Hyperbolic SOM-based clustering of DNA fragment features for taxonomic visualization and classification

Motivation: Modern high-throughput sequencing technologies enable the simultaneous analysis of organisms in an environment. The analysis of species diversity and the binning of DNA fragments of non-sequenced species for assembly are two major challenges in sequence analysis. To achieve reasonable bi...

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Veröffentlicht in:Bioinformatics Jg. 24; H. 14; S. 1568 - 1574
Hauptverfasser: Martin, Christian, Diaz, Naryttza N., Ontrup, Jörg, Nattkemper, Tim W.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Oxford University Press 15.07.2008
Oxford Publishing Limited (England)
Schlagworte:
Algorithms
Archaeal Proteins - genetics
Artificial Intelligence
Bacterial Proteins - genetics
Bioinformatics
Biological and medical sciences
Classification
Cluster Analysis
Computational Biology - methods
Databases, Genetic
Deoxyribonucleic acid
DNA
DNA - chemistry
Fundamental and applied biological sciences. Psychology
General aspects
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Models, Genetic
Models, Statistical
Multigene Family
Oligonucleotides - chemistry
Phylogeny
Sequence Analysis, DNA - methods
Species diversity
Visualization
Bioinformatics
DNA
Classification
ISSN:1367-4803, 1367-4811, 1460-2059, 1367-4811
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Zusammenfassung:Motivation: Modern high-throughput sequencing technologies enable the simultaneous analysis of organisms in an environment. The analysis of species diversity and the binning of DNA fragments of non-sequenced species for assembly are two major challenges in sequence analysis. To achieve reasonable binnings and classifications, DNA fragment structure has to be represented appropriately, so it can be processed by machine learning algorithms. Results: Hierarchically growing hyperbolic Self-Organizing maps (H 2SOMs) are trained to cluster small variable-length DNA fragments (0.2–50 kb) of 350 prokaryotic organisms at six taxonomic ranks Superkingdom, Phylum, Class, Order, Genus and Species in the Tree of Life. DNA fragments are mapped to three different types of feature vectors based on the genomic signature: basic features, features considering the importance of oligonucleotide patterns as well as contrast enhanced features. The H 2SOM classifier achieves high classification rates while at the same time its visualization allows further insights into the projected data and has the potential to support binning of short sequence reads, because DNA fragments can be grouped into phylogenetic groups. Availability: An implementation of the H 2HSOM classifier in Matlab is provided at www.techfak.uni-bielefeld.de/ags/ani/projects/HHSOMSeqData Contact: christian.martin@uni-bielefeld.de Supplementary Information: Supplementary data are available at Bioinformatics online.
Bibliographie:ArticleID:btn257
ark:/67375/HXZ-ML6BBMH0-L
Associate Editor: Limsoon Wong
To whom correspondence should be addressed.
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ISSN:1367-4803
1367-4811
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btn257