Epigenomics data analysis
See behind expression patterns with genome-wide epigenomics data analysis.
Researchers venturing into epigenomics often aim to map the dynamic state of the DNA in order to explain phenomena observed via gene expression studies. Our ChIP-sequencing data analysis pipeline has been optimized to identify both narrow transcription factor binding sites and wider histone binding sites. Clients interested in the binding motifs can opt for our motif discovery analysis, which is based on sequences where these molecules bind.
With our epigenomics data analysis pipelines, methylated CpG islands can be identified from both bisulfite sequencing data (Bis-seq or RRBS) and enrichment-based DNA methylation sequencing data (MeDIP-seq). The methylated regions are then annotated with information such as the overlapping of known promoters or enhancer regions, in order to help in interpreting the biological meaning of these events. Linking these data back to expression data allows the identification of functional methylation events in your experiments.
Read more about our epigenomics data analysis:
- DNA binding sites (ChIP-seq) See the transcriptomic potential of your transcription factor, at a genome-wide level
Chromatin immunoprecipitation of a DNA-binding protein, coupled with next-generation sequencing (ChIP-seq) is one of the most widely used high-throughput epigenomics measurement methods. From such data, we can identify protein binding sites throughout the genome. We deliver a list of significant peaks that are annotated with the genomic location and statistical information, such as width, number of reads, significance p-values, location relative to the nearest genes (distance to TSS), location within genes (exon, intron, UTR), and the binding motif found within the peak. The binding sites are often studied in parallel with transcriptomics data in order to reveal the genes that are likely to be under regulation by the DNA-binding protein of interest. If expression data exist, the expression of nearest gene will also be included to make the association easier.
- Statistically significant binding locations
- Functional annotation of the binding loci
- Comparison of binding events between samples
- Chromatin state (ChIP-seq) Full analysis of transcription potential by integrating histone modification data
Using antibodies that target specific histone modifications, then pulling down and sequencing the DNA results in genome-wide epigenomics data indicating the positions of modified histones. Targeting multiple different markers and integrating the data can reveal a map of chromatin state that indicates promoters, active and inactive enhancers and actively transcribed genes. In addition to the fully annotated locations of histones with specific modifications, we can also deliver an interpretation of the chromatin state, given enough measurements of different modifications.
- Statistically significant binding locations with functional annotation
- Comparison of binding events between samples
- Chromatin state interpretation based on combinations of histone modifications
- DNA-methylation (BS-seq, RRBS-seq, MeDIP-seq) Find out how methylation affects transcriptional patterns
Addition of methyl groups to cytosines in DNA modifies the expression levels of nearby genes. Methylated DNA can either be pulled down and sequenced (MeDIP-seq) or unmethylated cytosines can be converted to uracil and sequenced (bisulphite sequencing). We can map, annotate and compare the methylated CpG islands using a range of protocols in order to make it easier for you to interpret your results. Methylation profiles can also be analyzed integratively with other epigenomics or transcriptomics measurements.
- Quantification of methylation for all CpG islands
- Functional annotation for differentially methylated CpG islands
- Methylated individual cytosines (for BS- and RRBS-seq)
- Open chromatin sequencing (ATAC-seq) NGS-based assays allow you to study regulation by histone occlusion
The dynamic state of chromatin is a central focus within epigenomics, and can be studied by measuring the openness of chromatin throughout the genome. Segments of genome that are not tightly packed can be mapped using ATAC sequncing. Open chromatin is connected to active regions in regard to gene expression or regulation of expression. Therefore transcriptomics data are usually integrated with open chromatin information. Our analysis will indicate regions of open chromatin, with annotations regarding what genes are within that region and how the regions may have changed between your samples.
- Loci of open chromatin
- Differentially open chromatin between samples
- Functional annotation of open chromatin loci