BMC Bioinformatics | |
AKE - the Accelerated k-mer Exploration web-tool for rapid taxonomic classification and visualization | |
Methodology Article | |
Daniel Langenkämper1  Tim Wilhelm Nattkemper1  Alexander Goesmann2  | |
[1] Biodata Mining, Bielefeld University, Universitätsstraße 15, Bielefeld, Germany;Bioinformatik und Systembiologie, Justus Liebig University, Düsternbrooker Weg 20, Gießen, Germany; | |
关键词: Metagenomics; Classification; Acceleration; Web-based; H2SOM; k-mer; | |
DOI : 10.1186/s12859-014-0384-0 | |
received in 2014-07-17, accepted in 2014-11-12, 发布年份 2014 | |
来源: Springer | |
【 摘 要 】
BackgroundWith 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.ResultsIn 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).ConclusionWe 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).
【 授权许可】
CC BY
© Langenkämper et al.; licensee BioMed Central. 2014
【 预 览 】
Files | Size | Format | View |
---|---|---|---|
RO202311099701325ZK.pdf | 1990KB | download |
【 参考文献 】
- [1]
- [2]
- [3]
- [4]
- [5]
- [6]
- [7]
- [8]
- [9]
- [10]
- [11]
- [12]
- [13]
- [14]
- [15]
- [16]
- [17]
- [18]
- [19]
- [20]
- [21]
- [22]
- [23]
- [24]
- [25]
- [26]
- [27]
- [28]
- [29]
- [30]
- [31]