Predicting coding region candidates in the DNA sequence based on visualization without training

Bo Chen, Ping Ji

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Identifying the protein coding regions in the DNA sequence is an active issue in computational biology. Presently, there are many outstanding methods in predicting the coding regions with extreme high accuracy, after conducting preceding training process. However, the training dependence may reduce adaptability of the methods, particularly for new sequences from unknown organisms with no or small training sets. In this paper, we firstly present a Self Adaptive Spectral Rotation (SASR) approach, which was first introduced in a previous work published in Nucleic Acids Research. This approach is adopted to visualize the Triplet Periodicity (TP) property, which is a simple and universal coding related property. After that, we use a segmentation technique to computationally analyze the visualization and provide a numerical prediction of the coding region candidates in the DNA sequence. This approach does not require any training process, so it can work before any extra information is available, especially is helpful when dealing with new sequences from unknown organisms. Hence, it could be an efficient tool for coding region prediction in the early stage study.
Original languageEnglish
Title of host publicationIEEE SSCI 2011 - Symposium Series on Computational Intelligence - CIBCB 2011
Subtitle of host publication2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology
Pages1-6
Number of pages6
DOIs
Publication statusPublished - 28 Sep 2011
Event2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2011 - Paris, France
Duration: 11 Apr 201115 Apr 2011

Conference

Conference2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2011
Country/TerritoryFrance
CityParis
Period11/04/1115/04/11

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Biomedical Engineering
  • Health Informatics

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