DNA Methylation: Oxford Nanopore Data Processing Workflow
Learning Outcomes
In this tutorial, you will learn how to process raw files from ONT sequencing from basecalling, mapping to generating a QC report. You will also learn how to visualise modified base calls in IGV and generate summary counts of modified and unmodified bases using Modkit. If there is time, you will also learn how to use Nextflow to launch pre-packaged reproducible workflows for ONT data analysis.
Introduction
Oxford Nanopore Technologies (ONT) sequencing platform is capable of detecting DNA modifications such as 5-methylcytosine (5mC), 5-hydroxymethylcytosine (5hmC), and 6-methyladenine (6mA) directly from native DNA without the need for chemical conversion or affinity purification. This is achieved by training machine learning models to recognize the altered electrical signals produced when modified bases pass through the nanopore during sequencing. In this tutorial, we will explore how to perform basecalling and modified base detection using ONT’s Dorado software, followed by quality control and visualization of the results.
Connect to Pelle
We have booked 5 CPU and 1 GPU cores on Pelle per course participant. For this tutorial, you will not be needing an interactive session. Instead you will be submitting jobs to the SLURM queue system.
Open a terminal and log in to Pelle. Note that in Pelle, you will have to login using 2-factor authentication (2FA).
ssh -Y <username>@pelle.uppmax.uu.se
Set up your working directory
Change directory to the course directory
cd /proj/uppmax2025-2-309/nobackup/ngi-epigenomics/students/
, and create your personal folder with name <your_name>.
mkdir <your_name>
Create sub folders to tidy files in your personal folder, replace <your_name> with your name in the commands below.
mkdir <your_name>/scripts #folder to store your codes
mkdir <your_name>/data #folder to store data
mkdir <your_name>/output #folder to store output files generated after running your codes
Instead of copying data files, you will generate softlinks of ONT data to your personal folder. Soft links, or symbolic links, are special files that act as shortcuts to another file or directory by storing a path to the original location.
cd data
ln -s /proj/uppmax2025-2-309/nobackup/ngi-epigenomics/data/modbase-validation_2024.10 modbase-validation_2024.10
cd ../
Copy source codes. You will need to edit your local copy of the codes later.
cp /proj/uppmax2025-2-309/nobackup/ngi-epigenomics/scripts/* scripts/.
The dataset
The ONT sample data set is derived from synthetic oligonucleotides and sequenced on a PromethION-24 device. Each data contains canonical (unmodified) or modified bases within all distinct 5-mer sequence contexts. The raw pod5 files are available from a “full” dataset and a “subset” dataset. The subset dataset was produced from the aligned full dataset by randomly selecting 5,000 reads per synthetic construct. For this workshop, we will use the subset data set to quickly reproduce results. The corresponding bam files generated with the SUP basecalling model are also available to allow you to inspect modified base calls without the need to run the basecalling step.
The data directory structure is as follows:
└── modbase-validation_2024.10 ├── basecalls │ ├── 5hmC_rep1.bam │ ├── 5hmC_rep2.bam │ ├── 5mC_rep1.bam │ ├── 5mC_rep2.bam │ ├── 6mA_rep1.bam │ ├── 6mA_rep2.bam │ ├── control_rep1.bam │ └── control_rep2.bam ├── README ├── references │ ├── all_5mers_5hmC_sites.bed │ ├── all_5mers_5mC_sites.bed │ ├── all_5mers_6mA_sites.bed │ ├── all_5mers_A_sites.bed │ ├── all_5mers_C_sites.bed │ └── all_5mers.fa └── subset ├── 5hmC_rep1.pod5 ├── 5hmC_rep2.pod5 ├── 5mC_rep1.pod5 ├── 5mC_rep2.pod5 ├── 6mA_rep1.pod5 ├── 6mA_rep2.pod5 ├── control_rep1.pod5 └── control_rep2.pod5
This tutorial uses two open source tools available on GitHub: Dorado for basecalling, including modified base calling, and Modkit for summary counts of modified and unmodified bases. Both are command-line tools from Oxford Nanopore Technologies.
Basecalling using Dorado
Load the latest version of dorado module in Pelle.
module load dorado
To see all the available options and their default values in dorado, run
dorado -h dorado <subcommand> -h dorado basecaller -h
By default, dorado basecaller will attempt to detect any adapter or primer sequences at the beginning and end of reads, and remove them from the output sequence.
