Phase long reads and CpG methylations from Oxford Nanopore Technologies
Citation: Akbari, V., Garant, JM., O'Neill, K. et al. Megabase-scale methylation phasing using nanopore long reads and NanoMethPhase. Genome Biol 22, 68 (2021).
Access here
This will clone the github repository and then installs NanoMethPhase depndencies in a conda environment named nanomethphase. Then you can run nanomethphase.py which is in the path you cloned the repository.
git clone https://github.com/vahidAK/NanoMethPhase.git
cd NanoMethPhase
conda env create -f envs/environment.yaml
conda activate nanomethphase
python nanomethphase.py
You can also use other package management systems to create a virtual environment and install dependencies in the environment.yaml file or install them in your base environment and then use nanomethphase.py
Preparing methylation call file for methylation phasing or conversion of a bam file to whole genome bisulfite sequencing format for visualization in IGV.
usage: nanomethphase methyl_call_processor --MethylCallfile METHYLCALLFILE
[-h]
[--tool_and_callthresh TOOL_AND_CALLTHRESH]
[--motif MOTIF] [--threads THREADS]
[--chunk_size CHUNK_SIZE]
Preparing methylation call file for methylation phasing. Extended usage:
nanomethphase methyl_call_processor -mc [FILE] | sort -k1,1 -k2,2n -k3,3n |
bgzip > [FILE].bed.gz && tabix -p bed [FILE].bed.gz
required arguments:
--MethylCallfile METHYLCALLFILE, -mc METHYLCALLFILE
The path to the per-read methylation call file.
optional arguments:
-h, --help show this help message and exit
--tool_and_callthresh TOOL_AND_CALLTHRESH, -tc TOOL_AND_CALLTHRESH
Software you have used for methylation calling
(nanoplish, megalodon, deepsignal):methylation call
threshold for considering a site as methylated,
unmethylated or ambiguous in methylation call file.
Default is nanopolish:2 which is when methylation
calling performed by nanopolish and a CpG with llr >=
2 will be considered as methylated and llr <= -2 as
unmethylated, anything in between will be considered
as ambiguous call.For megalodon, call thresold will be
delta probability (prob_methylated -
prob_unmethylated) which is e^mod_log_prob - (1 -
e^mod_log_prob). For example, with a call threshold of
0.6 (0.8-0.2) CpGs between 0.8 and 0.2 probability
will be considered as ambiguous and >=0.8 as
methylated and <=0.2 as unmethylated. For deepsignal,
as for megalodon, this call threshold is delta
probability (prob_methylated -
prob_unmethylated).NOTE: Megalodon per-read text file
must be for only 5mC CpGs . Do not use per-read text
file where there are predictions for 2 or more
modifications (e.g. 5mC and 5hmC)
--motif MOTIF, -mf MOTIF
The motif you called methylation for (cpg), Currently
just cpg.
--threads THREADS, -t THREADS
Number of parallel processes. Default is 4
--chunk_size CHUNK_SIZE, -cs CHUNK_SIZE
Number of reads send to each proccessor. Default is
100
Phasing reads and CpG Methylation data to the coresponding haplotypes.
usage: nanomethphase phase --bam BAM --vcf VCF --output OUTPUT
[--reference REFERENCE]
[--methylcallfile METHYLCALLFILE] [-h]
[--outformat OUTFORMAT] [--per_read PER_READ]
[--min_variant MIN_VARIANT] [--hapratio HAPRATIO]
[--mapping_quality MAPPING_QUALITY]
[--min_base_quality MIN_BASE_QUALITY]
[--average_base_quality AVERAGE_BASE_QUALITY]
[--include_indels] [--include_supplementary]
[--motif MOTIF] [--window WINDOW]
[--threads THREADS] [--chunk_size CHUNK_SIZE]
[--overwrite]
Phasing reads and Methylation
required arguments:
--bam BAM, -b BAM The path to the cordinate sorted bam file.
