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Abstract
Pattern recognition is an important field in Bioinformatics and a well-known task is the search for Single Nucleotide Polymorphism (SNP). It is possible to search for a known SNP position and analyze it using patterns of DNA bases, called masks. Nonetheless, this process becomes computationally expensive as the amount of available genomic data increases. Thus, in this study, we have developed a parallelization scheme, based on multithread programming, to SNP analysis using masks. In our tests, we noticed that the proposed scheme improved the execution time in 98.05 times when compared with the sequential approach.
Highlights
Due to the huge amount of genomic data available, performing manual biological analysis becomes unfeasible
The tests were run for the sequential Single Nucleotide Polymorphism (SNP) Verifier method and our parallel approach, in order to observe the obtained improvement
This method cannot be compared with the other methods presented in section SNP Concepts, since they perform detection of unknown SNP positions, while our method performs the analysis of known SNP positions, using the mask search approach
Summary
Due to the huge amount of genomic data available, performing manual biological analysis becomes unfeasible. It is necessary the development of computational methods to support biologists in their analysis and inferences. This fact has originated Bioinformatics (Amorim et al, 2016). Concerning the pattern recognition problems, the search problem is one of the most important (Wang et al, 2016) and a well-known one is the search and analysis for Single Nucleotide Polymorphism (SNP) (Trick et al, 2009). The detection of a SNP can be performed through the amplification of the target sequence and its identification using hybridization probe (Real-Time PCR) or through DNA sequencing using capillary electrophoresis (Sanger Sequencing) (Sanger and Coulson, 1975) or Generation
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