Abstract
We analyzed DNA sequences using a new measure of entropy. The general aim was to analyze DNA sequences and find interesting sections of a genome using a new formulation of Shannon like entropy. We developed this new measure of entropy for any non-trivial graph or, more broadly, for any square matrix whose non-zero elements represent probabilistic weights assigned to connections or transitions between pairs of vertices. The new measure is called the graph entropy and it quantifies the aggregate indeterminacy effected by the variety of unique walks that exist between each pair of vertices. The new tool is shown to be uniquely capable of revealing CRISPR regions in bacterial genomes and to identify Tandem repeats and Direct repeats of genome. We have done experiment on 26 species and found many tandem repeats and direct repeats (CRISPR for bacteria or archaea). There are several existing separate CRISPR or Tandem finder tools but our entropy can find both of these features if present in genome.
Highlights
Deciphering the enormously long nucleotide sequences that are being uncovered in the human genome is one of the major challenges in our days
The new measure is called the graph entropy and it quantifies the aggregate indeterminacy effected by the variety of unique walks that exist between each pair of vertices
The new tool is shown to be uniquely capable of revealing Clustered Regularly Inter Spaced Palindromic Repeats (CRISPRs) regions in bacterial genomes and to identify Tandem repeats and Direct repeats of genome
Summary
Deciphering the enormously long nucleotide sequences that are being uncovered in the human genome is one of the major challenges in our days. Along with serious ethical issues, we encounter a series of tremendously hard scientific problems These problems mainly arise from the fact that sequencing techniques are almost completely automatic controlled the analysis of the sequenced data is not. D. Sengupta 42 ciple, biochemical methods are able to do this job, but since they are extremely expensive and time consuming, there is a high demand for alternative approaches to extract the information hidden in genome [1]. Sengupta 42 ciple, biochemical methods are able to do this job, but since they are extremely expensive and time consuming, there is a high demand for alternative approaches to extract the information hidden in genome [1] In this situation, concepts and techniques from information theory turned out to be welcoming tools to handle the problem of extracting valuable information from biosequences such as DNA, RNA, or amino acid chains.
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