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Automated protein-family classification based on hidden Markov models
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Bioinformatics.
2015 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The aim of the project presented in this paper was to investigate the possibility toautomatically sub-classify the superfamily of Short-chain Dehydrogenase/Reductases (SDR).This was done based on an algorithm previously designed to sub-classify the superfamily ofMedium-chain Dehydrogenase/Reductases (MDR). While the SDR-family is interesting andimportant to sub-classify there was also a focus on making the process as automatic aspossible so that future families also can be classified using the same methods.To validate the results generated it was compared to previous sub-classifications done on theSDR-family. The results proved promising and the work conducted here can be seen as a goodinitial part of a more comprehensive full investigation

Place, publisher, year, edition, pages
2015. , 23 p.
UPTEC X, 14 033
Keyword [en]
Hidden Markov model, sequence identity, cluster, automatic clustering
National Category
Bioinformatics and Systems Biology
URN: urn:nbn:se:uu:diva-252372OAI: diva2:810083
Educational program
Molecular Biotechnology Engineering Programme
2014-05-15, 12:15 (English)
Available from: 2015-05-06 Created: 2015-05-06 Last updated: 2016-05-05Bibliographically approved

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Frisk, Christoffer
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