Background: DNA sequence analysis is an important research topic in bioinformatics. Evaluating the similarity between sequences, which is crucial for sequence analysis, has attracted much research effort in the last two decades, and a dozen of algorithms and tools have been developed. These methods are based on alignment, word frequency and geometric representation respectively, each of which has its advantage and disadvantage. Results: In this paper, for effectively computing the similarity between DNA sequences, we introduce a novel method based on frequency patterns and entropy to construct representative vectors of DNA sequences. Experiments are conducted to evaluate the proposed method, which is compared with two recently-developed alignment-free methods and the BLASTN tool. When testing on the b-globin genes of 11 species and using the results from MEGA as the baseline, our method achieves higher correlation coefficients than the two alignmentfree methods and the BLASTN tool. Conclusions: Our method is not only able to capture fine-granularity information (location and ordering) of DNA sequences via sequence blocking, but also insensitive to noise and sequence rearrangement due to considering only the maximal frequent patterns. It outperforms major existing methods or tools.
To compute the similarity between DNA sequences, REF introduced a novel method based on frequency patterns and entropy to construct representative vectors of DNA sequences Frequent sequence discovery is a sub-problem in pattern recognition, there are many researches focusing on the frequent sequences discovery in DNA sequence.
Similarity evaluation of DNA sequences based on frequent patterns and entropy
BMC Genomics
BMC Genomics
Medicine Biology