Question
What is the argument when invoking
dorado basecallerif you want to skip read trimming?
We will write a bash script that will execute dorado command and submit this script to the SLURM queue system. The job submission script will include a number of SLURM directives prefixed with #SBATCH. Have a look at each of the #SBATCH directives and their meanings.
Job script
#!/bin/bash -l # Tells the shell that the script should be interpreted and executed by 'bash'
#SBATCH -A uppmax2025-2-309 # Replace with your NAISS project name
#SBATCH -p gpu # Request a GPU partition or node
#SBATCH --gres=gpu:1 # Request generic resources of 1 gpu
#SBATCH -t 24:00:00 # Set a limit of the total run time, format is days-hours:minutes:seconds
#SBATCH -J DORADO # Specifies name for the job
#SBATCH -e DORADO_%j_error.txt # output file for the bash script standard error
#SBATCH -o DORADO_%j_out.txt # output file for the bash script standard output
# load dorado - latest version
module load dorado
#
# load modkit - latest version
module load modkit
#
# location of a precompiled pycoQC binary
pycoQC="/home/louel/.conda/envs/pycoQC/bin/pycoQC"
#
# load samtools - latest version
module load SAMtools
# input raw POD5 file. CHANGE to your project folder!
inpod5="/proj/uppmax2025-2-309/nobackup/ngi-epigenomics/students/louella/data/modbase-validation_2024.10/subset/5mC_rep1.pod5"
#
# reference genome in fasta format. CHANGE to your project folder!
reffasta="/proj/uppmax2025-2-309/nobackup/ngi-epigenomics/students/louella/data/modbase-validation_2024.10/references/all_5mers.fa"
# specify the output directory to store the output files. CHANGE to your project folder!
outputdir=/proj/uppmax2025-2-309/nobackup/ngi-epigenomics/students/louella/output
#
# specify the output filename
outputbam=$(echo $inpod5 | xargs basename -s .pod5)
outputbam="hac.$outputbam"
echo "Saving output file to .... $outputbam"
echo
sleep 3s
#
#
# 1.
# run dorado basecaller command
# output is unaligned BAM file
dorado basecaller hac,5mC_5hmC $inpod5 > $outputdir/$outputbam.unaligned.bam
# run dorado basecaller command and align reads to the reference genome
# output is aligned BAM file
dorado basecaller hac,5mC_5hmC $inpod5 --reference $reffasta > $outputdir/$outputbam.bam
#
# sort bam by coordinates then index
samtools sort $outputdir/$outputbam.bam > $outputdir/tmp.$outputbam.bam
mv $outputdir/tmp.$outputbam.bam $outputdir/$outputbam.bam
samtools index $outputdir/$outputbam.bam
#
#
# 2.
# outputs read level sequencing information from the BAM file
dorado summary $outputdir/$outputbam.bam > $outputdir/$outputbam.summary.tsv
Dorado supports both CPUs and GPUs, but using GPUs is essential for practical runtime. In the script, we have requested to use one GPU core. The job should finish in a few minutes, in contrast to several hours in CPU mode.
Question
What is the maximum limit of run time that you have set in running this job?
The texts that start with # except in #SBATCH and #!/bin/bash are just comments that usually describe what a certain line of code does.
Hence, these comments will be ingored when the script is executed.
nano.Ctrl+O to save, Ctrl+X to exit.cd scripts
nano run.dorado.gpu.Pelle.sh
<your_name> in variables inpod5, reffasta and outputdir.Ctrl+O and Enter to save your changes.Note that for aligning reads to a reference sequence after basecalling, dorado uses minimap2 aligner.
Question
What is the argument when invoking dorado basecaller if you want to proceed to read alignment?
