Estimating the occurrence rate of DNA palindromes

I. Ping Tu, Shao Hsuan Wang, Yuan Fu Huang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

A DNA palindrome is a segment of letters along a DNA sequence with inversion symmetry that one strand is identical to its complementary one running in the opposite direction. Searching nonrandom clusters of DNA palindromes, an interesting bioinformatic problem, relies on the estimation of the null palindrome occurrence rate. The most commonly used approach for estimating this number is the average rate method. However, we observed that the average rate could exceed the actual rate by 50% when inserting 5000 bp hotspot regions with 15-fold rate in a simulated 150,000 bp genome sequence. Here, we propose a Markov based estimator to avoid counting the number of palindromes directly, and thus to reduce the impact from the hot-spots. Our simulation shows that this method is more robust against the hot-spot effect than the average rate method. Furthermore, this method can be generalized to either a higher order Markov model or a segmented Markov model, and extended to calculate the occurrence rate for palindromes with gaps. We also provide a p-value approximation for various scan statistics to test nonrandom palindrome clusters under a Markov model.

Original languageEnglish
Pages (from-to)1095-1110
Number of pages16
JournalAnnals of Applied Statistics
Volume7
Issue number2
DOIs
StatePublished - Jun 2013

Keywords

  • DNA palindrome
  • Genome sequence
  • Hairpin structure
  • Higher order Markov model
  • Hot-spot
  • Markov model
  • Occurrence rate
  • P-value
  • Poisson process
  • Power
  • Segmented Markov model

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