A novel hierarchical clustering algorithm for gene sequences
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Updated: May 20, 2026

Novel Sequence Discovery by Subtractive Genomics
Published on: January 25, 2019
Dan Wei1, Qingshan Jiang, Yanjie Wei
1Cognitive Science Department & Fujian Key Laboratory of the Brain-like Intelligent Systems, Xiamen University, Xiamen, China.
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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