In addition, we specified in dorado basecaller that we want to use hac and 5mc_5hmC for base calling and modified basecalling models respectively. There are 3 models available namely fast, hac (high-accuracy), and sup (super-accurate). These are in order of increasing basecalling accuracy where fast is the least accurate and sup is the most accurate, and generally in increasing computing time with sup being the most computationally expensive. The Dorado developers recommend the hac model for most users as it strikes the best balance between accuracy and computational cost.
hac, it will use the latest compatible hac model.{analyte}_{pore type}_{kit chemistry}_{translocation speed}_{model type}@version, e.g.,dna_r10.4.1_e8.2_400bps_sup@v5.2.0. For more info about Dorado models, please see here.Dorado also supports modified base calling. Modified bases are modifications to one of the canonical bases (ACGT). See table below for a list of supported DNA modified bases. Modified base models can be either all-context or motif-specific. For example, given the sequence ACGTCA the 5mC all-context model will predict at all C bases i.e., aCgtCa. On the other hand, the 5mCG model will return predictions at only CG motif i.e., aCgtca. Furthermore, you can define a space separated list of modified base codes from these choices: 6mA, 5mC, 5mCG, 5mC_5hmC, 5mCG_5hmCG, 4mC_5mC.
Mod |
Name |
SAM Code |
|---|---|---|
5mC |
5-Methylcytosine |
C+m |
5hmC |
5-Hydroxymethylcytosine |
C+h |
4mC |
N(4)-methylcytosine |
C+21839 |
6mA |
6-Methyladenine |
A+a |
Table 1: DNA modifications supported in Dorado
Question
What does this command do?
dorado basecaller hac,6mA,5mCG_5hmCG file.pod5
The default output of dorado is an unaligned BAM, and if alignment is enabled then the BAM contains alignment information too. This BAM can then be used to generate a summary of the whole dataset using dorado summary command. This command outputs a tab-separated file with read level sequencing information from the BAM file.
Question
In running dorado basecaller, how would you specify that you want the output file format to be in FASTQ?
Submitting a job
After all the lengthy explanation above, you now have understood what the bash script will do and some important information and options in running dorado basecaller. Now you are ready to submit this job script.
Ctrl+X to exit nano.sbatch run.dorado.gpu.Pelle.sh
username is your UPPMAX login name.squeue -u username
JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON)
5104668 gpu DORADO username PD 0:00 1 (Priority)
Here we can see in the status column (ST) that the job is pending (PD) and has not started yet. The job is waiting for a node to become available. When the job starts, the status will change to R (running).
To cancel a job,
scancel <job id>
You can see the job id in the output from squeue.
scancel 5104668
Dorado will generate some runtime information (logging) which is written to stderr or standard error.
In the script, you will find a code line with #SBATCH -e DORADO_%j_error.txt.
This means that after your job has finished running, any generated runtime messages will be saved to a log file with filename DORADO_%j_error.txt, where %j is the job id.
To view the content of this file,
less -S DORADO_%j_error.txt
<your_name>/scripts.pwd
Change directory to your output folder.
cd ../output
List all files in the current directory with file extension .bam.
ls *.bam
Load the pre-installed latest version of samtools in Pelle.
module load SAMtools
Generate alignment summary statistics.
samtools flagstat hac.5mC_rep1.unaligned.bam
samtools flagstat hac.5mC_rep1.bam
Question
What is the mapping rate of each bam file?
Exercise:
dorado basecaller with sup model.outputbam="sup.$outputbam"Question
Did the use of the sup model increase the mapping rate of the output BAM?
pycoQC
We can use the software pycoQC to generate interactive QC plots. This tool has been developed specifically for ONT sequencing data. It requires a sequencing summary file summary.tsv that is generated by the command dorado summary.
The minimal usage is
pycoQC -f /path/to/summary.tsv -o /path/to/output.html
Exercise:
run.dorado.gpu.Pelle.sh$pycoQC -f /path/to/summary.tsv -a /path/to/input.bam -o /path/to/output.htmlDownload the html report to your laptop.
Open a terminal and change to the desired directory, i.e., cd /path/to/myfolder,
then use the scp command to transfer files.
scp <your_uppmax_username>@pelle.uppmax.uu.se:/proj/uppmax2025-2-309/nobackup/ngi-epigenomics/students/<your_name>/output/hac.5mC_rep1.html .
View the html report with a web browser.
Question
IGV
IGV is a genome browser that allows you to visualize read mapping.
You can enable a coloring scheme that is designed to create visualizations of alignments with modified bases specified with MM and ML tags in the BAM file, denoting modification type and likelihood respectively (see the Sam Tags specification). While designed for visualization of 5mC, it can be used to visualize any modification.
Exercise:
Download a BAM file and its index to your laptop.
scp <your_uppmax_username>@pelle.uppmax.uu.se:/proj/uppmax2025-2-309/nobackup/ngi-epigenomics/students/<your_name>/output/*.bam* .
