PhyloMap: an algorithm for visualizing relationships of large sequence data sets and its application to the influenza A virus genome
1 Institute of Biochemistry, Center for Structural and Cell Biology in Medicine, University of Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany
2 Graduate School for Computing in Medicine and Life Sciences, University of Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany
3 Institute for Neuro- and Bioinformatics, University of Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany
4 Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
5 Laboratory for Structural Biology of Infection and Inflammation, c/o DESY, Building 22a, Notkestr. 85, 22603 Hamburg, Germany
6 Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zu Chong Zhi Rd., Shanghai 201203, China
BMC Bioinformatics 2011, 12:248 doi:10.1186/1471-2105-12-248Published: 20 June 2011
Results of phylogenetic analysis are often visualized as phylogenetic trees. Such a tree can typically only include up to a few hundred sequences. When more than a few thousand sequences are to be included, analyzing the phylogenetic relationships among them becomes a challenging task. The recent frequent outbreaks of influenza A viruses have resulted in the rapid accumulation of corresponding genome sequences. Currently, there are more than 7500 influenza A virus genomes in the database. There are no efficient ways of representing this huge data set as a whole, thus preventing a further understanding of the diversity of the influenza A virus genome.
Here we present a new algorithm, "PhyloMap", which combines ordination, vector quantization, and phylogenetic tree construction to give an elegant representation of a large sequence data set. The use of PhyloMap on influenza A virus genome sequences reveals the phylogenetic relationships of the internal genes that cannot be seen when only a subset of sequences are analyzed.
The application of PhyloMap to influenza A virus genome data shows that it is a robust algorithm for analyzing large sequence data sets. It utilizes the entire data set, minimizes bias, and provides intuitive visualization. PhyloMap is implemented in JAVA, and the source code is freely available at http://www.biochem.uni-luebeck.de/public/software/phylomap.html webcite