Visualization and Classification of DNA Sequence by Using Pareto Learning Self Organizing Maps for Short Sequences

Next-generation sequencing techniques produce an enormous amount of sequence data. Analyzing these sequences requires an efficient method that can handle large amounts of data. Self-organizing maps (SOMs), which use the frequencies of N-tuples and correlation coefficients of nucleotide, can categorize sets of DNA sequences with unsupervised learning. And Pareto learning SOM can classify the DNA sequences with supervised learning. In this study, the effect of the short fragments, which will be given directly as the sequencing results of NGS are examined using Pareto learning SOM, and the novel method which uses the correlation coefficients of tuples and integration of correlation coefficient and frequencies are proposed. keywords: Sequence Analysis, meta-genome,