Download the reference sequence FASTA file and its index to your laptop.
scp <your_uppmax_username>@pelle.uppmax.uu.se:/proj/uppmax2025-2-309/nobackup/ngi-epigenomics/students/<your_name>/data/modbase-validation_2024.10/references/*.fa* .
Genomes > Load Genome from File.File > Load from File.5mers_rand_ref_adapter_01.Color alignments by > base modification 2-color (all).
Fig. 1: IGV snapshot for chromosome ``5mers_rand_ref_adapter_01``
Question
What kind of information is shown by the coverage track?
Summarise counts using Modkit
After modified base calling is done, one can aggregate read counts for each base modification across genomic position. This can be done by executing the modkit pileup command.
Modkit will then create a table in an extended bedMethyl format that will tabulate the summary counts of modified and unmodified bases at a genomic position.
Load the latest version of modkit module in Pelle.
module load modkit
To see all the available options and their default values in modkit, run
modkit -h modkit <subcommand> -h modkit pileup -h
The basic syntax is
modkit pileup [OPTIONS] <IN_BAM> <OUT_BED>
The input <IN_BAM> is an aligned basecalled BAM generated by dorado basecaller (i.e., also called a modBAM). Note that modkit requires that the input modBAM is sorted by genomic position and indexed. To sort by position, use samtools sort <IN_BAM>. To index a BAM file, use samtools index <IN_BAM>.
The output <OUT_BED> of a modkit pileup is a bedMethyl table as described in detail here.
By default, modkit will output a BED row for all genomic positions where there is at least one base modification in the input modBAM.
Modkit uses single-letter codes for common modified bases. This code will be written out in column 4 of the bedMethyl table.
Now I will run modkit pileup on 5mC_rep1.bam. Note that modkit requires a reference sequence --ref whenever --cpg is invoked. The path /path/to/ is arbitrary, you can replace this with the correct location of the relevant file in your personal folder.
modkit pileup /path/to/5mC_rep1.bam pileup.bed --log-filepath pileup.log --ref /path/to/all_5mers.fa --cpg --threads 8
From the output of pileup.bed, I am only selecting the rows corresponding to position 5mers_rand_ref_adapter_01 86 87. Furthemore, I am only showing columns 4,6,10,11,12,13,14-18.
cat pileup.bed | awk '$1=="5mers_rand_ref_adapter_01" && $2==86' | cut -f4,6,10,11,12,13,14-
Mod base |
strand |
Nvalid_cov |
%mod |
Nmod |
Ncanonical |
Nother_mod |
Ndelete |
Nfail |
Ndiff |
Nnocall |
|---|---|---|---|---|---|---|---|---|---|---|
a |
22 |
13.64 |
3 |
19 |
0 |
13 |
82 |
4874 |
0 |
|
h |
4851 |
0.41 |
20 |
22 |
4809 |
13 |
82 |
45 |
0 |
|
m |
4851 |
99.13 |
4809 |
22 |
20 |
13 |
82 |
45 |
0 |
Table 2: Example bedMethly output generated by modkit
Question
--log-filepath for?The command above has invoked the optional argument of --cpg which will only summarise counts at CpG motifs. Generally, you can specify the sequence motif on which to tally the read counts by using --motif <Motif> <offset, 0-based>. For example, the argument ---motif GATC 1 will pileup counts for the A in the second position on the top strand and the A in the third position on the bottom strand.
The --cpg argument is shorthand for --motif CG 0, i.e., to pileup counts for the first C in the motif on the top strand and the second C (complement to G) on the bottom strand.
You can also use --combine-strands to sum the counts from both strands, and --combine-mods to combine the counts from all modified bases.
Exercise:
modkit pileup run in step 4 of the bash script run.dorado.gpu.Pelle.sh--preset traditional which is equivalent to--cpg --ref <reference.fasta> --ignore h --combine-strands.NB: The backlash \ is used to split a long command into multiple lines for better readability.
modkit pileup path/to/5mC_rep1.bam output/path/pileup.bed \
--ref path/to/reference.fasta \
--preset traditional
--log-filepath output/path/pileup.log
Question
What does --ignore h do ?