--vcf VCF, -v VCF The path to the phased vcf file.
--output OUTPUT, -o OUTPUT
The path to directory and prefix to save files. e.g
path/to/directory/prefix
conditional required arguments based on selected output format(s):
--reference REFERENCE, -r REFERENCE
The path to the reference file in case you selected
bam2bis output format. Fasta file must be already
indexed using samtools faidx.
--methylcallfile METHYLCALLFILE, -mc METHYLCALLFILE
If you have selected methylcall or bam2bis output
format to phase methylation or make mock bisulfite
bams, give the path to the bgziped and indexed
methylation call file from methyl_call_processor
Module.
General optional arguments:
-h, --help show this help message and exit
--outformat OUTFORMAT, -of OUTFORMAT
What type of output you want (bam,bam2bis,methylcall).
Default is bam2bis,methylcall. bam: outputs phased
reads to seperate bam files. bam2bis: outputs phased
reads to seperate bam files converted to bisulfite bam
format for visualisation in IGV. methylcall: outputs
phased methylcall and methylation frequency files for
seperate haplotypes. You can select any format and
multiple or all of them seperated by comma.
--per_read PER_READ, -pr PER_READ
If it is your second try and you have per read info
file from the first try you can specify the per-read
file to make the process faster. This also enables you
to try different threshols of options (-mv, -mbq, -mq,
-hr, -abq), include/exclude indels, include/exclude
supp reads.
--min_variant MIN_VARIANT, -mv MIN_VARIANT
minimum number of phased variants must a read have to
be phased. Default is 1
--hapratio HAPRATIO, -hr HAPRATIO
0-1 .The maximum ratio (# of variants from one halotype
over the other) between haplotypes to tag as H1 or H2
(H2/H1 when #variants at H1 > H2 and H1/H2 when #variants at
H2 > H1). Default is 0.75
--mapping_quality MAPPING_QUALITY, -mq MAPPING_QUALITY
An integer value to specify minimum read mapping
quality. Default is 20
--min_base_quality MIN_BASE_QUALITY, -mbq MIN_BASE_QUALITY
Only include bases with this minimum base quality.
Default is 7.
--average_base_quality AVERAGE_BASE_QUALITY, -abq AVERAGE_BASE_QUALITY
Minimum quality that variants tagged to a haplotype
should have compare to the other haplotype when
average of qualities is not informative. This will be
used when the average base quality of variants mapped
to two haplotypes for one read is not informative and
decision cannot be made (e.g. when 10 variants of HP1
mapped to a read with average quality of 30, but only
one variant from HP2 mapped to the same read with
bq=35). Then, instead of considering average of
qualities, the tool will count number of variants in
both haplotypes that meet the given
average_base_quality and uses the counts to make
decision. Default is 20.
--include_indels, -ind
Also include indels for read phasing to haplotypes.
--include_supplementary, -is
Also include supplementary reads
--motif MOTIF, -mt MOTIF
The motif you called methylation for (cpg), Currently
just cpg.
--window WINDOW, -w WINDOW
if you want to only phase read for a region or
chromosome. You must insert region like this chr1 or
chr1:1000-100000.
--threads THREADS, -t THREADS
Number of parallel processes. Default is 4
--chunk_size CHUNK_SIZE, -cs CHUNK_SIZE
Number of reads send to each proccessor. Default is
100
--overwrite, -ow If output files exist overwrite them
To perform differential Methylation analysis for two group comparison. It is a wrapper for DSS R package to detect differentially methylated regions between haplotypes.
