Masakari: visualization supported statistical analysis of genome segmentations

BMC Bioinformatics

Daniel Wiegreffe¹  Alrik Hausdorf²  Dirk Zeckzer²  Nicole Hinzmann²  Lydia Müller³ 

[1] Bioinformatics Group, Department of Computer Science, University of Leipzig, Härtelstraße 16-18, 04107, Leipzig, Germany;Image and Signal Processing Group, Department of Computer Science, University of Leipzig, Augustusplatz 10, 04109, Leipzig, Germany;Image and Signal Processing Group, Department of Computer Science, University of Leipzig, Augustusplatz 10, 04109, Leipzig, Germany;Natural Language Processing Group, Department of Computer Science, University of Leipzig, Augustusplatz 10, 04109, Leipzig, Germany;Bioinformatics Group, Department of Computer Science, University of Leipzig, Härtelstraße 16-18, 04107, Leipzig, Germany;

关键词: ChIP-seq;    Chromatin state;    Histone modifications;    Cell development;   

DOI  :  10.1186/s12859-020-03761-6

来源: Springer

【 摘 要 】

BackgroundIn 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 discussionWe 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.ConclusionMasakari 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.

【 授权许可】

CC BY