nar/gkr225

Cited by 108 publications

(88 citation statements)

References 29 publications

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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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confidence: 99%

Pathoscope: Species identification and strain attribution with unassembled sequencing data

et al. 2013

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“…[154][155][156][157]. The third method is based on the assembly of the obtained contigs into genes or even genomes de novo [158,159].…”

Section: Review Evolution Of Infectionsmentioning

confidence: 99%

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2017

Bulletin of RSMU

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“…According to the literature on taxonomic classification, the sequence similarity search of BLAST is a computational bottleneck (Gerlach & Stoye 2011). Therefore, we used the customized viral database for a BLAST search to investigate how much the computational time was reduced and whether the conventional LCA could identify the infecting viruses.…”

Section: Taxonomic Classification Using the Lca With Blastn Virusesmentioning

confidence: 99%

“…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%

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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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Taxonomic classification of metagenomic shotgun sequences with CARMA3

Cited by 108 publications

References 29 publications

Pathoscope: Species identification and strain attribution with unassembled sequencing data

Pathoscope: Species identification and strain attribution with unassembled sequencing data

Untitled

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

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