usage: nanomethphase dma --case CASE --control CONTROL --out_dir OUT_DIR
--out_prefix OUT_PREFIX [-h] [--columns COLUMNS]
[--coverage COVERAGE] [--Rscript RSCRIPT]
[--script_file SCRIPT_FILE] [--overwrite]
[--smoothing_span SMOOTHING_SPAN]
[--smoothing_flag SMOOTHING_FLAG]
[--equal_disp EQUAL_DISP] [--pval_cutoff PVAL_CUTOFF]
[--delta_cutoff DELTA_CUTOFF] [--minlen MINLEN]
[--minCG MINCG] [--pct_sig PCT_SIG]
[--dis_merge DIS_MERGE]
Differential Methylation analysis for two group only (to find DMRs using
phased frequency results) using DSS R package.
required arguments:
--case CASE, -ca CASE
The path to the tab delimited input methylation
frequency or ready input case file(s) (First rwo is
header which will be ignored). If multiple files,
files must be in the same directory and give the path
to the directory.
--control CONTROL, -co CONTROL
The path to the tab delimited input methylation
frequency or ready input control file(s) (First rwo is
header which will be ignored). If multiple files,
files must be in the same directory and give the path
to the directory.
--out_dir OUT_DIR, -o OUT_DIR
The path to the output directory
--out_prefix OUT_PREFIX, -op OUT_PREFIX
The prefix for the output files
General optional arguments.:
-h, --help show this help message and exit
--columns COLUMNS, -c COLUMNS
Comma seperated Columns in the methylation frequency
files that include the following information,
respectively: chromosome start(CG_position) strand
coverage methylation_frequency. If the methylation
frequency file does not have strand level information
then just enter columns number for chromosome
start(CG_position) coverage
methylation_frequency. Default is that your input files
are already in a format required by DSS so you do not
need to select any column. If you are giving as input
NanoMethPhase frequency files select this: --columns
1,2,4,5,7. When strand column is given the assumption
is that negative strand positions are 1 bp greater
than positive strand, just like NanoMethPhase's
frequency outputs.
--coverage COVERAGE, -cov COVERAGE
Minimum coverage cutoff. Default is 1. It is
recommended that do not filter for coverage as DSS R
package will take care of it. For strand-level inputs,
this coverage is per-strand. When no --columns is
given, coverage option is skipped.
--Rscript RSCRIPT, -rs RSCRIPT
The path to a particular instance of Rscript to use.
--script_file SCRIPT_FILE, -sf SCRIPT_FILE
The path to the DSS_DMA.R script file. By default the
script that was shipped during
nanomethphase installation will be used.
--overwrite, -ow If output files exist overwrite them.
optional arguments that will be used in DSS DMLtest function.:
--smoothing_span SMOOTHING_SPAN, -sms SMOOTHING_SPAN
The size of smoothing window, in basepairs. Default is
500.
--smoothing_flag SMOOTHING_FLAG, -smf SMOOTHING_FLAG
TRUE/FALSE. A flag to indicate whether to apply
smoothing in estimating mean methylation levels. It is
recommended to use smoothing TRUE for whole-genome BS-
seq data, and smoothing FALSE for sparser data such
like from RRBS or hydroxyl-methylation data (TAB-seq).
see -ed option and DSS R package details for more
information. Default is TRUE.
--equal_disp EQUAL_DISP, -ed EQUAL_DISP
TRUE/FALSE. A flag to indicate whether the dispersion
in two groups are deemed equal or not. Default is
FALSE and the dispersion shrinkages are performed on
two conditions independently. More info on -ed and
-smf: When there is no biological replicate in one or
both treatment groups, users can either (1) specify
equal.disp TRUE, which assumes both groups have the
same dispersion, then the data from two groups are
combined and used as replicates to estimate
dispersion; or (2) specify smoothing TRUE, which uses
the smoothed means (methylation levels) to estimate
dispersions via a shrinkage estimator. This smoothing
procedure uses data from neighboring CpG sites as
"pseudo-replicate" for estimating biological variance.
optional arguments that will be used in DSS callDML and callDMR functions.:
--pval_cutoff PVAL_CUTOFF, -pvc PVAL_CUTOFF
A threshold of p-values for calling DMLs and DMRs.
