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Applying Hidden Markov Models to RNA-seq data
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- Golumbeanu, Monica (author)
- KTH,Beräkningsbiologi, CB
- (creator_code:org_t)
- KTH Royal Institute of Technology, 2013
- English 20 s.
- Reports (other academic/artistic)
Abstract
Subject headings
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- Enterococcus faecalis is one of the most controversial commensal bacteria of the human intestinal flora that is also responsible for lethal nosocomial infections. Determining the factors that influence its pathogenicity is at present a great challenge. Cutting-edge approaches analyze the E. faecalis bacterium trough the next generation RNA-sequencing technology. Since next generation sequencing is recent and yields a large amount of data, there is a continuous need for appropriate statistical methods to interpret its output. We propose an approach based on hidden Markov models to explore RNA-seq data and show an example of how we can apply this statistical tool to detect transcription start sites. We compare this application with a previously developed method based on signal processing.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
Keyword
- RNA-sequencing
- statistical modeling
- hidden Markov models
- inference
- Baum-Welch algorithm
- auto-regressive hidden Markov model
- transcription start sites prediction
Publication and Content Type
- vet (subject category)
- rap (subject category)
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