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AKE - the Accelerated k-mer Exploration web-tool for rapid taxonomic classification and visualization.

Langenkämper D, Goesmann A, Nattkemper TW - BMC Bioinformatics (2014)

Bottom Line: With the advent of low cost, fast sequencing technologies metagenomic analyses are made possible.The large data volumes gathered by these techniques and the unpredictable diversity captured in them are still, however, a challenge for computational biology.We show that the execution time of this approach is orders of magnitude shorter than competitive approaches and that accuracy is comparable.

View Article: PubMed Central - PubMed

Affiliation: Biodata Mining, Bielefeld University, Universitätsstraße 15, Bielefeld, Germany. dlangenk@cebitec.uni-bielefeld.de.

ABSTRACT

Background: With the advent of low cost, fast sequencing technologies metagenomic analyses are made possible. The large data volumes gathered by these techniques and the unpredictable diversity captured in them are still, however, a challenge for computational biology.

Results: In this paper we address the problem of rapid taxonomic assignment with small and adaptive data models (< 5 MB) and present the accelerated k-mer explorer (AKE). Acceleration in AKE's taxonomic assignments is achieved by a special machine learning architecture, which is well suited to model data collections that are intrinsically hierarchical. We report classification accuracy reasonably well for ranks down to order, observed on a study on real world data (Acid Mine Drainage, Cow Rumen).

Conclusion: We show that the execution time of this approach is orders of magnitude shorter than competitive approaches and that accuracy is comparable. The tool is presented to the public as a web application (url: https://ani.cebitec.uni-bielefeld.de/ake/ , username: bmc, password: bmcbioinfo).

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Related in: MedlinePlus

Results of the AMD study. Comparison of AKE/NBC/PhylopythiaS with a generic model/PhylopythiaS with a sample specific model. Data for NBC/PhylopythiaS derived from [11].
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Fig3: Results of the AMD study. Comparison of AKE/NBC/PhylopythiaS with a generic model/PhylopythiaS with a sample specific model. Data for NBC/PhylopythiaS derived from [11].

Mentions: The high abundant species are Thermoplasmatales archaeon Gpl (410), Leptospirillum sp. Group II (70), Leptospirillum sp. Group III (474), Ferroplasma acidarmanus Type I (170), Ferroplasma acidarmanus Type II (59). When looking at the results (Figure 3) we see that AKE outperforms NBC and PhylopythiaS (generic model). But it is outperformed by PhylopythiaS employing a sample specific model.Figure 3


AKE - the Accelerated k-mer Exploration web-tool for rapid taxonomic classification and visualization.

Langenkämper D, Goesmann A, Nattkemper TW - BMC Bioinformatics (2014)

Results of the AMD study. Comparison of AKE/NBC/PhylopythiaS with a generic model/PhylopythiaS with a sample specific model. Data for NBC/PhylopythiaS derived from [11].
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4307196&req=5

Fig3: Results of the AMD study. Comparison of AKE/NBC/PhylopythiaS with a generic model/PhylopythiaS with a sample specific model. Data for NBC/PhylopythiaS derived from [11].
Mentions: The high abundant species are Thermoplasmatales archaeon Gpl (410), Leptospirillum sp. Group II (70), Leptospirillum sp. Group III (474), Ferroplasma acidarmanus Type I (170), Ferroplasma acidarmanus Type II (59). When looking at the results (Figure 3) we see that AKE outperforms NBC and PhylopythiaS (generic model). But it is outperformed by PhylopythiaS employing a sample specific model.Figure 3

Bottom Line: With the advent of low cost, fast sequencing technologies metagenomic analyses are made possible.The large data volumes gathered by these techniques and the unpredictable diversity captured in them are still, however, a challenge for computational biology.We show that the execution time of this approach is orders of magnitude shorter than competitive approaches and that accuracy is comparable.

View Article: PubMed Central - PubMed

Affiliation: Biodata Mining, Bielefeld University, Universitätsstraße 15, Bielefeld, Germany. dlangenk@cebitec.uni-bielefeld.de.

ABSTRACT

Background: With the advent of low cost, fast sequencing technologies metagenomic analyses are made possible. The large data volumes gathered by these techniques and the unpredictable diversity captured in them are still, however, a challenge for computational biology.

Results: In this paper we address the problem of rapid taxonomic assignment with small and adaptive data models (< 5 MB) and present the accelerated k-mer explorer (AKE). Acceleration in AKE's taxonomic assignments is achieved by a special machine learning architecture, which is well suited to model data collections that are intrinsically hierarchical. We report classification accuracy reasonably well for ranks down to order, observed on a study on real world data (Acid Mine Drainage, Cow Rumen).

Conclusion: We show that the execution time of this approach is orders of magnitude shorter than competitive approaches and that accuracy is comparable. The tool is presented to the public as a web application (url: https://ani.cebitec.uni-bielefeld.de/ake/ , username: bmc, password: bmcbioinfo).

Show MeSH
Related in: MedlinePlus