When delta is not specified, Loci with p-values less
than this threshold will be picked as DML and also
joint to form the DMRs. See DSS R package 'details'
for more information for this regarding DMLs and DMRs.
Default is 0.001.
--delta_cutoff DELTA_CUTOFF, -dc DELTA_CUTOFF
A threshold for defining DMLs and DMRs. In DML
detection procedure, a hypothesis test that the two
groups means are equal is conducted at each CpG site.
Here if 'delta' is specified, the function will
compute the posterior probability that the difference
of the means are greater than delta, and then call DML
and construct DMR based on that. This only works when
the test results are from 'DMLtest', which is for two-
group comparison. See DSS R package for more
information. Default is 0.
optional arguments that will be used in DSS callDMR function.:
--minlen MINLEN, -ml MINLEN
Minimum length (in basepairs) required for DMR.
Default is 100 bps.
--minCG MINCG, -mcg MINCG
Minimum number of CpG sites required for DMR. Default
is 15.
--pct_sig PCT_SIG, -pct PCT_SIG
In all DMRs, the percentage of CG sites with
significant p-values (less than p.threshold) must be
greater than this threshold. Default is 0.5. This is
mainly used for correcting the effects of merging of
nearby DMRs.
--dis_merge DIS_MERGE, -dm DIS_MERGE
When two DMRs are very close to each other and the
distance (in bps) is less than this number, they will
be merged into one. Default is 100 bps. See dma
section notes for more details.
Convert a bam file to a mock whole-genome bisulfite sequencing format for visualization in IGV. Note that the reads in the output bam from this module are not exactly the same as the reads in the input bam. In the output bam the sequence of the reads corresponds to the sequence from reference they mapped to and cytosine is also converted based on its methylation status.
usage: nanomethphase bam2bis --bam BAM --reference REFERENCE --methylcallfile
METHYLCALLFILE --output OUTPUT [-h]
[--mapping_quality MAPPING_QUALITY]
[--motif MOTIF] [--methylation]
[--include_supplementary] [--window WINDOW]
[--threads THREADS] [--chunk_size CHUNK_SIZE]
[--overwrite]
Convert a bam file to a bisulfite format for nice visualization in IGV
required arguments:
--bam BAM, -b BAM The path to the cordinate sorted bam file.
--reference REFERENCE, -r REFERENCE
The path to the reference file. Fasta file must be
already indexed using samtools faidx.
--methylcallfile METHYLCALLFILE, -mc METHYLCALLFILE
The path to the the bgziped and indexed methylation
call file from methyl_call_processor Module.
--output OUTPUT, -o OUTPUT
The path to the output directory and desired prefix.
optional arguments:
-h, --help show this help message and exit
--mapping_quality MAPPING_QUALITY, -mq MAPPING_QUALITY
An integer value to specify minimum mapping quality of
the read. Default is 20
--motif MOTIF, -mt MOTIF
The motif you called methylation for (cpg), Currently
just cpg.
--methylation, -met Output methylation call and frequency for converted
reads.
--include_supplementary, -is
Also include supplementary reads
--window WINDOW, -w WINDOW
if you want to only convert reads for a region or
chromosome. You must insert region like this chr1 or
chr1:1000-100000.
--threads THREADS, -t THREADS
Number of parallel processes. Default is 4
--chunk_size CHUNK_SIZE, -cs CHUNK_SIZE
Number of reads send to each proccessor. Default is
100
--overwrite, -ow If output files exist overwrite it
In order to get the phased methylome you also need the following third-party software:
Nanopolish, f5c >=v0.7 (f5c is an optimised re-implementation of Nanopolish), Megalodon, or DeepSignal to call CpG methylation (More info regarding methylation callers provided at the end of tutorial.). Note that when NanoMethPhase phases methylation, it matches the read coordinates from methylcall file to the read coordinates from bam file. Therefore, the optimal results will be when the same bam used for methylation calling is used for NanoMethPhase phasing. This is the case for methylation callers such as nanopolsih and f5c because they need an input bam file to call methylation, so the same bam can be used for NanoMethPhase as well. Input bam file must contain base qualities as NanoMethPhase uses them during phasing.