Modkit extras
Modkit has a suite of tools for processing and analyzing modified base calls in modBAM files. Here are two more functionality that you may find useful. We do not have the time to discuss them in this workshop. However, if you are interested, please visit the Modkit documentation for example usage and more details.
The modkit repair command is useful when you have a BAM with reads where the canonical sequences have been altered in some way that either renders the MM and ML tags invalid. An example case is the BAM has been trimmed or hard-clipped. The modkit repair command will fix the MM and ML tags to be consistent with the canonical sequence in the BAM file.
You can use the command modkit dmr to perform differential methylation analysis between two haplotypes (allele-specific change) or between two groups.
Haplotype-level DMR analysis focuses on individual haplotypes, e.g., maternal vs paternal haplotypes.
Population-scale DMR analysis takes population groups (e.g., case/control cohorts) and identifies methylation differences between entire groups.
Alternative workflows
We present here alternative workflows built using Nextflow that are pre-packaged and reproducible pipelines to analyze Oxford Nanopore sequencing data. These workflows automate complex tasks like alignment, count pileup, and finding DMRs.
For a tutorial about the basics of Nextflows and launching of nf-core pipelines in HPC, please see here.
EPI2ME wf-basecalling
EPI2ME Labs maintains a collection of Nextflow bioinformatics workflows tailored to ONT data. Here we only present the workflow for basecalling. For a full description of this workflow, please visit here.
dorado basecallerChange directory to your personal folder.
cd /proj/uppmax2025-2-309/nobackup/ngi-epigenomics/students/<your_name>
A demo dataset is provided by EPI2ME for testing the workflow. The POD5 dataset is a subset of the January 2025 GIAB HG002 Dataset used for testing the EPI2ME wf-basecalling pipeline.
cd data
ln -s /proj/uppmax2025-2-309/nobackup/ngi-epigenomics/data/wf-basecalling-demo wf-basecalling-demo
cd ../
Edit your local copy of the script run.epi2me.basecall.sh.
cd script
nano run.epi2me.basecall.sh
Job script
#!/bin/bash -l
#SBATCH -A uppmax2025-2-309 # Replace with your NAISS project name
#SBATCH -p gpu # Request a GPU partition or node
#SBATCH --gres=gpu:1 # Request generic resources of 1 gpu
#SBATCH --ntasks 16 # Request this number of threads
#SBATCH -t 12:00:00 # Set a limit of the total run time
#SBATCH -J EPI2ME # Specifies name for the job
#SBATCH -e EPI2ME_%j_error.txt # output file for the bash script standard error
#SBATCH -o EPI2ME_%j_out.txt # output file for the bash script standard output
# Load the preinstalled latest Nextflow
module load Nextflow
# set your personal directory
mydir="/proj/uppmax2025-2-309/nobackup/ngi-epigenomics/students/louella"
# Set Apptainer/Singularity environment variables to define caching and tmp
# directories. These are used during the conversion of Docker images to
# Apptainer/Singularity ones.
# These lines can be omitted if the variables are already set in your `~/.bashrc` file.
#
# create directory
mkdir -p $mydir/apptainer/cache
mkdir -p $mydir/apptainer/tmp
#
export APPTAINER_CACHEDIR="$mydir/apptainer/cache"
export APPTAINER_TMPDIR="$mydir/apptainer/tmp"
# output folder
OUTPUT="$mydir/output/TEST_EPI2ME"
# create directory
mkdir -p $OUTPUT
# directory containing all pod5 files
inpod5="$mydir/data/wf-basecalling-demo/input_1"
# reference sequence FASTA file
reffasta="$mydir/data/wf-basecalling-demo/GCA_000001405.15_GRCh38_no_alt_analysis_set.fasta"
# launch nextflow process
nextflow run epi2me-labs/wf-basecalling \
--basecaller_cfg 'dna_r10.4.1_e8.2_400bps_hac@v5.2.0' \ # name of the model for basecalling
--remora_cfg 'dna_r10.4.1_e8.2_400bps_hac@v5.2.0_5mCG_5hmCG@v1' \ # name of the model for modified basecalling
--dorado_ext 'pod5' \ # file extension for Dorado inputs
--input $inpod5 \
--ref $reffasta \
-profile singularity \
--out_dir $OUTPUT
louella with <your_name> in variable mydir.Ctrl+O and Enter to save your changes.Ctrl+X to exit nano.To submit the job, type the command below in the terminal.
sbatch run.epi2me.basecall.sh
username is your UPPMAX login name.squeue -u username
The example run of EPI2ME wf-basecalling in the job script will generate CRAM files that can be found in your output directory $OUTPUT.
