Identification of Coding Regions in Prokaryotic DNA Sequences Using Bayesian Classification

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The identification of protein-coding regions in genomic DNA sequences is a well-known problem in computational genomics. Various computational algorithms can be employed to achieve the identification process. The rapid advances in this field have motivated the development of innovative engineering methods that allow for further analysis and modeling of many processes in molecular biology. The proposed algorithm utilizes well-known concepts in communications theory, such as correlation, the maximal ratio combining (MRC) algorithm, and filtering techniques to create a signal whose maxima and minima indicate coding and noncoding regions, respectively. The proposed algorithm investigates several prokaryotic genome sequences. Two Bayesian classifiers are designed to test and evaluate the performance of the proposed algorithm. The obtained simulation results prove that the algorithm can efficiently and accurately detect protein-coding regions, which is being demonstrated by the obtained sensitivity and specificity values that are comparable to well-known gene detection methods in prokaryotes. The obtained results further verify the correctness and the biological relevance of using communications theory concepts for genomic sequence analysis.

Original languageEnglish
Title of host publicationBioinformatics and Biomedical Engineering - 8th International Work-Conference, IWBBIO 2020, Proceedings
EditorsIgnacio Rojas, Olga Valenzuela, Fernando Rojas, Luis Javier Herrera, Francisco Ortuño
PublisherSpringer
Pages3-14
Number of pages12
ISBN (Print)9783030453848
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event8th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2020 - Granada, Spain
Duration: May 6 2020May 8 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12108 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2020
Country/TerritorySpain
CityGranada
Period5/6/205/8/20

Keywords

  • Bayesian classification
  • Correlation
  • Gene identification
  • Maximal ratio combining
  • Period-3 filter

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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