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Combinations of histone modifications mark exon inclusion levels
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology. Uppsala University, Science for Life Laboratory, SciLifeLab.
Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology.
Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medical Genetics.
Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology.
2012 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 7, no 1, e29911Article in journal (Refereed) Published
Abstract [en]

Splicing is a complex process regulated by sequence at the classical splice sites and other motifs in exons and introns with an enhancing or silencing effect. In addition, specific histone modifications on nucleosomes positioned over the exons have been shown to correlate both positively and negatively with exon expression. Here, we trained a model of "IF … THEN …" rules to predict exon inclusion levels in a transcript from histone modification patterns. Furthermore, we showed that combinations of histone modifications, in particular those residing on nucleosomes preceding or succeeding the exon, are better predictors of exon inclusion levels than single modifications. The resulting model was evaluated with cross validation and had an average accuracy of 72% for 27% of the exons, which demonstrates that epigenetic signals substantially mark alternative splicing.

Place, publisher, year, edition, pages
2012. Vol. 7, no 1, e29911
National Category
Cell and Molecular Biology
Identifiers
URN: urn:nbn:se:uu:diva-175875DOI: 10.1371/journal.pone.0029911ISI: 000312662100045PubMedID: 22242188OAI: oai:DiVA.org:uu-175875DiVA: diva2:533278
Funder
Knut and Alice Wallenberg FoundationSwedish Foundation for Strategic Research Swedish Research CouncilSwedish Cancer Society
Available from: 2012-06-13 Created: 2012-06-13 Last updated: 2017-12-07Bibliographically approved
In thesis
1. Rule-based Models of Transcriptional Regulation and Complex Diseases: Applications and Development
Open this publication in new window or tab >>Rule-based Models of Transcriptional Regulation and Complex Diseases: Applications and Development
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

As we gain increased understanding of genetic disorders and gene regulation more focus has turned towards complex interactions. Combinations of genes or gene and environmental factors have been suggested to explain the missing heritability behind complex diseases. Furthermore, gene activation and splicing seem to be governed by a complex machinery of histone modification (HM), transcription factor (TF), and DNA sequence signals. This thesis aimed to apply and develop multivariate machine learning methods for use on such biological problems. Monte Carlo feature selection was combined with rule-based classification to identify interactions between HMs and to study the interplay of factors with importance for asthma and allergy.

Firstly, publicly available ChIP-seq data (Paper I) for 38 HMs was studied. We trained a classifier for predicting exon inclusion levels based on the HMs signals. We identified HMs important for splicing and illustrated that splicing could be predicted from the HM patterns. Next, we applied a similar methodology on data from two large birth cohorts describing asthma and allergy in children (Paper II). We identified genetic and environmental factors with importance for allergic diseases which confirmed earlier results and found candidate gene-gene and gene-environment interactions.

In order to interpret and present the classifiers we developed Ciruvis, a web-based tool for network visualization of classification rules (Paper III). We applied Ciruvis on classifiers trained on both simulated and real data and compared our tool to another methodology for interaction detection using classification. Finally, we continued the earlier study on epigenetics by analyzing HM and TF signals in genes with or without evidence of bidirectional transcription (Paper IV). We identified several HMs and TFs with different signals between unidirectional and bidirectional genes. Among these, the CTCF TF was shown to have a well-positioned peak 60-80 bp upstream of the transcription start site in unidirectional genes.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2014. 69 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1167
Keyword
Histone modification, Transcription factor, Transcriptional regulation, Next-generation sequencing, Feature selection, Machine learning, Rule-based classification, Asthma, Allergy
National Category
Bioinformatics and Systems Biology Bioinformatics (Computational Biology)
Research subject
Bioinformatics
Identifiers
urn:nbn:se:uu:diva-230159 (URN)978-91-554-9005-8 (ISBN)
Public defence
2014-10-03, BMC C8:301, Husargatan 3, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2014-09-12 Created: 2014-08-19 Last updated: 2015-01-22

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Enroth, StefanBornelöv, SusanneWadelius, ClaesKomorowski, Jan
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