Output files generated
├── execution
│ ├── report.html
│ ├── timeline.html
│ └── trace.txt
├── SAMPLE.fail.cram
├── SAMPLE.fail.cram.crai
├── SAMPLE.pass.cram
├── SAMPLE.pass.cram.crai
└── wf-basecalling-report.html
nf-core methylong
The nf-core/methylong is an end-to-end comprehensive pipeline that is tailored for long-read methylation calling. The user has an option to either run the ONT or the PacBio HiFi workflows. Here we only present the ONT workflow. Full documentation is here.
Note that we will be using the version dev, which is still under active development and has not been deployed for production release. Hence, it is not uncommon to encounter bugs when running this pipeline version. We are already testing the dev version as it has more functionality than the latest release 1.0.0.
The ONT workflow can be used to perform:
(Modified) basecalling using
dorado basecallertrim (
porechop) and repair tags (modkit repair) of input modBAM- align to reference using
minimap2 optional: If input is an aligned modBAM then use argument
--resetto remove previous alignment information before running read alignment.
- align to reference using
create bedMethyl table using
modkit pileupcreate bedgraphs for visualisation (optional)
SNV calling using
clair3SNV phasing using
whatshap phase- DMR analysis using
DSS(default) ormodkit dmr includes DMR haplotype level and population scale
- DMR analysis using
You can use this premade sample sheet samplesheet.dev.csv in your \script folder which uses some POD5 files from the dataset modbase-validation_2024.10.
Change directory to your personal folder.
cd /proj/uppmax2025-2-309/nobackup/ngi-epigenomics/students/<your_name>
Edit your local copy of the script run.nfcore.methylong.Pelle.sh.
cd script
nano run.nfcore.methylong.Pelle.sh
Job script
#!/bin/bash -l
#SBATCH -A uppmax2025-2-309 # Replace with your NAISS project name
#SBATCH -p gpu # Request a GPU partition or node
#SBATCH --gres=gpu:1 # Request generic resources of 1 gpu
#SBATCH --ntasks 16 # Request this number of threads
#SBATCH -t 48:00:00 # Set a limit of the total run time
#SBATCH -J methylong # Specifies name for the job
#SBATCH -e methylong_%j_error.txt # output file for the bash script standard error
#SBATCH -o methylong_%j_out.txt # output file for the bash script standard output
# Load the preinstalled latest Nextflow
module load Nextflow
# set your personal directory
mydir="/proj/uppmax2025-2-309/nobackup/ngi-epigenomics/students/louella"
# Set Apptainer/Singularity environment variables to define caching and tmp
# directories. These are used during the conversion of Docker images to
# Apptainer/Singularity ones.
# These lines can be omitted if the variables are already set in your `~/.bashrc` file.
#
# create directory
mkdir -p $mydir/apptainer/cache
mkdir -p $mydir/apptainer/tmp
#
export APPTAINER_CACHEDIR="$mydir/apptainer/cache"
export APPTAINER_TMPDIR="$mydir/apptainer/tmp"
# output folder
OUTPUT="$mydir/output/TEST_METHYLONG"
# create directory
mkdir -p $OUTPUT
# launch nextflow process
nextflow run nf-core/methylong -r dev \ # specify to use the version dev
-profile uppmax \ # use a config that is pre-configured in nf-core with a setup suitable for the UPPMAX clusters
--input samplesheet.dev.csv \
--outdir $OUTPUT \
--project uppmax2025-2-309 \
--bedgraph \ # generate output bedgraph for visualisation
--skip_snvs \ # skip snvcall and phasing
--dmr_population_scale \ # perform DMR analysis for population scale
--population_dmrer modkit \ # use modkit for DMR analysis
--dmr_a 5mc --dmr_b ctrl \ # define the group of DMR analysis in population scale
--dorado_model hac \
--dorado_modification 5mCG_5hmCG \
-c local.config # use a user defined configuration setting to change default parameters of the pipeline
louella with <your_name> in variable mydir.Ctrl+O and Enter to save your changes.Ctrl+X to exit nano.Have a look at the provided sample sheet by using this command.
less -S samplesheet.dev.csv
To submit the job, type the command below in the terminal.
sbatch run.nfcore.methylong.Pelle.sh
username is your UPPMAX login name.squeue -u username
The example run of nf-core methylong in the job script will take around 2.5 hours to finish.
