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Statistical Analysis of PAR-CLIP data
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
2013 (English)Independent thesis Advanced level (degree of Master (Two Years)), 60 credits / 90 HE creditsStudent thesis
Abstract [en]

From creation to its degradation, the RNA molecule is the action field of many binding proteins with different roles in regulation and RNA metabolism. Since these proteins are involved in a large number of processes, a variety of diseases are related to abnormalities occurring within the binding mechanisms. One of the experimental methods for detecting the binding sites of these proteins is PAR-CLIP built on the next generation sequencing technology. Due to its size and intrinsic noise, PAR-CLIP data analysis requires appropriate pre-processing and thorough statistical analysis.

The present work has two main goals. First, to develop a modular pipeline for preprocessing PAR-CLIP data and extracting necessary signals for further analysis. Second, to devise a novel statistical model in order to carry out inference about presence of protein binding sites based on the signals extracted in the pre-processing step.

Place, publisher, year, edition, pages
2013. , 50 p.
Keyword [en]
statistical modeling, PAR-CLIP, RNA-binding proteins, Bayesian analysis
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:kth:diva-124347OAI: oai:DiVA.org:kth-124347DiVA: diva2:634135
Educational program
Master of Science - Systems Biology
Supervisors
Examiners
Available from: 2013-12-13 Created: 2013-06-28 Last updated: 2013-12-13Bibliographically approved

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Computational Biology, CB
Bioinformatics (Computational Biology)

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CiteExportLink to record
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  • apa
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