Skip Navigation



Bioinformatics Advance Access published online on June 5, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn257
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow Supplementary Data
Right arrow All Versions of this Article:
24/14/1568    most recent
btn257v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Martin, C.
Right arrow Articles by Nattkemper, T. W.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Martin, C.
Right arrow Articles by Nattkemper, T. W.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

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

Christian Martin a,b,*, Naryttza N. Diaz a,b, Jörg Ontrup a and Tim W. Nattkemper a

aAG Applied Neuroinformatics, Technical Faculty, Bielefeld University, Germany
bGraduate School Bioinformatics and Genome Research, CeBiTec, Bielefeld University, Germany

*To whom correspondence should be addressed. Christian Martin, E-mail: christian.martin{at}uni-bielefeld.de


  Abstract

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 classi cations, DNA fragment structure has to be represented appropriately, so it can be processed by machine learning algorithms.

Results: Hierarchical growing hyperbolic SOMs (H2SOMs) are trained to cluster small variable-length DNA fragments (0.2-50Kbp) 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 H2SOM classi er achieves high classi cation 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 H2SOM classi er in Matlab is provided at www.techfak.uni-bielefeld.de/ags/ani/projects/HHSOMSeqData

Contact: christian.martin{at}uni-bielefeld.de

Supplementary Information: Available at Bioinformatics online.

Associate Editor: Dr. Limsoon Wong

Received on February 22, 2008; revised on May 16, 2008; accepted on June 3, 2008

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.