Clair3 or other variant callers to call variants for your sample. Alternatively, you might already have variant calling data for example from short-read sequencing.
WhatsHap to phase variants. You may use other phasing tools or phased vcf input, however the phase information for the phased heterozygous variants, that are used by NanoMethPhase, must be indicated by 0|1 or 1|0 in the start of 10th column of the vcf file.
Here we use nanopolish
NOTE: Fastqs must be merged to a single file
nanopolish index -d /path/to/fast5s_directory/ fastq.fastq
nanopolish call-methylation -t <number_of_threads> -q cpg -r /path/to/fastq_fromstep-1/fastq.fastq -b /path/to/sorted_and_indexed/bam.bam -g /path/to/reference.fa > /path/to/MethylationCall.tsv
For the full tutorial please refer to Nanopolish page on GitHub.
Here we use Clair3
run_clair3.sh --bam_fn=/path/to/Nanopore_aligned_reads.bam \
--ref_fn=/path/to/reference.fa \
--output=/path/to/output/directory \
--threads=<# of threads> --platform=ont \
--model_path=/path/to/model
After variant calling the results will be in merge_output.vcf.gz file in the output directory. You can further filter this file to keep PASS variants:
gunzip -c /path/to/output/directory/merge_output.vcf.gz | awk '$1 ~ /^#/ || $7=="PASS"' > PassedVariants.vcf
Or if you want to filter based on a quaity threshold
gunzip -c /path/to/output/directory/merge_output.vcf.gz | awk '$1 ~ /^#/ || $6 > <quality threshold>' > QualityFilteredVariants.vcf
Here we use WhatsHap to phase variants
whatshap phase --ignore-read-groups --reference reference.fa -o whatshap_phased.vcf input.vcf sorted_indexed.bam
This wil only phase SNVs, to also phase indels you can add --indels option:
whatshap phase --ignore-read-groups --indels --reference reference.fa -o whatshap_phased.vcf input.vcf sorted_indexed.bam
For the full tutorial please refer to WhatsHap page on GitHub.
NOTE: NanoMethPhase requires a single sample vcf file in which phase information for the het variants in the 10th column indicated by "|" (i.e. 0|1 or 1|0).
python nanomethphase.py methyl_call_processor -mc MethylationCall.tsv -t 20 | sort -k1,1 -k2,2n -k3,3n | bgzip > MethylationCall.bed.gz && tabix -p bed MethylationCall.bed.gz
See nanomethphase methyl_call_processor help for more information and how to run it for other methylation callers.
python nanomethphase.py phase -b sorted.bam -v Phased.vcf -mc MethylationCall.bed.gz -r hg38.fa -o Test_methylome -of bam,methylcall,bam2bis -t 64
You can select --include_indels option to also use indels during phasing.
You can select 3 output options:
bam: output phased bam files
methylcall: this will output phased methylation call (MethylCall.tsv, read level data) and methylation frequency files (MethylFrequency.tsv, Aggregated methylations for each region. These files can be used to detect differentially methylated regions between haplotype using dma module.).
The headers for methylation call files are as follow:
| Shorten | Description |
|---|---|
| chromosome | Chromosome name. |
| start | Start position of CpG. |
| end | End position of CpG. |
| strand | Strand. |
| read_name | Read ID. |
| llr_Or_DeltaProb | llr for CpG from nanopolosh (Or delta prob in case of megalodon and deepsignal). |
CpG coordinates are zero-based here and coordinates from both strands are based on positive strand.
