Nucleic Acids Research Advance Access originally published online on August 6, 2009
Nucleic Acids Research 2009 37(18):5959-5968; doi:10.1093/nar/gkp634
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Nucleic Acids Research, 2009, Vol. 37, No. 18 5959-5968
© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Computational Biology |
Detecting species-site dependencies in large multiple sequence alignments
1Institute of Hygiene and Microbiology, Josef-Schneider-Strasse 2/E1, 97080, 2Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, 3Bioinformatics for High-Throughput Technologies at the Chair of Algorithm Engineering (Ls11), Computer Science Department, TU Dortmund, 44221 Dortmund, 4Institute of Human Genetics, Biocenter, University of Würzburg, Würzburg, Germany and 5Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, 3400 N. Charles St., Baltimore, MD 21204, USA
*To whom correspondence should be addressed. Tel: +49 931 318 4563; Fax: +49 931 318 4552; Email: tobias.mueller{at}biozentrum.uni-wuerzburg.de
Received May 5, 2009. Revised June 29, 2009. Accepted July 16, 2009.
Multiple sequence alignments (MSAs) are one of the most important sources of information in sequence analysis. Many methods have been proposed to detect, extract and visualize their most significant properties. To the same extent that site-specific methods like sequence logos successfully visualize site conservations and sequence-based methods like clustering approaches detect relationships between sequences, both types of methods fail at revealing informational elements of MSAs at the level of sequence–site interactions, i.e. finding clusters of sequences and sites responsible for their clustering, which together account for a high fraction of the overall information of the MSA. To fill this gap, we present here a method that combines the Fisher score-based embedding of sequences from a profile hidden Markov model (pHMM) with correspondence analysis. This method is capable of detecting and visualizing group-specific or conflicting signals in an MSA and allows for a detailed explorative investigation of alignments of any size tractable by pHMMs. Applications of our methods are exemplified on an alignment of the Neisseria surface antigen LP2086, where it is used to detect sites of recombinatory horizontal gene transfer and on the vitamin K epoxide reductase family to distinguish between evolutionary and functional signals.