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Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.
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Aggregate and Heatmap Representations of Genome-Wide Localization Data Using VAP, a Versatile Aggregate Profiler.
Mylène Brunelle1, Charles Coulombe2, Christian Poitras3
1Département de biologie, Faculté des sciences, Université de Sherbrooke, 2500 boul. de l'Université, Sherbrooke, QC, Canada, J1K 2R1.
Methods in Molecular Biology (Clifton, N.J.)
|September 26, 2015
Summary
This chapter details best practices for using the versatile aggregate profiler (VAP) tool to analyze chromatin signal enrichment data. It focuses on generating aggregate profiles and highlights new features in VAP version 1.1.0 for improved genomic data analysis.
Area of Science:
- Genomics
- Epigenetics
- Computational Biology
Background:
- Analyzing genome-wide signal enrichment, like histone modifications, often requires visualizing data over genomic regions.
- Aggregate profiles and individual profiles are crucial for interpreting this data, especially around genes.
Purpose of the Study:
- To describe best practices for generating aggregate and individual profiles using the versatile aggregate profiler (VAP) tool.
- To explain various applications of the VAP tool for analyzing chromatin feature signal enrichment.
- To highlight new functionalities in VAP version 1.1.0.
Main Methods:
- Utilizing the versatile aggregate profiler (VAP) software.
- Generating aggregate profiles from experimental genomic data.
- Applying VAP to analyze signal enrichment of chromatin features (e.g., histone modifications).
Main Results:
- Established best practices for creating genomic signal profiles.
- Demonstrated the utility of VAP for visualizing chromatin data over regions of interest.
- Showcased enhanced capabilities of VAP version 1.1.0.
Conclusions:
- The versatile aggregate profiler (VAP) is an effective tool for analyzing and visualizing genome-wide chromatin data.
- Best practices and new features in VAP 1.1.0 enhance the interpretation of signal enrichment profiles.
- VAP facilitates a deeper understanding of epigenetic modifications across the genome.
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