The headers for methylation frequency files are as follow:
| Shorten | Description |
|---|---|
| chromosome | Chromosome name. |
| start | Start position of CpG. |
| end | End position of CpG. |
| strand | Strand. |
| NumOfAllCalls | Number of all called CpGs. |
| NumOfModCalls | Number of all CpGs that called as methylated. |
| MethylFreq | Methylation frequency (NumOfModCalls/NumOfAllCalls). |
CpG coordinates are zero-based here and coordinates of negative strand are 1 bp greater than the positive strand.
NOTE: NanoMethPhase outputs strand-level frequency files to not lose strand information if you needed them. However, usually methlation information from both strands are aggregated for each CpG to have per-CpG methylation. If you want to aggregate the information from both strand, you need to aggregate number of all calls and number of methylated calls from both strands for each CpG and then calculate the new frequency for each CpG site. For example, following command aggregates data from both strands and calculates new methylation frequency for each CpG (You need to install datamash before using this command):
sed '1d' NanoMethPhase_HP1_MethylFrequency.tsv | awk -F'\t' '{if ($4=="-") {$2=$2-1;$3=$3-1}; print $1,$2,$3,$5,$6}' OFS='\t' | sort -k1,1 -k2,2n | datamash -g1,2,3 sum 4,5 | awk -F'\t' '{print $0,$5/$4}' OFS='\t' | sed '1i chromosome\tstart\tend\tNumOfAllCalls\tNumOfModCalls\tMethylFreq' > HP1_MethylFrequency.tsv
During DMA, dma module also aggregates all calls and methylated calls from both strands for each CpG ("ReadyForDSS" files) and then performs differential methylation analysis.
bam2bis: output mock whole-genome bisulfite converted bam files which can be visualized in IGV. Note that the reads in the output bams are not exactly the same as the reads in the input bam. In the output bams the sequence of the reads corresponds to the sequence from reference they mapped to and cytosine is also converted based on its methylation status.
NOTE: NanoMethPhase will also output a PerReadInfo.tsv file. This file includes the following information:
| Shorten | Description |
|---|---|
| chromosome | Chromosome that read mapped to. |
| ReadRefStart | Start position where the read mapped. |
| ReadRefEnd | End position where the read mapped. |
| ReadID | Read ID. |
| strand | Strand. |
| ReadFlag:Is_Supplementary | Bitwise flag of the read:If read is supplementary or not. |
| ReadLength:MappingQuality | The length of mapped read:Mapping quality of the read. |
| Position:BaseQuality:HP1_Variants | Position and Phred quality of bases from read at haplotype 1. |
| Position:BaseQuality:HP2_Variants | Position and Phred quality of bases from read at haplotype 2. |
Base quality for indels represent the base quality of the first base. Coordinates are zero-based in per-read info file.
Having this file allow you to use it along with the vcf file which improves the speed significantly for the next runs. This also allows you to try different thresholds of options (-mq, -mbq, -mv, -hr, -abq, -ind, -is). Note that per-read file from previous version cannot be used to run with different mapping quality or include/exclude supplementary reads.
python nanomethphase.py dma -c 1,2,4,5,7 -ca <path to methylation frequency for haplotype1> -co <path to methylation frequency for haplotype2> -o <output directory> -op <output Prefix>
We use DSS R/Bioconductor package to call DMRs between haplotypes. -dm, -ml, -mcg, -sms, -smf, -ed, -pvc, -dc and -pct options and their help are from DSS R package and you can read DSS documentation for more information.
callDMR.txt is the main output you need that stores differentially methylated regions, callDML.txt is the output that stores differentialy methylated loci and DMLtest.txt is the output that stores statistical test results for all loci. For more documentation of output data refere to DSS documentation page. During DMA, for inputs with methylation from both strands, such as HP1 and HP2 here, methylation information (number of all reads/calls and number of modified reads/calls) will be aggregated from both strands on the positive strand for each CpG site (stored in "ReadyForDSS" files. CpG position of negative strand converted to positive strand by subtracting 1). Therefore, the cordinates of the outputs are based on the positive strand.
