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CoMeta: classification of metagenomes using k-mers.

Kawulok J, Deorowicz S - PLoS ONE (2015)

Bottom Line: In CoMeta, we used the exact method for read classification using short subsequences (k-mers) and fast program for indexing large set of k-mers.In contrast to the most popular methods based on BLAST, where the query is compared with each reference sequence, we begin the classification from the top of the taxonomy tree to reduce the number of comparisons.The presented experimental study confirms that CoMeta outperforms other programs used in this context.

View Article: PubMed Central - PubMed

Affiliation: Institute of Informatics, Silesian University of Technology, Gliwice, Poland.

ABSTRACT
Nowadays, the study of environmental samples has been developing rapidly. Characterization of the environment composition broadens the knowledge about the relationship between species composition and environmental conditions. An important element of extracting the knowledge of the sample composition is to compare the extracted fragments of DNA with sequences derived from known organisms. In the presented paper, we introduce an algorithm called CoMeta (Classification of metagenomes), which assigns a query read (a DNA fragment) into one of the groups previously prepared by the user. Typically, this is one of the taxonomic rank (e.g., phylum, genus), however prepared groups may contain sequences having various functions. In CoMeta, we used the exact method for read classification using short subsequences (k-mers) and fast program for indexing large set of k-mers. In contrast to the most popular methods based on BLAST, where the query is compared with each reference sequence, we begin the classification from the top of the taxonomy tree to reduce the number of comparisons. The presented experimental study confirms that CoMeta outperforms other programs used in this context. CoMeta is available at https://github.com/jkawulok/cometa under a free GNU GPL 2 license.

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Related in: MedlinePlus

Taxonomy tree-based classification.Iterative execution of stage II (Classification) in Fig 1.
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pone.0121453.g002: Taxonomy tree-based classification.Iterative execution of stage II (Classification) in Fig 1.

Mentions: The proposed method consists of two major stages outlined in Figs 1 and 2. Firstly (in the database construction stage), the indexed k-mer databases of clustered reference sequences are constructed. Subsequently (in the classification stage), the reads are classified to various groups with the use of the databases. The second stage is composed of two steps. In the comparison step, the input reads are scored according to a number of databases ({Di}). In the assignment step, the reads are assigned to the best group. What is important, the classification stage is performed iteratively (for taxonomic classification) to search the taxonomy tree downwards.


CoMeta: classification of metagenomes using k-mers.

Kawulok J, Deorowicz S - PLoS ONE (2015)

Taxonomy tree-based classification.Iterative execution of stage II (Classification) in Fig 1.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4401624&req=5

pone.0121453.g002: Taxonomy tree-based classification.Iterative execution of stage II (Classification) in Fig 1.
Mentions: The proposed method consists of two major stages outlined in Figs 1 and 2. Firstly (in the database construction stage), the indexed k-mer databases of clustered reference sequences are constructed. Subsequently (in the classification stage), the reads are classified to various groups with the use of the databases. The second stage is composed of two steps. In the comparison step, the input reads are scored according to a number of databases ({Di}). In the assignment step, the reads are assigned to the best group. What is important, the classification stage is performed iteratively (for taxonomic classification) to search the taxonomy tree downwards.

Bottom Line: In CoMeta, we used the exact method for read classification using short subsequences (k-mers) and fast program for indexing large set of k-mers.In contrast to the most popular methods based on BLAST, where the query is compared with each reference sequence, we begin the classification from the top of the taxonomy tree to reduce the number of comparisons.The presented experimental study confirms that CoMeta outperforms other programs used in this context.

View Article: PubMed Central - PubMed

Affiliation: Institute of Informatics, Silesian University of Technology, Gliwice, Poland.

ABSTRACT
Nowadays, the study of environmental samples has been developing rapidly. Characterization of the environment composition broadens the knowledge about the relationship between species composition and environmental conditions. An important element of extracting the knowledge of the sample composition is to compare the extracted fragments of DNA with sequences derived from known organisms. In the presented paper, we introduce an algorithm called CoMeta (Classification of metagenomes), which assigns a query read (a DNA fragment) into one of the groups previously prepared by the user. Typically, this is one of the taxonomic rank (e.g., phylum, genus), however prepared groups may contain sequences having various functions. In CoMeta, we used the exact method for read classification using short subsequences (k-mers) and fast program for indexing large set of k-mers. In contrast to the most popular methods based on BLAST, where the query is compared with each reference sequence, we begin the classification from the top of the taxonomy tree to reduce the number of comparisons. The presented experimental study confirms that CoMeta outperforms other programs used in this context. CoMeta is available at https://github.com/jkawulok/cometa under a free GNU GPL 2 license.

Show MeSH
Related in: MedlinePlus