Tracing and tracking epiallele families in complex DNA populations.
Antonio PezoneAlfonso TramontanoGiovanni ScalaMariella CuomoPatrizia RiccioSergio De NicolaAntonio PorcelliniLorenzo ChiariottiEnrico V AvvedimentoPublished in: NAR genomics and bioinformatics (2020)
DNA methylation is a stable epigenetic modification, extremely polymorphic and driven by stochastic and deterministic events. Most of the current techniques used to analyse methylated sequences identify methylated cytosines (mCpGs) at a single-nucleotide level and compute the average methylation of CpGs in the population of molecules. Stable epialleles, i.e. CpG strings with the same DNA sequence containing a discrete linear succession of phased methylated/non-methylated CpGs in the same DNA molecule, cannot be identified due to the heterogeneity of the 5'-3' ends of the molecules. Moreover, these are diluted by random unstable methylated CpGs and escape detection. We present here MethCoresProfiler, an R-based tool that provides a simple method to extract and identify combinations of methylated phased CpGs shared by all components of epiallele families in complex DNA populations. The methylated cores are stable over time, evolve by acquiring or losing new methyl sites and, ultimately, display high information content and low stochasticity. We have validated this method by identifying and tracing rare epialleles and their families in synthetic or in vivo complex cell populations derived from mouse brain areas and cells during postnatal differentiation. MethCoresProfiler is written in R language. The software is freely available at https://github.com/84AP/MethCoresProfiler/.
Keyphrases
- dna methylation
- circulating tumor
- cell free
- single molecule
- gene expression
- genome wide
- single cell
- genetic diversity
- induced apoptosis
- nucleic acid
- transcription factor
- oxidative stress
- autism spectrum disorder
- preterm infants
- bone marrow
- circulating tumor cells
- cell therapy
- cell proliferation
- health information
- copy number
- cell cycle arrest
- endoplasmic reticulum stress
- quantum dots