Functional Analysis of DNAm Sequencing Data
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/.
QUICK START
# Code that should run only on non-Windows systems
library(knowYourCG)
# Download query and knowledgebase datasets:
temp_dir <- tempdir()
knowledgebase <- file.path(temp_dir, "ChromHMM.20220414.cm")
query <- file.path(temp_dir, "single_cell_10_samples.cg")
knowledgebase_url <- "https://github.com/zhou-lab/KYCGKB_mm10/raw/refs/heads/main/ChromHMM.20220414.cm"
query_url <- "https://github.com/zhou-lab/YAME/raw/refs/heads/main/test/input/single_cell_10_samples.cg"
download.file(knowledgebase_url, destfile = knowledgebase)
download.file(query_url, destfile = query)
# test enrichment (require YAME installed in shell)
res = testEnrichment(query, knowledgebase)
KYCG_plotDot(res, short_label=TRUE)
KNOWLEDGEBASES
The curated target features are called the knowledgebase sets. We have curated a variety of knowledgebases that represent different categorical and continuous methylation features such as CpGs associated with chromatin states, technical artifacts, gene association and gene expression correlation, transcription factor binding sites, tissue specific methylation, CpG density, etc.
Whole-genome knowledgebases are available as listed in the following tables.
| Assembly | Link |
|---|---|
| human (hg38) | https://github.com/zhou-lab/KYCGKB_hg38 |
| mouse (mm10) | https://github.com/zhou-lab/KYCGKB_mm10 |
INPUT FORMAT
For non-array data, CpG sets (query, knowledgebase, and universe)
should be formated using YAME. YAME supports binary representation of a
set (format “b”) and optionally with a universe (format “d”). Format “d”
can be created from format “b” using the yame mask
function.
If you have a BED-formated data, you can pack it to YAME-“b” format using the following pipeline. This pipeline requires BEDTools and a reference coordinate file (YAME-“r”) available below:
| Assembly | Link |
|---|---|
| human (hg38) | https://github.com/zhou-lab/KYCGKB_hg38/raw/refs/heads/main/cpg_nocontig.cr |
| mouse (mm10) | https://github.com/zhou-lab/KYCGKB_mm10/raw/refs/heads/main/cpg_nocontig.cr |
yame unpack cpg_nocontig.cr | bedtools intersect -a - -b [your_input.bed] -c -sorted |
cut -f4 | yame pack -fb - > [your_input.cg]The above assumes your input is already sorted. Check out the bedtools instersect if you encounter any problems at this step.
ENRICHMENT TESTING
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.
The following is an analysis example in R that explicitly call
yame in R.
SESSION INFO
## R Under development (unstable) (2024-10-21 r87258)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] sesame_1.25.2 knitr_1.49
## [3] gprofiler2_0.2.3 SummarizedExperiment_1.37.0
## [5] Biobase_2.67.0 GenomicRanges_1.59.1
## [7] GenomeInfoDb_1.43.2 IRanges_2.41.2
## [9] S4Vectors_0.45.2 MatrixGenerics_1.19.0
## [11] matrixStats_1.4.1 sesameData_1.25.0
## [13] ExperimentHub_2.15.0 AnnotationHub_3.15.0
## [15] BiocFileCache_2.15.0 dbplyr_2.5.0
## [17] BiocGenerics_0.53.3 generics_0.1.3
## [19] knowYourCG_1.3.13
##
## loaded via a namespace (and not attached):
## [1] DBI_1.2.3 bitops_1.0-9 rlang_1.1.4
## [4] magrittr_2.0.3 compiler_4.5.0 RSQLite_2.3.9
## [7] png_0.1-8 vctrs_0.6.5 reshape2_1.4.4
## [10] stringr_1.5.1 pkgconfig_2.0.3 crayon_1.5.3
## [13] fastmap_1.2.0 XVector_0.47.1 labeling_0.4.3
## [16] fontawesome_0.5.3 rmarkdown_2.29 tzdb_0.4.0
## [19] UCSC.utils_1.3.0 preprocessCore_1.69.0 purrr_1.0.2
## [22] bit_4.5.0.1 xfun_0.49 cachem_1.1.0
## [25] jsonlite_1.8.9 blob_1.2.4 DelayedArray_0.33.3
## [28] BiocParallel_1.41.0 parallel_4.5.0 R6_2.5.1
## [31] bslib_0.8.0 stringi_1.8.4 RColorBrewer_1.1-3
## [34] jquerylib_0.1.4 Rcpp_1.0.13-1 wheatmap_0.2.0
## [37] readr_2.1.5 Matrix_1.7-1 tidyselect_1.2.1
## [40] abind_1.4-8 yaml_2.3.10 codetools_0.2-20
## [43] curl_6.0.1 lattice_0.22-6 tibble_3.2.1
## [46] plyr_1.8.9 withr_3.0.2 KEGGREST_1.47.0
## [49] evaluate_1.0.1 Biostrings_2.75.3 pillar_1.10.0
## [52] BiocManager_1.30.25 filelock_1.0.3 plotly_4.10.4
## [55] RCurl_1.98-1.16 BiocVersion_3.21.1 hms_1.1.3
## [58] ggplot2_3.5.1 munsell_0.5.1 scales_1.3.0
## [61] glue_1.8.0 lazyeval_0.2.2 tools_4.5.0
## [64] data.table_1.16.4 grid_4.5.0 tidyr_1.3.1
## [67] AnnotationDbi_1.69.0 colorspace_2.1-1 GenomeInfoDbData_1.2.13
## [70] cli_3.6.3 rappdirs_0.3.3 S4Arrays_1.7.1
## [73] viridisLite_0.4.2 dplyr_1.1.4 gtable_0.3.6
## [76] sass_0.4.9 digest_0.6.37 SparseArray_1.7.2
## [79] ggrepel_0.9.6 farver_2.1.2 htmlwidgets_1.6.4
## [82] memoise_2.0.1 htmltools_0.5.8.1 lifecycle_1.0.4
## [85] httr_1.4.7 bit64_4.5.2