Software Performance Optimization for Analysis of Genetic Diversity Using Parallel Programming
Abstract
The use of molecular markers together with the main methodologies of genetic diversity allow us to infer how close organisms are on an evolutionary scale. In the present work, performance optimizations were performed in the calculation of genetic distance measurements in a R package, widely used by the scientific community with different data formats. With a mean reduction of 90 % in the time spent for these calculations using different measures of diversity, the results showed great optimization potential in methods of an area that is constantly evolving and has produced a massive amount of biological data.
Keywords:
Parallel and Distributed Algorithms, High performance applications in Agriculture, Biology, Engineering, Phisics, Mathematics, Computer Science, Medicine, Financial Markets, Chemistry, Nanosciences and others, High Performance Computing
References
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Kamvar, Z. N., Tabima, J. F., and Grünwald, N. J. (2014). Poppr: an r package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ, 2:e281.
Mohammadi, S. and Prasanna, B. (2003). Analysis of genetic diversity in crop plants—salient statistical tools and considerations. Crop science, 43(4):1235–1248.
Nadeem, M. A., Nawaz, M. A., Shahid, M. Q., Dogan, Y., Comertpay, G., Yıldız, M., Hatipoglu, R., Ahmad, F., Alsaleh, A., Labhane, N., et al. (2018). Dna molecular markers in plant breeding: current status and recent advancements in genomic selection and genome editing. Biotechnology & Biotechnological Equipment, 32(2):261–285.
Ramalho, M., dos Santos, J. B., and Pinto, C. B. (1990). Genética na agropecuária. FAEPE.
Schlötterer, C. (2004). The evolution of molecular markers—just a matter of fashion? Nature reviews genetics, 5(1):63.
Sunnucks, P. (2000). Efficient genetic markers for population biology. Trends in ecology & evolution, 15(5):199–203.
Published
2019-04-12
How to Cite
AONO, Alexandre; FAZENDA, Álvaro.
Software Performance Optimization for Analysis of Genetic Diversity Using Parallel Programming. In: REGIONAL SCHOOL OF HIGH PERFORMANCE COMPUTING FROM SÃO PAULO (ERAD-SP), 10. , 2019, Campinas.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 5-8.
DOI: https://doi.org/10.5753/eradsp.2019.13584.
