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Mol Biosyst. 2014 Jun;10(6):1576-85. doi: 10.1039/c4mb00142g. Epub 2014 Apr 7.

Combining a wavelet change point and the Bayes factor for analysing chromosomal interaction data.

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1
Computer Laboratory, University of Cambridge, Cambridge, CB3 0FD, UK. ys388@cam.ac.uk pl219@cam.ac.uk.

Abstract

Over the past few decades we have witnessed great efforts to understand the cellular function at the cytoplasm level. Nowadays there is a growing interest in understanding the relationship between function and structure at the nuclear, chromosomal and sub-chromosomal levels. Data on chromosomal interactions that are now becoming available in unprecedented resolution and scale open the way to address this challenge. Consequently, there is a growing need for new methods and tools that will transform these data into knowledge and insights. Here, we have developed all the steps required for the analysis of chromosomal interaction data (Hi-C data). The result is a methodology which combines a wavelet change point with the Bayes factor for useful correction, segmentation and comparison of Hi-C data. We further developed chromoR, an R package that implements the methods presented here. The chromoR package provides researchers with a means to analyse chromosomal interaction data using statistical bioinformatics, offering a new and comprehensive solution to this task.

PMID:
24710657
DOI:
10.1039/c4mb00142g
[Indexed for MEDLINE]
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