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Comparative Study
. 2012 Jul 23:13:174.
doi: 10.1186/1471-2105-13-174.

A novel hierarchical clustering algorithm for gene sequences

Dan Wei  1 , Qingshan Jiang, Yanjie Wei, Shengrui Wang
Affiliations
Comparative Study

A novel hierarchical clustering algorithm for gene sequences

Dan Wei et al. BMC Bioinformatics. .

Abstract

Background: 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.

Results: 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.

Conclusions: 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.

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Figures

Figure 1
Figure 1
The phylogenetic trees for 10 species using the full DNA sequences of β-globin.
Figure 2
Figure 2
The distribution of F-measure as a function of the number of clusters based on the k-tuple distance (The real numbers of DS1, DS2, DS3 and DS4 are 8, 6, 6, and 6, respectively).
Figure 3
Figure 3
The distribution of F-measure as a function of the number of clusters based on DMk (The real numbers of DS1, DS2, DS3 and DS4 are 8, 6, 6, and 6, respectively).
Figure 4
Figure 4
The phylogenetic trees for 10 species using the full DNA sequences of β-globin.
Figure 5
Figure 5
The phylogenetic trees for 60 H1N1 viruses.
Figure 6
Figure 6
The time comparison of three methods.
Figure 7
Figure 7
The relationship between the runtime and different numbers of sequences and length of sequences.
See this image and copyright information in PMC

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