Modeling sequencing errors by combining Hidden Markov models

C. Lottaz, C. Iseli, C. V. Jongeneel, P. Bucher

Research output: Contribution to journalArticlepeer-review

Abstract

Among the largest resources for biological sequence data is the large amount of expressed sequence tags (ESTs) available in public and proprietary databases. ESTs provide information on transcripts but for technical reasons they often contain sequencing errors. Therefore, when analyzing EST sequences computationally, such errors must be taken into account. Earlier attempts to model error prone coding regions have shown good performance in detecting and predicting these while correcting sequencing errors using codon usage frequencies. In the research presented here, we improve the detection of translation start and stop sites by integrating a more complex mRNA model with codon usage bias based error correction into one hidden Markov model (HMM), thus generalizing this error correction approach to more complex HMMs. We show that our method maintains the performance in detecting coding sequences.

Original languageEnglish (US)
Pages (from-to)ii103-ii112
JournalBioinformatics
Volume19
Issue numberSUPPL. 2
DOIs
StatePublished - 2003

Keywords

  • Coding region prediction
  • Expressed sequence tags
  • Hidden Markov models
  • Sequencing errors

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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