1. Proceedings
  2. I3M
  3. 2020
  4. EMSS
  5. 10.46354/i3m.2020.emss.001

Usefulness of HIGH-LEVEL parallel compositions in genomics

  • a Mario Rossainz-López , 
  • b Sarahí Zúñiga-Herrera , 
  • c Iván Olmos Pineda , 
  • d Ivo Pineda-Torres , 
  • e Manuel Capel-Tuñón 
  • a,b,c,d Faculty of Computer Science, Autonomous University of Puebla, San Claudio Avenue and South 14th Street, San Manuel, Puebla, Puebla, 72000, México
  • e Software Engineering Department, College of Informatics and Telecommunications ETSIIT, University of Granada, Daniel Saucedo Aranda s/n, Granada 18071, Spain
Cite as
Rossainz-López M., Zúñiga-Herrera S., Olmos Pineda I., Pineda-Torres I., Capel-Tuñón M. (2020). Usefulness of HIGH-LEVEL parallel compositions in genomics. Proceedings of the 32nd European Modeling & Simulation Symposium (EMSS 2020), pp. 1-9. DOI: https://doi.org/10.46354/i3m.2020.emss.001

Abstract

This work shows the use of parallel objects to build High Level Parallel Compositions or HLPC and their usefulness in genomics through four case studies related to sequencing DNA chains. The first two case studies are combinatorial optimization problems: grouping fragments of DNA sequences and the parallel exhaustive search (PES) of RNA strings that help the sequence and assembly of DNAs in the construction of gnomes. The third case study shows the implementation of a Convolutional Neuronal Network as a Parallel Object Composition to solve the problem of the recognition of DNA sequences from a database with 4 types of hepatitis C virus (type 1, 2, 3 and 6). The results of this classification were obtained in terms of percentages of training precision and validation precision. The fourth and final case study shows the problem of sequence typing (STP) as a form of DNA sequence classification. It is particularized in a proposal for a parallel solution to find conserved regions of sequences that help discriminate between different types of hepatitis C virus, through the creation of a decision tree using HLPC. We show the algorithms that solves these problems using modeling and parallel simulation, their design and implementation as HLPC and the performance metrics in their parallel execution using multicores, video accelerator card and CPU-SET or processors with shared-distributed memory.

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