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A Segmental Maximum A Posteriori Approach to Genome-wide Copy Number Profiling
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
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2008 (English)In: Bioinformatics, ISSN 1367-4803, Vol. 24, no 6, 751-758 p.Article in journal (Other academic) Published
Abstract [en]

MOTIVATION: Copy number profiling methods aim at assigning DNA copy numbers to chromosomal regions using measurements from microarray-based comparative genomic hybridizations. Among the proposed methods to this end, Hidden Markov Model (HMM)-based approaches seem promising since DNA copy number transitions are naturally captured in the model. Current discrete-index HMM-based approaches do not, however, take into account heterogeneous information regarding the genomic overlap between clones. Moreover, the majority of existing methods are restricted to chromosome-wise analysis. RESULTS: We introduce a novel Segmental Maximum A Posteriori approach, SMAP, for DNA copy number profiling. Our method is based on discrete-index Hidden Markov Modeling and incorporates genomic distance and overlap between clones. We exploit a priori information through user-controllable parameterization that enables the identification of copy number deviations of various lengths and amplitudes. The model parameters may be inferred at a genome-wide scale to avoid overfitting of model parameters often resulting from chromosome-wise model inference. We report superior performances of SMAP on synthetic data when compared with two recent methods. When applied on our new experimental data, SMAP readily recognizes already known genetic aberrations including both large-scale regions with aberrant DNA copy number and changes affecting only single features on the array. We highlight the differences between the prediction of SMAP and the compared methods and show that SMAP accurately determines copy number changes and benefits from overlap consideration.

Place, publisher, year, edition, pages
2008. Vol. 24, no 6, 751-758 p.
National Category
Medical and Health Sciences
URN: urn:nbn:se:uu:diva-13616DOI: 10.1093/bioinformatics/btn003ISI: 000254010400003PubMedID: 18204059OAI: oai:DiVA.org:uu-13616DiVA: diva2:41386
Available from: 2008-08-21 Created: 2008-08-21 Last updated: 2010-11-11Bibliographically approved
In thesis
1. Decoding the Structural Layer of Transcriptional Regulation: Computational Analyses of Chromatin and Chromosomal Aberrations
Open this publication in new window or tab >>Decoding the Structural Layer of Transcriptional Regulation: Computational Analyses of Chromatin and Chromosomal Aberrations
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Gene activity is regulated at two separate layers. Through structural and chemical properties of DNA – the primary layer of encoding – local signatures may enable, or disable, the binding of proteins or complexes of them with regulatory potential to the DNA. At a higher level – the structural layer of encoding – gene activity is regulated through the properties of higher order DNA structure, chromatin, and chromosome organization. Cells with abnormal chromosome compaction or organization, e.g. cancer cells, may thus have perturbed regulatory activities resulting in abnormal gene activity.

Hence, there is a great need to decode the transcriptional regulation encoded in both layers to further our understanding of the factors that control activity and life of a cell and, ultimately, an organism. Modern genome-wide studies with those aims rely on data-intense experiments requiring sophisticated computational and statistical methods for data handling and analyses. This thesis describes recent advances of analyzing experimental data from quantitative biological studies to decipher the structural layer of encoding in human cells.

Adopting an integrative approach when possible, combining multiple sources of data, allowed us to study the influences of chromatin (Papers I and II) and chromosomal aberrations (Paper IV) on transcription. Combining chromatin data with chromosomal aberration data allowed us to identify putative driver oncogenes and tumor-suppressor genes in cancer (Paper IV).

Bayesian approaches enabling the incorporation of background information in the models and the adaptability of such models to data have been very useful. Their usages yielded accurate and narrow detection of chromosomal breakpoints in cancer (Papers III and IV) and reliable positioning of nucleosomes and their dynamics during transcriptional regulation at functionally relevant regulatory elements (Paper II).

Using massively parallel sequencing data, we explored the chromatin landscapes of human cells (Papers I and II) and concluded that there is a preferential and evolutionary conserved positioning at internal exons nearly unaffected by the transcriptional level. We also observed a strong association between certain histone modifications and the inclusion or exclusion of an exon in the mature gene transcript, suggesting a functional role in splicing.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2010. 76 p.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 768
Chromatin, Nucleosome positioning, Histone modifications, Chromosomal aberrations, Transcriptional regulation, Array-CGH, Next generation sequencing, ChIP-chip
National Category
Bioinformatics and Systems Biology
Research subject
urn:nbn:se:uu:diva-130999 (URN)978-91-554-7897-1 (ISBN)
Public defence
2010-11-02, C4:305, BMC, Husargatan 3, Uppsala, 09:00 (English)
Available from: 2010-10-12 Created: 2010-09-20 Last updated: 2010-11-11Bibliographically approved

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Andersson, RobinMenzel, UweNord, HelenaSandgren, Johannade Ståhl, Teresita DiazKomorowski, Jan
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