It will generate a large number of files stored in the output directory $OUTPUT.
Output files generated
├── multiqc
│ ├── multiqc_data
│ │ ├── multiqc_citations.txt
│ │ ├── multiqc_data.json
│ │ ├── multiqc_general_stats.txt
│ │ ├── multiqc.log
│ │ ├── multiqc_samtools_flagstat.txt
│ │ ├── multiqc_software_versions.txt
│ │ ├── multiqc_sources.txt
│ │ └── samtools-flagstat-dp.txt
│ ├── multiqc_plots
│ │ ├── pdf
│ │ │ ├── general_stats_table.pdf
│ │ │ └── samtools-flagstat-dp.pdf
│ │ ├── png
│ │ │ ├── general_stats_table.png
│ │ │ └── samtools-flagstat-dp.png
│ │ └── svg
│ │ ├── general_stats_table.svg
│ │ └── samtools-flagstat-dp.svg
│ └── multiqc_report.html
├── ont
│ ├── 5mc&ctrl
│ │ └── dmr_population_scale
│ │ └── modkit
│ │ └── 5mc&ctrl_modkit_dmr_population_scale.bed
│ ├── 5mc-rep1
│ │ ├── alignment
│ │ │ ├── 5mc-rep1
│ │ │ │ ├── 5mc-rep1_repaired.bam
│ │ │ │ └── 5mc-rep1_repaired.bam.bai
│ │ │ └── flagstat
│ │ │ └── 5mc-rep1_ont.flagstat
│ │ ├── basecall
│ │ │ └── 5mc-rep1_calls.bam
│ │ ├── bedgraphs
│ │ │ ├── 5mc-rep1_A_negative.bedgraph.gz
│ │ │ ├── 5mc-rep1_A_positive.bedgraph.gz
│ │ │ ├── 5mc-rep1_CG_negative.bedgraph.gz
│ │ │ ├── 5mc-rep1_CG_positive.bedgraph.gz
│ │ │ ├── 5mc-rep1_CHG_negative.bedgraph.gz
│ │ │ ├── 5mc-rep1_CHG_positive.bedgraph.gz
│ │ │ ├── 5mc-rep1_CHH_negative.bedgraph.gz
│ │ │ └── 5mc-rep1_CHH_positive.bedgraph.gz
│ │ ├── dmr_population_scale
│ │ │ └── modkit
│ │ │ └── 5mc-rep1.bed
│ │ ├── fastqc
│ │ │ ├── 5mc-rep1_ont_fastqc.html
│ │ │ └── 5mc-rep1_ont_fastqc.zip
│ │ ├── pileup
│ │ │ ├── 5mc-rep1.bed.gz
│ │ │ └── 5mc-rep1_pileup.log
│ │ ├── repair
│ │ │ ├── 5mc-rep1_repaired.bam
│ │ │ └── 5mc-rep1_repair.log
│ │ └── trim
│ │ ├── 5mc-rep1.fastq.gz
│ │ └── 5mc-rep1.log
│ ├── 5mc-rep2
│ │ ├── alignment
│ │ │ ├── 5mc-rep2
│ │ │ │ ├── 5mc-rep2_repaired.bam
│ │ │ │ └── 5mc-rep2_repaired.bam.bai
│ │ │ └── flagstat
│ │ │ └── 5mc-rep2_ont.flagstat
│ │ ├── basecall
│ │ │ └── 5mc-rep2_calls.bam
│ │ ├── bedgraphs
│ │ │ ├── 5mc-rep2_A_negative.bedgraph.gz
│ │ │ ├── 5mc-rep2_A_positive.bedgraph.gz
│ │ │ ├── 5mc-rep2_CG_negative.bedgraph.gz
│ │ │ ├── 5mc-rep2_CG_positive.bedgraph.gz
│ │ │ ├── 5mc-rep2_CHG_negative.bedgraph.gz
│ │ │ ├── 5mc-rep2_CHG_positive.bedgraph.gz
│ │ │ ├── 5mc-rep2_CHH_negative.bedgraph.gz
