Unmethylated Regions Encompass The Functional Space Within The Maize Genome
Ricci, W. A.
Show abstract
Delineating the functional space within genomes has been a long-standing goal shared among geneticists, molecular biologists, and genome scientists. The genome of Zea mays (maize) has served as a model for locating functional elements within the gene-distal intergenic space. A recent development has been the discovery and use of accessible chromatin as a proxy for functional regulatory elements. However, the idea has recently arisen that DNA methylation data could supplement the use of accessible chromatin data for homing in on regulatory regions. Here, I test the robustness of using DNA methylation as a proxy for functional space. I find that CHG methylation can be non-arbitrarily partitioned into hypo-methylated and hyper-methylated regions. Hypo-methylated CHG regions are stable across development and contain nearly all accessible chromatin. Note: changes that will be made in version 2: expand introduction; expand discussion; add additional analyses; expand methods; link to github scripts.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Genomic asymmetry of the Brassica napus seed: Epigenetic contributions of DNA methylation and small RNAs to subgenome bias 95%
- MDR1 DNA glycosylase regulates the expression of genomically imprinted genes and helitrons 95%
- Limited consequences for loss of RNA-directed DNA methylation in Setaria viridis domains rearranged methyltransferase (DRM) mutants 94%
Similar papers in this journal
- The maize gene maternal derepression of r1 (mdr1) encodes a DNA glycosylase that demethylates DNA and reduces siRNA expression in endosperm 95%
- Ovule siRNAs methylate protein-coding genes in trans 93%
- Characterization of Arabidopsis thaliana promoter bidirectionality and antisense RNAs by depletion of nuclear RNA decay enzymes 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.