A novel hierarchical clustering algorithm for gene sequences
1 Cognitive Science Department & Fujian Key Laboratory of the Brain-like Intelligent Systems, Xiamen University, Xiamen, China
2 Shenzhen Key Lab for High Performance Data Mining, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
3 Department of Computer Sciences, University of Sherbrooke, Sherbrooke, QC, Canada
Citation and License
BMC Bioinformatics 2012, 13:174 doi:10.1186/1471-2105-13-174Published: 23 July 2012
Clustering DNA sequences into functional groups is an important problem in bioinformatics. We propose a new alignment-free algorithm, mBKM, based on a new distance measure, DMk, for clustering gene sequences. This method transforms DNA sequences into the feature vectors which contain the occurrence, location and order relation of k-tuples in DNA sequence. Afterwards, a hierarchical procedure is applied to clustering DNA sequences based on the feature vectors.
The proposed distance measure and clustering method are evaluated by clustering functionally related genes and by phylogenetic analysis. This method is also compared with BlastClust, CD-HIT-EST and some others. The experimental results show our method is effective in classifying DNA sequences with similar biological characteristics and in discovering the underlying relationship among the sequences.
We introduced a novel clustering algorithm which is based on a new sequence similarity measure. It is effective in classifying DNA sequences with similar biological characteristics and in discovering the relationship among the sequences.