Abstract
Background: The multiple sequence alignment (MSA) algorithms are the traditional ways to compare and analyze DNA sequences. However, for large DNA sequences, these algorithms require a long time computationally. Objective: Here we will propose a new numerical method to characterize and compare DNA sequences quickly. Method: Based on a new 2-dimensional (2D) graphical representation of DNA sequences, we can obtain an 8-dimensional vector using two basic concepts of probability, the mean and the variance. Results: We perform similarity/dissimilarity analyses among two real DNA data sets, the coding sequences of the first exon of beta-globin gene of 11 species and 31 mammalian mitochondrial genomes, respectively. Conclusion: Our results are in agreement with the existing analyses in our literatures. We also compare our approach with other methods and find that ours is more effective.
Highlights
With the rapid growth in biological data, how to get more information from these big data is a challenge for scientists
We have found the novel DNA map from the space of DNA sequences to the 8-dimensional Euclidean space
Our method provides a map from the space of DNA sequences to the 8-dimensional Euclidean space
Summary
With the rapid growth in biological data, how to get more information from these big data is a challenge for scientists. For this purpose, an important problem is to find a suitable way to digitize these DNA sequences so that the sequence comparison can be applied. Beyond the traditional multiple sequence alignment (MSA), many alignment-free sequence comparison methods were introduced, for more details, please refer to [1] [2] [3] and the references therein. The multiple sequence alignment (MSA) algorithms are the traditional ways to compare and analyze DNA sequences. Objective: Here we will propose a new numerical method to characterize and compare DNA sequences quickly. We compare our approach with other methods and find that ours is more effective
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