At KonAnBio we use cutting-edge algorithms, computational tools, and machine learning techniques to enable researchers to extract valuable insights from their epigenomic datasets, understand the biology of diseases caused by the epigenome so that therapies can be optimised.
Epigenomics is the study of epigenetic changes in a cell. Epigenomics can help in genetic disorders, metabolic disorders, cancers, degenerative diseases, and changes in plant plasticity have all been linked to epigenetic errors.
At KonAnBio we analyse a wide range of epigenomic sequencing data that will help finding changes to the DNA and associated proteins that can impact gene expressions, and as a result, we might have altered cellular states, including diseases.
Most epigenetic assays focus on major mechanisms such as DNA methylation, histone modifications, DNA-binding proteins, chromatin accessibility or the 3D conformation of the chromatin.
- DNA methylation assays use next-generation sequencing or microarrays. The assays are based on bisulfite-treated DNA enable and can identify methylation events at the highest resolution.
- Histone modifications. Transcription factors as well as chemicals to the histone proteins to identify DNA-bound proteins make use of antibodies. The most common method is the ChIP-sequencing, however, newer alternatives with better resolution have been developed.
- Chromatin accessibility. Considered the gold standards assay for mapping regions of open chromatin, the ATAC-sequencing is a replacement for DNase-seq and FAIRE-seq.
- Chromatin conformation. Hi-C is a type of assay used to look at the physical interactions of genes and their distal regulatory elements, whilst ChIA-PET is used to study the proteins that cause such looping of the chromatin.
Peak calling and annotation: epigenomic sequencing based data such as ChIP-seq, ATAC-seq deals with identification, annotation, and analysing peaks, or genomic regions with signal of interest.
Exploratory analysis: using PCA and heatmap we are able to visuale annotated peaks across the sample set.
Differential peak analysis: In order to compare various illnesses, the identified peaks can be statistically compared or can be directly called from the respective read coverage signals. These statistics obtained via the differential peak analysis can be visualised as a volcano plot. For signals at sites of interest in different conditions, density heatmaps are used to visualise.
Transcription factor binding site analyses: the genome across transcription factor binding sites can be identified using chip-seq and related protocols.
DNA methylation data analysis: the first step in this analysis is the quality control and alignment of sequencing reads, followed by proceeds to calling the methylated sites.
Some of downstream analyses for DNA methylation data may include:
- Integration with gene expression data
- Epigenetic biomarker discovery
- Biological age analysis