Title: | Gaussian Process Modelling of Genome-wide High-throughput Sequencing Time Series |
Author(s): | Topa, Hande |
Date: | 2018 |
Language: | en |
Pages: | 113 + app. 79 |
Department: | Tietotekniikan laitos Department of Computer Science |
ISBN: | 978-952-60-8338-4 (electronic) 978-952-60-8337-7 (printed) |
Series: | Aalto University publication series DOCTORAL DISSERTATIONS, 245/2018 |
ISSN: | 1799-4942 (electronic) 1799-4934 (printed) 1799-4934 (ISSN-L) |
Supervising professor(s): | Kaski, Samuel, Prof., Aalto University, Department of Computer Science, Finland |
Thesis advisor(s): | Honkela, Antti, Asst. Prof., University of Helsinki, Finland |
Subject: | Computer science |
Keywords: | gaussian process, high-throughput sequencing, time series, probabilistic modelling |
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Abstract:During the last decade, high-throughput sequencing (HTS) has become the mainstream technique for simultaneously studying enormous number of genetic features present in the genome, transcriptome, or epigenome of an organism. Besides the static experiments which compare genetic features between two or more distinct biological conditions, time series experiments which monitor genetic features over time provide valuable information about the dynamics of complex mechanisms in various biological processes. However, analysis of the currently available HTS time series data sets involves challenges as these data sets often consist of short and irregularly sampled time series which lack sufficient biological replication. In addition, quantification of the genetic features from HTS data is inherently subject to uncertainty due to the limitations of HTS platforms such as short read lengths and varying sequencing depths.
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Parts:[Publication 1]: Hande Topa, Antti Honkela. Gaussian process modelling of multiple short time series. In ESANN 2015 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and MachineLearning, Bruges (Belgium), i6doc.com publ. pp. 83-88, April 2015.[Publication 2]: Hande Topa, Ágnes Jónás, Robert Kofler, Carolin Kosiol, Antti Honkela. Gaussian process test for high-throughput sequencing time series: application to experimental evolution. Bioinformatics, 31(11):1762-1770, 2015. DOI: 10.1093/bioinformatics/btv014 View at Publisher [Publication 3]: Hande Topa, Antti Honkela. Analysis of differential splicing suggests different modes of short-term splicing regulation. Bioinformatics, 32(12):i147-i155, 2016. Full Text in Aalto/Acris: http://urn.fi/URN:NBN:fi:aalto-201703283214. DOI: 10.1093/bioinformatics/btw283 View at Publisher [Publication 4]: Hande Topa, Antti Honkela. GPrank: an R package for detecting dynamic elements from genome-wide time series. BMC Bioinformatics, 19:367, 2018. Full Text in Aaltodoc/Acris: http://urn.fi/URN:NBN:fi:aalto-201810245514. DOI: 10.1186/s12859-018-2370-4 View at Publisher |
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