│ │ │ └── 5mc-rep2_CHH_positive.bedgraph.gz
│ │ ├── dmr_population_scale
│ │ │ └── modkit
│ │ │ └── 5mc-rep2.bed
│ │ ├── fastqc
│ │ │ ├── 5mc-rep2_ont_fastqc.html
│ │ │ └── 5mc-rep2_ont_fastqc.zip
│ │ ├── pileup
│ │ │ ├── 5mc-rep2.bed.gz
│ │ │ └── 5mc-rep2_pileup.log
│ │ ├── repair
│ │ │ ├── 5mc-rep2_repaired.bam
│ │ │ └── 5mc-rep2_repair.log
│ │ └── trim
│ │ ├── 5mc-rep2.fastq.gz
│ │ └── 5mc-rep2.log
│ ├── Ctl-rep1
│ │ ├── alignment
│ │ │ ├── Ctl-rep1
│ │ │ │ ├── Ctl-rep1_repaired.bam
│ │ │ │ └── Ctl-rep1_repaired.bam.bai
│ │ │ └── flagstat
│ │ │ └── Ctl-rep1_ont.flagstat
│ │ ├── basecall
│ │ │ └── Ctl-rep1_calls.bam
│ │ ├── bedgraphs
│ │ │ ├── Ctl-rep1_A_negative.bedgraph.gz
│ │ │ ├── Ctl-rep1_A_positive.bedgraph.gz
│ │ │ ├── Ctl-rep1_CG_negative.bedgraph.gz
│ │ │ ├── Ctl-rep1_CG_positive.bedgraph.gz
│ │ │ ├── Ctl-rep1_CHG_negative.bedgraph.gz
│ │ │ ├── Ctl-rep1_CHG_positive.bedgraph.gz
│ │ │ ├── Ctl-rep1_CHH_negative.bedgraph.gz
│ │ │ └── Ctl-rep1_CHH_positive.bedgraph.gz
│ │ ├── dmr_population_scale
│ │ │ └── modkit
│ │ │ └── Ctl-rep1.bed
│ │ ├── fastqc
│ │ │ ├── Ctl-rep1_ont_fastqc.html
│ │ │ └── Ctl-rep1_ont_fastqc.zip
│ │ ├── pileup
│ │ │ ├── Ctl-rep1.bed.gz
│ │ │ └── Ctl-rep1_pileup.log
│ │ ├── repair
│ │ │ ├── Ctl-rep1_repaired.bam
│ │ │ └── Ctl-rep1_repair.log
│ │ └── trim
│ │ ├── Ctl-rep1.fastq.gz
│ │ └── Ctl-rep1.log
│ └── Ctl-rep2
│ ├── alignment
│ │ ├── Ctl-rep2
│ │ │ ├── Ctl-rep2_repaired.bam
│ │ │ └── Ctl-rep2_repaired.bam.bai
│ │ └── flagstat
│ │ └── Ctl-rep2_ont.flagstat
│ ├── basecall
│ │ └── Ctl-rep2_calls.bam
│ ├── bedgraphs
│ │ ├── Ctl-rep2_A_negative.bedgraph.gz
│ │ ├── Ctl-rep2_A_positive.bedgraph.gz
│ │ ├── Ctl-rep2_CG_negative.bedgraph.gz
│ │ ├── Ctl-rep2_CG_positive.bedgraph.gz
│ │ ├── Ctl-rep2_CHG_negative.bedgraph.gz
│ │ ├── Ctl-rep2_CHG_positive.bedgraph.gz
│ │ ├── Ctl-rep2_CHH_negative.bedgraph.gz
│ │ └── Ctl-rep2_CHH_positive.bedgraph.gz
│ ├── dmr_population_scale
│ │ └── modkit
│ │ └── Ctl-rep2.bed
│ ├── fastqc
│ │ ├── Ctl-rep2_ont_fastqc.html
│ │ └── Ctl-rep2_ont_fastqc.zip
│ ├── pileup
│ │ ├── Ctl-rep2.bed.gz
│ │ └── Ctl-rep2_pileup.log
│ ├── repair
│ │ ├── Ctl-rep2_repaired.bam
│ │ └── Ctl-rep2_repair.log
│ └── trim
│ ├── Ctl-rep2.fastq.gz
│ └── Ctl-rep2.log