chroGPS2: differential analysis of epigenome maps in R

1Abstract1.1BackgroundsIn the last years we have faced an unprecedented growth in the availability of high-throughput epigenomics data related to the genomic distribution of epigenetic marks, namely transcription factors, histone modifications and other DNA binding proteins. This also pointed out the need for efficient tools for integration, visualization and functional analysis of genomics and epigenomics data. On this subject, we previously developed a computational framework, chroGPS, to integrate and visualize the associations between epigenetic factors and their relation to functional genetic elements in low-dimensional maps. We demonstrated the usefulness of our approach with several practical case studies based on well-defined biological hypothesis.1.2ResultsHere we introduce chroGPS version 2, a major update of our previously developed software with new functionalities for differential analysis of epigenomes. Methods are provided for efficient integration and comparison of data from different conditions or biological backgrounds, accounting and adjusting for systematic biases in order to provide an efficient and statistically robust base for differential analysis. We also include new useful functionalities for general data assessment and quality control prior to comparing maps, such as functions to study chromatin domain conservation between epigenomic backgrounds, to detect gross technical outliers and also to help in the selection of candidate marks for de-novo epigenome mapping. Our software, implemented in R as a Bioconductor package, provides detailed reference and user manuals to allow the final user to reproduce the presented case studies and use them as a starting point for generating and exploring epigenetic maps according to their own experimental needs.1.3ConclusionchroGPS2 extends our previously developed software, providing now a complete, intuitive, efficient and statistically robust framework for generation, visualization, characterization and differential analysis of epigenomic maps, in a way which is easy to adapt to different biological and technical backgrounds allowing exploration and hypothesis testing in multiple biological scenarios.