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Masakari: visualization supported statistical analysis of genome segmentations

BACKGROUND: In epigenetics, the change of the combination of histone modifications at the same genomic location during cell differentiation is of great interest for understanding the function of these modifications and their combinations. Besides analyzing them locally for individual genomic locatio...

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Autores principales: Zeckzer, Dirk, Hausdorf, Alrik, Hinzmann, Nicole, Müller, Lydia, Wiegreffe, Daniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7542120/
https://www.ncbi.nlm.nih.gov/pubmed/33028199
http://dx.doi.org/10.1186/s12859-020-03761-6
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author Zeckzer, Dirk
Hausdorf, Alrik
Hinzmann, Nicole
Müller, Lydia
Wiegreffe, Daniel
author_facet Zeckzer, Dirk
Hausdorf, Alrik
Hinzmann, Nicole
Müller, Lydia
Wiegreffe, Daniel
author_sort Zeckzer, Dirk
collection PubMed
description BACKGROUND: In epigenetics, the change of the combination of histone modifications at the same genomic location during cell differentiation is of great interest for understanding the function of these modifications and their combinations. Besides analyzing them locally for individual genomic locations or globally using correlations between different cells types, intermediate level analyses of these changes are of interest. More specifically, the different distributions of these combinations for different cell types, respectively, are compared to gain new insights. RESULTS AND DISCUSSION: We propose a new tool called ‘Masakari’ that allows segmenting genomes based on lists of ranges having a certain property, e.g., peaks describing histone modifications. It provides a graphical user interface allowing to select all data sets and setting all parameters needed for the segmentation process. Moreover, the graphical user interface provides statistical graphics allowing to assess the quality and suitability of the segmentation and the selected data. CONCLUSION: Masakari provides statistics based visualizations and thus fosters insights into the combination of histone modification marks on genome ranges, and the differences of the distribution of these combinations between different cell types.
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spelling pubmed-75421202020-10-08 Masakari: visualization supported statistical analysis of genome segmentations Zeckzer, Dirk Hausdorf, Alrik Hinzmann, Nicole Müller, Lydia Wiegreffe, Daniel BMC Bioinformatics Software BACKGROUND: In epigenetics, the change of the combination of histone modifications at the same genomic location during cell differentiation is of great interest for understanding the function of these modifications and their combinations. Besides analyzing them locally for individual genomic locations or globally using correlations between different cells types, intermediate level analyses of these changes are of interest. More specifically, the different distributions of these combinations for different cell types, respectively, are compared to gain new insights. RESULTS AND DISCUSSION: We propose a new tool called ‘Masakari’ that allows segmenting genomes based on lists of ranges having a certain property, e.g., peaks describing histone modifications. It provides a graphical user interface allowing to select all data sets and setting all parameters needed for the segmentation process. Moreover, the graphical user interface provides statistical graphics allowing to assess the quality and suitability of the segmentation and the selected data. CONCLUSION: Masakari provides statistics based visualizations and thus fosters insights into the combination of histone modification marks on genome ranges, and the differences of the distribution of these combinations between different cell types. BioMed Central 2020-10-07 /pmc/articles/PMC7542120/ /pubmed/33028199 http://dx.doi.org/10.1186/s12859-020-03761-6 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Software
Zeckzer, Dirk
Hausdorf, Alrik
Hinzmann, Nicole
Müller, Lydia
Wiegreffe, Daniel
Masakari: visualization supported statistical analysis of genome segmentations
title Masakari: visualization supported statistical analysis of genome segmentations
title_full Masakari: visualization supported statistical analysis of genome segmentations
title_fullStr Masakari: visualization supported statistical analysis of genome segmentations
title_full_unstemmed Masakari: visualization supported statistical analysis of genome segmentations
title_short Masakari: visualization supported statistical analysis of genome segmentations
title_sort masakari: visualization supported statistical analysis of genome segmentations
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7542120/
https://www.ncbi.nlm.nih.gov/pubmed/33028199
http://dx.doi.org/10.1186/s12859-020-03761-6
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