Applying Hidden Markov Models to RNA-seq data
2013 (English)Report (Other academic)
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.
Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2013. , 20 p.
RNA-sequencing, statistical modeling, hidden Markov models, inference, Baum-Welch algorithm, auto-regressive hidden Markov model, transcription start sites prediction
Bioinformatics (Computational Biology)
IdentifiersURN: urn:nbn:se:kth:diva-128384OAI: oai:DiVA.org:kth-128384DiVA: diva2:647517
Individual Project in Computational Biology. QC 201309112013-09-112013-09-112013-09-11Bibliographically approved