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Designing autocorrelated genes

  • Rukhsana Yeasmin
  • , Jesmin Jahan Tithi
  • , Jeffrey Chen
  • , Steven Skiena
  • Stony Brook University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

The redundancy in the genetic code enables a protein to be encoded by many different sequences. Recent studies show that the degree of tRNA autocorrelation in a coding sequence has important effects on translation speed. The tRNA pairing index (TPI) has been used widely to study the phenomenon of autocorrelation in sequences. However TPI only counts successive transitions of tRNA usage, with- out regard to how far apart they occur in the sequence. In this paper, we propose a new type of autocorrelation measure, DICA (Distance Incorporated Codon Autocorre- lation), which weighs positional distance between codons as well as the number of transitions. We demonstrate that our DICA correlates better to the expression level of a particular gene than TPI. Finally, we devise exact and heuristic algorithms to find near optimally autocorrelated and anti- Autocorrelated genes for the purposes of synthetic gene design.

Original languageEnglish
Title of host publication2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013
Pages458-467
Number of pages10
DOIs
StatePublished - 2013
Event2013 4th ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013 - Wshington, DC, United States
Duration: Sep 22 2013Sep 25 2013

Publication series

Name2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013

Conference

Conference2013 4th ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013
Country/TerritoryUnited States
CityWshington, DC
Period09/22/1309/25/13

Keywords

  • Codon autocorrelation
  • Gene design
  • Sequence analysis

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