Abstract
One of the interesting problems in Bioinformatics is finding transcription start site in a gene. In fact, finding this site which separate promoter region from coding sequence, actually will end to promoter prediction. This leads to activate or inactivate some parts of gene which plays an important role after being translated to protein sequence. While traditional methods are reliable ways for promoter prediction, because of the large number of sequences and too much of information, it is not possible to study these sequences by those methods. Although some of these sequences have been already recognized and their information has been stored in big databases like NCBI, there are some sequences which their promoter regions have not been identified yet. This research aimed to design a parallel algorithm for one of the known promoter prediction algorithms, Ohler. We attempt to reduce the response time of Ohler algorithm, consequently increases the number of test samples, and improves the accuracy of the algorithm. The experimental results show that we have succeeded to achieve our purpose.
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- 1.
Programs have been tested on Intel core i7 processor in C language, and have been executed on windows 8.1 OS.
- 2.
Programs have been tested on Intel core i7 processor which has 4 physical cores and 8 threads; and the processor can support 8 processes in parallel at maximum. Programs designed and developed in C language, and we used Open MPI for parallelization. All programs have been executed on windows 8.1 OS.
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Acknowledgements
This research is done by companion of Agriculture Biotechnology Research Institute of Iran, Karaj. Thanks to all of the members of the Institute who helped during the project and especially thanks to Dr. Zahra Sadat Shobbar.
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Langroudi, S.M.S., Hamidi, H.R., Kermanshahani, S. (2019). A Parallel Algorithm for Eukaryotic Promoter Recognition. In: Grandinetti, L., Mirtaheri, S., Shahbazian, R. (eds) High-Performance Computing and Big Data Analysis. TopHPC 2019. Communications in Computer and Information Science, vol 891. Springer, Cham. https://doi.org/10.1007/978-3-030-33495-6_36
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DOI: https://doi.org/10.1007/978-3-030-33495-6_36
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