Dataset Information

Masakari: visualization supported statistical analysis of genome segmentations.

ABSTRACT: 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.

SUBMITTER: Zeckzer D

PROVIDER: S-EPMC7542120 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Masakari: visualization supported statistical analysis of genome segmentations.

Zeckzer Dirk D Hausdorf Alrik A Hinzmann Nicole N Müller Lydia L Wiegreffe Daniel D

BMC bioinformatics 20201007 1

<h4>Background</h4>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 combinatio ...[more]

PMID: 33028199

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Project description:BackgroundFeedback loops in gene regulatory networks play pivotal roles in governing functional dynamics of cells. Systems approaches demonstrated characteristic dynamical features, including multistability and oscillation, of positive and negative feedback loops. Recent experiments and theories have implicated highly interconnected feedback loops (high-feedback loops) in additional nonintuitive functions, such as controlling cell differentiation rate and multistep cell lineage progression. However, it remains challenging to identify and visualize high-feedback loops in complex gene regulatory networks due to the myriad of ways in which the loops can be combined. Furthermore, it is unclear whether the high-feedback loop structures with these potential functions are widespread in biological systems. Finally, it remains challenging to understand diverse dynamical features, such as high-order multistability and oscillation, generated by individual networks containing high-feedback loops. To address these problems, we developed HiLoop, a toolkit that enables discovery, visualization, and analysis of several types of high-feedback loops in large biological networks.ResultsHiLoop not only extracts high-feedback structures and visualize them in intuitive ways, but also quantifies the enrichment of overrepresented structures. Through random parameterization of mathematical models derived from target networks, HiLoop presents characteristic features of the underlying systems, including complex multistability and oscillations, in a unifying framework. Using HiLoop, we were able to analyze realistic gene regulatory networks containing dozens to hundreds of genes, and to identify many small high-feedback systems. We found more than a 100 human transcription factors involved in high-feedback loops that were not studied previously. In addition, HiLoop enabled the discovery of an enrichment of high feedback in pathways related to epithelial-mesenchymal transition.ConclusionsHiLoop makes the study of complex networks accessible without significant computational demands. It can serve as a hypothesis generator through identification and modeling of high-feedback subnetworks, or as a quantification method for motif enrichment analysis. As an example of discovery, we found that multistep cell lineage progression may be driven by either specific instances of high-feedback loops with sparse appearances, or generally enriched topologies in gene regulatory networks. We expect HiLoop's usefulness to increase as experimental data of regulatory networks accumulate. Code is freely available for use or extension at https://github.com/BenNordick/HiLoop .

Dataset Information

Masakari: visualization supported statistical analysis of genome segmentations.

Publications

Masakari: visualization supported statistical analysis of genome segmentations.

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