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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

Classification accuracy for CoMeta in Experiment One.Accuracy of classification is shown when taking into account only the match files (dotted line with square mark) and when considering additionally the mismatch files (solid line with a circle mark). The performance curve reflects various k-mer lengths.
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pone.0121453.g004: Classification accuracy for CoMeta in Experiment One.Accuracy of classification is shown when taking into account only the match files (dotted line with square mark) and when considering additionally the mismatch files (solid line with a circle mark). The performance curve reflects various k-mer lengths.

Mentions: The precisions and sensitivities for CoMeta, depending on k, are shown in Fig 4. The results are presented with and without taking into account the mismatch files (MM). It may be noticed that for growing k up to k = 25 both precision and sensitivity grows, then sensitivity falls down. The reason is that with the increase of k, the number of unclassified sequences also increases.


CoMeta: classification of metagenomes using k-mers.

Kawulok J, Deorowicz S - PLoS ONE (2015)

Classification accuracy for CoMeta in Experiment One.Accuracy of classification is shown when taking into account only the match files (dotted line with square mark) and when considering additionally the mismatch files (solid line with a circle mark). The performance curve reflects various k-mer lengths.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0121453.g004: Classification accuracy for CoMeta in Experiment One.Accuracy of classification is shown when taking into account only the match files (dotted line with square mark) and when considering additionally the mismatch files (solid line with a circle mark). The performance curve reflects various k-mer lengths.
Mentions: The precisions and sensitivities for CoMeta, depending on k, are shown in Fig 4. The results are presented with and without taking into account the mismatch files (MM). It may be noticed that for growing k up to k = 25 both precision and sensitivity grows, then sensitivity falls down. The reason is that with the increase of k, the number of unclassified sequences also increases.

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