KnowYourCG (KYCG) is a supervised learning framework designed for the functional analysis of DNA methylation data. Unlike existing tools that focus on genes or genomic intervals, KnowYourCG directly targets CpG dinucleotides, featuring automated supervised screenings of diverse biological and technical influences, including sequence motifs, transcription factor binding, histone modifications, replication timing, cell-type-specific methylation, and trait associations. KnowYourCG addresses the challenges of data sparsity in various methylation datasets, including low-pass Nanopore sequencing, single-cell DNA methylomes, 5-hydroxymethylation profiles, spatial DNA methylation maps, and array-based datasets for epigenome-wide association studies and epigenetic clocks.
The input to KYCG is a CpG set (query). The CpG sets can represent differential methylation, results from an epigenome-wide association studies, or any sets that may be derived from analysis. If analyzing sequencing data, the preferred format is a YAME-compressed binary vector of 0 and 1 to indicate whether the CpG is in set. This format assume a specific order of CpGs following the genomic coordinates. Since it’s a coordinate-free approach, the reference coordinate is critical. Please refer to the YAME documentation for details. https://zhou-lab.github.io/YAME/.
First, we will pack the bedfile into a .cg format. If the input bedfile is already sorted, you can start with the intersect step. Check out the bedtools instersect help page if you encounter any problems at this step.
Download query and knowledgebase datasets:
Whole-genome knowledgebases are available as listed in the following tables.
Then we simply run yame summary
with -m feature file for enrichment testing. We have
provided comprehensive enrichment feature files, and you can download
them from th KYCG github page mm10/hg38. You can also
create your own feature file with yame pack.
Detailed information of the output columns can be found on the yame summary
page. Basically, a higher log2oddsratio indicates a stronger association
between the feature being tested and the query set. Generally, a large
log2 odds ratio is typically considered to be around 2 or greater, with
values between 1 and 2 often being viewed as potentially important and
worthy of further investigation, while values around 0.5 might be
considered a small effect size. For significance testing, seasame
R package provided the testEnrichmentFisherN function, which is also
provided in the yame github R page. The four input parameters correspond
to the four columns from yame summary output.
ND = N_mask
NQ = N_query
NDQ = N_overlap
NU = N_universeWe can create a coarse differential methylation datasets the following way
-H controls directionality and -c controls minimum coverage.
The output is a query CG sets with proper universe background. Selecting the appropriate background for enrichment testing is crucial because it can significantly impact the interpretation of the results. Usually, we use the background set that is measured in the experiment under different conditions.
## R version 4.4.2 (2024-10-31)
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## Running under: Ubuntu 24.04.1 LTS
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## attached base packages:
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## other attached packages:
## [1] sesame_1.25.1 knitr_1.49
## [3] gprofiler2_0.2.3 SummarizedExperiment_1.37.0
## [5] Biobase_2.67.0 GenomicRanges_1.59.1
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