Note: You can refine your DMR list afterwars based on "diff.methy" column, which is the difference of average methylations at DMR from both comparisons, and/or areaStat. For list of DMLs, You can also use "diff" column that is difference of methylations at CpG site from both comparisons to further refine your DML list. You may use other columns as well to further refine your results.
Note: When dis_merge (dis.merge) is greater than minlen, current versions of DSS package just consider dis.merge equal to minlen. Therefore, the maximum dis.merge is minlen even if you selected a dis.merge greater than minlen (You can read issue #31 here for more info.).
We have included an example data in the Example_Data folder which you can use for a quick detection of haplotype methylome on 1Mb of chr21.
If you want to try workflow from basecalling to methylation phasing we have included a subset of NA12878 data (chr11:2600000-2800000) at this link which you can download and try.
Output Per-read methylation call file from the current versions of methylation callers mentiond above (Nanopolish, f5c>=v0.7, Megalodon, and DeepSignal) are compatible with NanoMethPhase. Here are some more information about their output per-read methylation call file and compatibility with methyl_call_processor:
nanopolish and f5c>=v0.7 produce the following columns and the CpG coordinates are zero-based and coordinates for both strands are based on positive strand (positions for the CpG from both strands are the same):
chromosome strand start end read_name log_lik_ratio log_lik_methylated log_lik_unmethylated num_calling_strands num_motifs sequence
chr2 + 200000365 200000365 50152360-5abb-4e1f-9ce0-c08a49d65b57 3.91 -142.10 -146.01 1 1 GTGAACGCTTT
chr2 + 200000776 200000776 50152360-5abb-4e1f-9ce0-c08a49d65b57 -20.59 -243.72 -223.13 1 1 TAACTCGATTT
chr2 - 200000365 200000365 607a605c-f01b-4b02-a8d5-b4c8adb88e6b 4.93 -257.29 -262.22 1 1 GTGAACGCTTT
chr2 - 200000776 200000776 607a605c-f01b-4b02-a8d5-b4c8adb88e6b -11.09 -225.59 -214.50 1 1 TAACTCGATTT
Megalodon per-read text methylation call output has the following columns and CpG coordinates are zero-based and coordinates of negative strand are 1 bp greater than positive strand (The methylation call file must be only for methylation. Do not use per-read methylation file that has multiple modification calls, e.g. 5mC and 5hmC):
read_id chrm strand pos mod_log_prob can_log_prob mod_base
56780a98-ccb3-41a5-8ed1-fc069412fc13 chr11 + 21488565 -0.9126647710800171 -0.5132502558405262 m
56780a98-ccb3-41a5-8ed1-fc069412fc13 chr11 + 21486004 -0.8042076826095581 -0.5931974211226271 m
2cc45d27-6084-49f1-b156-34501adc7651 chr11 - 21488566 -3.271272659301758 -0.03869726232984402 m
2cc45d27-6084-49f1-b156-34501adc7651 chr11 - 21486005 -4.3451995849609375 -0.013053750265459633 m
DeepSignal methylation call file has the following columns and CpG coordinates are zero-based and coordinates of negative strand are 1 bp greater than the positive strand:
chrom pos strand pos_in_strand readname read_strand prob_0 prob_1 called_label k_mer
chr11 2669073 + -1 19b5bd8e-0a50-449d-8dc1-ea2dc4e2fe2b t 0.09740365 0.90259635 1 TACCCTGCCGTATCAGT
chr11 2669107 + -1 19b5bd8e-0a50-449d-8dc1-ea2dc4e2fe2b t 0.13432296 0.865677 1 ACTGGCTACGTGTGGCT
chr11 2669074 - -1 12652f63-7676-4ad8-b7bf-af1aec4b282d t 0.13398732 0.8660127 1 CACTGATACGGCAGGGT
chr11 2669108 - -1 12652f63-7676-4ad8-b7bf-af1aec4b282d t 0.12144542 0.87855464 1 GAGCCACACGTAGCCAG