2014
DOI: 10.1039/c4mb00142g
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Combining a wavelet change point and the Bayes factor for analysing chromosomal interaction data

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 i… Show more

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Cited by 36 publications
(36 citation statements)
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References 30 publications
(45 reference statements)
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“…GOTHiC’s approach is conceptually and computationally simpler than existing methods, which require the identification and separate modeling of individual biases [8,9], an iterative correction of biases [10,11], or variance stabilisation [12]. It yields similar rankings to previous methods, with comparable or even slightly improved bias removal and reproducibility between replicates.…”
Section: Discussionmentioning
confidence: 99%
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“…GOTHiC’s approach is conceptually and computationally simpler than existing methods, which require the identification and separate modeling of individual biases [8,9], an iterative correction of biases [10,11], or variance stabilisation [12]. It yields similar rankings to previous methods, with comparable or even slightly improved bias removal and reproducibility between replicates.…”
Section: Discussionmentioning
confidence: 99%
“…ChromoR , also only uses the information encaptured in observed read counts. For normalisation of Hi-C data it uses Haar-Fisz Transformation to decompose the Poisson distributed read counts into Gaussian coefficients that are subsequently de-noised by wavelet shrinkage methods [12]. …”
Section: Introductionmentioning
confidence: 99%
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“…In practice, both explicit and implicit approaches have been used to account for biases in Hi-C data; therefore, it would be helpful to conduct a comprehensive comparison between the two approaches. To date, only a partial comparison has been made, which highlighted the differences in reproducibility of cis and trans interaction frequencies at low resolution 84 . A novel computational framework that combines the strengths of the two approaches may enable more accurate bias removal and higher computational efficiency.…”
Section: Computational Analysis Of C-datamentioning
confidence: 99%
“…Mostly during the past five years, several software applications have been developed to analyze and visualize genomic interaction data, with different characteristics and outputs. If we consider Hi-C, available analysis software applications are chromoR [6], HiCdat [7], HiCNorm [8], Hi-Corrector [9], Hi-C Pipeline [10], HiC-Pro [11], HiCUP [12], HiFive [13], HIPPIE [14], HiTC [15], HOMER [16], ICE [17]. However, besides correcting biases and generating contact maps, most of them do not provide the entire pipeline to pre-process the raw data (downloadable files) or to compute topological domain coordinates.…”
Section: Introductionmentioning
confidence: 99%