An increasing number of studies evidence the need of coupling several epigenomic technologies in order to better understand the phenotypic changes observed either during normal development or during pathological processes such as cancer, neurodegenerative or chronic diseases.
For example, phenotypic changes observed in cancer cells are strongly linked to variations both in DNA methylation and chromatin modification patterns. Gene expression is strongly regulated by DNA methylation and chromatin dynamics; the binding sites linked to chromatin modifications are commonly integrated with transcriptomics data in order to discover the genes regulated by a specific protein. Transcriptomics data can also be integrated with open chromatin regions to accurately assess gene regulation patterns. Methylation profiles can equally be analyzed integratively with other epigenomics or transcriptomics measurements in order to understand the role of methylation with over or under expression of genes. Given the availability of different ‘omics’ data, we strongly recommend combining any of these techniques when interpreting epigenetic mechanisms.
What type of data can we combine?
ChIP-Seq with methylation
ATAC-Seq with methylation
mRNA-Seq with methylation data
sncRNA-Seq with ChIP-Seq or ATAC-Seq
mRNA-Seq with ChIP-Seq or ATAC-Seq
...and more
This analysis explores different linear mathematical models to estimate the relationship between two sets of omics data.
Complete analysis
Summary statistics for the different technologies used
Trimmed and filtered reads in fastQ files after sequencing QC
BAM sorted files from alignment to reference genome or transcriptome (indexed bam files and bigwig files included)
Outcome of interest:
List of peaks with their enrichment level (ChIP-Seq, ATAC-Seq, MeDIP-Seq)
List of CpGs with their methylation percentage (WGBS, RRBS, EPIC, BSAS)
Matrix with expression abundance estimation (total, messenger and small non-coding RNA)
Comparative analysis aimed at finding differential responses between two groups of samples
Functional gene annotation on features with differential responses
Integration of two sets of omics data - linear regression
Gene ontology enrichment analysis of correlated features
