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FourCSeq: analysis of 4C sequencing data

Motivation: Circularized Chromosome Conformation Capture (4C) is a powerful technique for studying the spatial interactions of a specific genomic region called the ‘viewpoint’ with the rest of the genome, both in a single condition or comparing different experimental conditions or cell types. Observ...

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Autores principales: Klein, Felix A., Pakozdi, Tibor, Anders, Simon, Ghavi-Helm, Yad, Furlong, Eileen E. M., Huber, Wolfgang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4576695/
https://www.ncbi.nlm.nih.gov/pubmed/26034064
http://dx.doi.org/10.1093/bioinformatics/btv335
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author Klein, Felix A.
Pakozdi, Tibor
Anders, Simon
Ghavi-Helm, Yad
Furlong, Eileen E. M.
Huber, Wolfgang
author_facet Klein, Felix A.
Pakozdi, Tibor
Anders, Simon
Ghavi-Helm, Yad
Furlong, Eileen E. M.
Huber, Wolfgang
author_sort Klein, Felix A.
collection PubMed
description Motivation: Circularized Chromosome Conformation Capture (4C) is a powerful technique for studying the spatial interactions of a specific genomic region called the ‘viewpoint’ with the rest of the genome, both in a single condition or comparing different experimental conditions or cell types. Observed ligation frequencies typically show a strong, regular dependence on genomic distance from the viewpoint, on top of which specific interaction peaks are superimposed. Here, we address the computational task to find these specific peaks and to detect changes between different biological conditions. Results: We model the overall trend of decreasing interaction frequency with genomic distance by fitting a smooth monotonically decreasing function to suitably transformed count data. Based on the fit, z-scores are calculated from the residuals, and high z-scores are interpreted as peaks providing evidence for specific interactions. To compare different conditions, we normalize fragment counts between samples, and call for differential contact frequencies using the statistical method DESeq2 adapted from RNA-Seq analysis. Availability and implementation: A full end-to-end analysis pipeline is implemented in the R package FourCSeq available at www.bioconductor.org. Contact: felix.klein@embl.de or whuber@embl.de Supplementary information: Supplementary data are available at Bioinformatics online.
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spelling pubmed-45766952015-09-25 FourCSeq: analysis of 4C sequencing data Klein, Felix A. Pakozdi, Tibor Anders, Simon Ghavi-Helm, Yad Furlong, Eileen E. M. Huber, Wolfgang Bioinformatics Original Papers Motivation: Circularized Chromosome Conformation Capture (4C) is a powerful technique for studying the spatial interactions of a specific genomic region called the ‘viewpoint’ with the rest of the genome, both in a single condition or comparing different experimental conditions or cell types. Observed ligation frequencies typically show a strong, regular dependence on genomic distance from the viewpoint, on top of which specific interaction peaks are superimposed. Here, we address the computational task to find these specific peaks and to detect changes between different biological conditions. Results: We model the overall trend of decreasing interaction frequency with genomic distance by fitting a smooth monotonically decreasing function to suitably transformed count data. Based on the fit, z-scores are calculated from the residuals, and high z-scores are interpreted as peaks providing evidence for specific interactions. To compare different conditions, we normalize fragment counts between samples, and call for differential contact frequencies using the statistical method DESeq2 adapted from RNA-Seq analysis. Availability and implementation: A full end-to-end analysis pipeline is implemented in the R package FourCSeq available at www.bioconductor.org. Contact: felix.klein@embl.de or whuber@embl.de Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2015-10-01 2015-06-01 /pmc/articles/PMC4576695/ /pubmed/26034064 http://dx.doi.org/10.1093/bioinformatics/btv335 Text en © The Author 2015. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Papers
Klein, Felix A.
Pakozdi, Tibor
Anders, Simon
Ghavi-Helm, Yad
Furlong, Eileen E. M.
Huber, Wolfgang
FourCSeq: analysis of 4C sequencing data
title FourCSeq: analysis of 4C sequencing data
title_full FourCSeq: analysis of 4C sequencing data
title_fullStr FourCSeq: analysis of 4C sequencing data
title_full_unstemmed FourCSeq: analysis of 4C sequencing data
title_short FourCSeq: analysis of 4C sequencing data
title_sort fourcseq: analysis of 4c sequencing data
topic Original Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4576695/
https://www.ncbi.nlm.nih.gov/pubmed/26034064
http://dx.doi.org/10.1093/bioinformatics/btv335
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