MethScope-Input

Overview

MethScope uses YAME .cg files as methylation input. A .cg file is a compact binary representation of methylation data in a fixed CpG order. Before running GenerateInput(), the query .cg file and the MRMP reference .cm file must be based on the same genome build and CpG coordinate order.

This tutorial shows common input-preparation patterns for:

For complete YAME command documentation, see https://zhou-lab.github.io/YAME/. YAME documentation describes the supported CX formats and the requirement that input rows match the reference CpG coordinates exactly.

For ALLC files, this tutorial follows the ALLCools definition: https://lhqing.github.io/ALLCools/start/input_files.html.

Install command-line dependencies

Install YAME and bedtools before converting files.

conda install -c bioconda yame bedtools

Choose the correct CpG reference

Use the CpG reference that matches the genome build of your data and the MethScope MRMP reference. For example, use an mm10 CpG reference with mm10_Liu2021.cm, and use an hg38 CpG reference with hg38_Zhou2025.cm or hg38_Loyfer2023.cm.

The examples below assume that you have a YAME CpG reference file called cpg_reference.cr. Convert it to BED once:

Reference .cr files can be downloaded from the Zhou lab KYCG knowledge-base repositories:

yame unpack cpg_reference.cr | gzip > cpg_reference.bed.gz

The BED file created from the .cr reference defines the required row order for the output .cg file.

Convert M/U count BED files

Use YAME format 3 (-f3) when the input has methylated and unmethylated read counts. This is the recommended format for bisulfite sequencing data when read depth is available.

Expected input:

chr1    3000826    3000827    4    6
chr1    3001006    3001007    0    8

where columns are:

chrom    start    end    M    U

Align the file to the reference CpG coordinates and pack:

bedtools sort -i sample_mu.bed > sample_mu.sorted.bed

bedtools intersect \
  -a cpg_reference.bed.gz \
  -b sample_mu.sorted.bed \
  -loj \
  -sorted | \
awk 'BEGIN{OFS="\t"} {if ($8==".") print 0,0; else print $8,$9}' | \
yame pack -f3 - > sample.cg

Check the result:

yame info sample.cg
yame summary sample.cg

Convert ALLC files

ALLC files are tab-separated base-resolution cytosine count tables. In the ALLCools convention, each row has 7 mandatory columns and no header:

chromosome    position    strand    sequence_context    mc    cov    methylated

where position is 1-based, mc is the count of reads supporting methylation, and cov is total read coverage. For MethScope, convert CG-context rows to YAME format 3 by setting:

M = mc
U = cov - mc

The command below keeps rows whose sequence context begins with CG, converts the 1-based ALLC position to a 0-based BED interval, derives U, aligns the result to the CpG reference, and packs the final two-column M U stream.

zcat sample.allc.tsv.gz | \
awk 'BEGIN{OFS="\t"}
     $4 ~ /^CG/ {
       chrom = $1
       start = $2 - 1
       end = $2
       m = $5
       u = $6 - $5
       if (u < 0) u = 0
       print chrom, start, end, m, u
     }' | \
bedtools sort -i - > sample_mu.sorted.bed

bedtools intersect \
  -a cpg_reference.bed.gz \
  -b sample_mu.sorted.bed \
  -loj \
  -sorted | \
awk 'BEGIN{OFS="\t"} {if ($8==".") print 0,0; else print $8,$9}' | \
yame pack -f3 - > sample.cg

If your ALLC file is not compressed, replace zcat sample.allc.tsv.gz with cat sample.allc.tsv.

Convert beta or methylation fraction files

Use YAME format 4 (-f4) when the input already contains methylation fractions or beta values. Missing CpGs should be encoded as NA.

Expected input:

chr1    3000826    3000827    0.42
chr1    3001006    3001007    0.81

Convert to .cg:

bedtools sort -i sample_beta.bed > sample_beta.sorted.bed

bedtools intersect \
  -a cpg_reference.bed.gz \
  -b sample_beta.sorted.bed \
  -loj \
  -sorted | \
awk 'BEGIN{OFS="\t"} {if ($8==".") print "NA"; else print $8}' | \
yame pack -f4 - > sample_beta.cg

Format 4 stores methylation fractions but does not retain read depth.

Convert binary BED tracks

Use YAME format 0 (-fb) when each CpG is represented as present or absent. This is useful for binary tracks, peak overlaps, or binarized methylation states.

bedtools sort -i binary_track.bed > binary_track.sorted.bed

bedtools intersect \
  -a cpg_reference.bed.gz \
  -b binary_track.sorted.bed \
  -sorted \
  -c | \
cut -f4 | \
awk '{if ($1 > 0) print 1; else print 0}' | \
yame pack -fb - > binary_track.cg

Use the converted file in MethScope

After creating sample.cg, use an MRMP reference .cm from the same genome build.

library(MethScope)

query_file <- "sample.cg"
reference_pattern <- "mm10_Liu2021.cm"

input_pattern <- GenerateInput(query_file, reference_pattern)

model <- Liu2021_MouseBrain_P1000()
prediction_result <- PredictCellType(model, input_pattern)

For a full MethScope test, clone the GitHub repository and run the tutorial with inst/extdata/example.cg and inst/extdata/mm10_Liu2021.cm.

Troubleshooting

If GenerateInput() fails or returns unexpected values, check these items first: