We will use a small subset of the original data. If we would do the computation on the orginal data the computation time for a tutorial is too long. To show you all necessary steps for Methyl-Seq we decided to use a subset of the data set. In a second step we use precomputed data from the study to show you different levels of methylation. We will consider samples from normal breast cells (NB), fibroadenoma (noncancerous breast tumor, BT089), two invasive ductal carcinomas (BT126, BT198) and a breast adenocarcinoma cell line (MCF7).

This tutorial is based off of Lin et al. 2015. The data we use in this tutorial is available at Zenodo.

Agenda

In this tutorial, we will deal with:

1. Data upload
2. Quality Control
3. Alignment
4. Methylation bias and metric extraction
5. Visualization
6. Metilene

Data upload

We will start by loading the example dataset which will be used for the tutorial into Galaxy

Hands On: Get the data into Galaxy

1. Create a new history

   Tip: Creating a new history

   To create a new history simply click the new-history icon at the top of the history panel:

   

2. Import the two example datasets from Zenodo or the shared data library:

    Copy

   https://zenodo.org/record/557099/files/subset_1.fastq
   https://zenodo.org/record/557099/files/subset_2.fastq
   

   Tip: Importing via links

   • Copy the link location

   • Click galaxy-upload Upload Data at the top of the tool panel

   • Select galaxy-wf-edit Paste/Fetch Data

   • Paste the link(s) into the text field

   • Press Start

   • Close the window

   Tip: Importing data from a data library

   As an alternative to uploading the data from a URL or your computer, the files may also have been made available from a shared data library:

   1. Go into Libraries (left panel)
   2. Navigate to the correct folder as indicated by your instructor.
      • On most Galaxies tutorial data will be provided in a folder named GTN - Material –> Topic Name -> Tutorial Name.
   3. Select the desired files
   4. Click on Add to History galaxy-dropdown near the top and select as Datasets from the dropdown menu

   5. In the pop-up window, choose

      • “Select history”: the history you want to import the data to (or create a new one)
   6. Click on Import

Link to here | FAQs | Gitter Chat | Help Forum

Quality Control

The first step in any analysis should always be quality control. We will use the Falco tool to asses the quality of our reads and determine if we need to perform any data cleaning before proceeding with our analysis. Falco is an efficiency optimized rewrite of FastQC

Hands On: Quality Control

1. Falco ( Galaxy version 1.2.4+galaxy0) with the following parameters:
   • param-files “Raw read data from your current history”: subset_1.fastq.gz and subset_2.fastq.gz

   Tip: Select multiple datasets

   1. Click on param-files Multiple datasets
   2. Select several files by keeping the Ctrl (or COMMAND) key pressed and clicking on the files of interest

2. Go to the web page result page and have a closer look at ‘Per base sequence content’

   

   Question

   1. Note the GC distribution and percentage of “T” and “C”. Why is this so weird?
   2. Is everything as expected?

   Solution

   1. The attentive audience of the theory part knows: Every C-meth stays a C and every normal C becomes a T during the bisulfite conversion.
   2. Yes it is. Always be careful and have the specific characteristics of your data in mind during the interpretation of Falco results.

Link to here | FAQs | Gitter Chat | Help Forum

Alignment

Hands On: Mapping with bwameth

We will now map the imported dataset against a reference genome.

1. bwameth ( Galaxy version 0.2.7+galaxy0) with the following parameters:
   • “Select a genome reference from your history or a built-in index?”: Use a built-in index
     • “Select a reference genome”: Human (hg38full)
   • “Is this library mate-paired”: Paired-end
     • “First read in pair”: subset_1.fastq
     • “Second read in pair”: subset_2.fastq

   Comment: Long compute times

   Please notice that mapping can take some time. If you want to skip this, we provide for you a precomputed alignment. Import https://zenodo.org/records/557099/files/aligned_subset.bam to your history.

   Question

   Why we need other alignment tools for bisulfite sequencing data?

   Solution

   You may have noticed that all the C’s are C-meth’s and a T can be a T or a C. A mapper for methylation data needs to find out what is what.

Link to here | FAQs | Gitter Chat | Help Forum

Methylation bias and metric extraction

Hands On: Methylation bias

In this step we will have a look at the distribution of the methylation and will look at a possible bias.

1. MethylDackel ( Galaxy version 0.5.2+galaxy0) with the following parameters:
   • “Load reference genome from”: Local cache
     • “Using reference genome”: Human (hg38)
   • “Sorted BAM file”: output of bwameth tool
   • “What do you want to do?”: Determine the position-dependent methylation bias in the dataset, producing diagnostic SVG images (mbias)
   • In “Advanced options”
     • “Keep singletons”: param-toggle Yes
     • “Keep discordant alignmetns”: param-toggle Yes

   

   Question

   1. Consider the original top strand output. Is there a methylation bias?
   2. If we would trim, what would be the start and the end positions?

   Solution

   1. The distribution of the methylation is more or less equal. Only at the start and the end we could trim a bit but a +- 5% variation is acceptable.
   2. To trim the reads we would include for the first strand only the positions 0 to 145, for the second 6 to 149.

Link to here | FAQs | Gitter Chat | Help Forum

Hands On: Methylation extraction with MethylDackel

We will extract the methylation on the resulting BAM file of the alignment step. We need this to create a methylation level plot in the next step.

1. MethylDackel ( Galaxy version 0.5.2+galaxy0) with the following parameters:
   • “Load reference genome from”: Local cache
     • “Using reference genome”: Human (hg38)
   • “Sorted BAM file”: output of bwameth tool
   • “What do you want to do?”: Extract methylation metrics from an alignment file in BAM/CRAM format (extract)
   • “Merge per-Cytosine metrics”: param-toggle Yes
   • “Output options”: CpG methylation fractions (--fraction)

Link to here | FAQs | Gitter Chat | Help Forum

Visualization

Hands On

In this step we want to visualize the methylation level around all TSS of our data. When located at gene promoters, DNA methylation is usually a repressive mark.

1. Wig/BedGraph-to-bigWig with the following parameters:

   • “Convert”: fraction CpG (result of MethylDackel tool)

     Tip: Database edit

     It can happen that you can not select the correct input file. In this case you have to add meta information about the used genome to the file.

     • Click on the pencil of the correct history item.
     • Change Database/Build: to the genome you used.
     • In our case the correct genome is Human Dec. 2013 (GRCh38/hg38) (hg38).

2. Import the BED file with CpG islands from Zenodo into the history

    Copy

   https://zenodo.org/records/557099/files/CpGIslands.bed
   

3. computeMatrix ( Galaxy version 3.5.4+galaxy0) with the following parameters:
   • “Regions to plot”: CpGIslands.bed
   • “Sample order matters”: No
   • “Score file”: Output of Wig/BedGraph-to-bigWig tool
   • “computeMatrix has two main output options”: reference-point
4. plotProfile ( Galaxy version 3.5.4+galaxy0) with the following parameters:
   • “Matrix file from the computeMatrix tool”: Matrix (output of computeMatrix tool)

The output should look like this:

Lets see how the methylation looks for a few provided files:

1. Import the BED file with CpG islands from Zenodo into the history

    Copy

   https://zenodo.org/records/557099/files/NB1_CpG.meth.bedGraph
   

2. Wig/BedGraph-to-bigWig with the following parameters:
   • “Convert”: NB1_CpG.meth.bedGraph

   Question

   The execution fails. Do you have an idea why?

   Solution

   A conversion to bigWig would fail right now. If it turned green, the file size should be 0 bytes. Probably dataset info box shows some error message like hashMustFindVal: '1' not found. The reason is the source of the reference genome which was used. There is ensembl and UCSC as sources which differ in naming the chromosomes. Ensembl is using just numbers e.g. 1 for chromosome one. UCSC is using chr1 for the same. Be careful with this especially if you have data from different sources. We need to convert this.

   Comment: UCSC - Ensembl convert

   Download the file containing mapping between Ensembl and UCS chromosome convention of hg38

    Copy

   https://raw.githubusercontent.com/dpryan79/ChromosomeMappings/master/GRCh38_ensembl2UCSC.txt
   

   Replace column ( Galaxy version 0.2) with the follwing parameters:

   • “File in which you want to replace some values”: NB1_CpG.meth.bedGraph
   • “Replace information file”: GRCh38_ensembl2UCSC.txt
   • “Which column should be replaced?”: Column: 1
   • “Skip this many starting lines”: 1
   • “Delimited by”: Tab

3. To save compute time we prepared the converted files for you. Import the following files. Create a collection list and label it all_coverage_files. Strip the file extension from the name. For example, rename from NB1_CpG.meth_ucsc.bedGraphto NB1_CpG.

    Copy

   https://zenodo.org/records/557099/files/NB1_CpG.meth_ucsc.bedGraph
   https://zenodo.org/records/557099/files/NB2_CpG.meth_ucsc.bedGraph
   https://zenodo.org/records/557099/files/BT089_CpG.meth_ucsc.bedGraph
   https://zenodo.org/records/557099/files/BT126_CpG.meth_ucsc.bedGraph
   https://zenodo.org/records/557099/files/BT198_CpG.meth_ucsc.bedGraph
   https://zenodo.org/records/557099/files/MCF7_CpG.meth_ucsc.bedgraph
   

   Tip: Creating a dataset collection

   • Click on galaxy-selector Select Items at the top of the history panel
   • Check all the datasets in your history you would like to include

   • Click n of N selected and choose Build Dataset List

     

   • Enter a name for your collection
   • Click Create list to build your collection
   • Click on the checkmark icon at the top of your history again

4. Change the datatype to bedgraph and set the database to hg38

   Tip: Changing the datatype

   • Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
   • In the central panel, click galaxy-chart-select-data Datatypes tab on the top
   • In the galaxy-chart-select-data Assign Datatype, select bedgraph from “New Type” dropdown
     • Tip: you can start typing the datatype into the field to filter the dropdown menu
   • Click the Save button

   Tip: Changing database/build (dbkey)

   • Click the desired dataset’s name to expand it.

   • Click on the “?” next to database indicator:

     

   • In the central panel, change the Database/Build field
   • Select your desired database key from the dropdown list: hg38
   • Click the Save button

5. Wig/BedGraph-to-bigWig with the following parameters:
   • param-collection “Convert”: all_coverage_files
6. computeMatrix ( Galaxy version 3.5.4+galaxy0) with the following parameters:
   • “Regions to plot”: CpGIslands.bed
   • “Sample order matters”: No
   • “Score file”: Output of previous Wig/BedGraph-to-bigWig tool
   • “computeMatrix has two main output options”: reference-point
7. plotProfile ( Galaxy version 3.5.4+galaxy0) with the following parameters:
   • “Matrix file from the computeMatrix tool”: Matrix (output of previous computeMatrix tool)
   • “Show advanced options”: Yes
     • “Make one plot per group of regions”: param-toggle Yes

The output should look like this:

Link to here | FAQs | Gitter Chat | Help Forum

Metilene

Hands On: Metilene

With metilene it is possible to detect differentially methylated regions (DMRs) which is a necessary prerequisite for characterizing different epigenetic states.

1. Import the following files from Zenodo into yout history

    Copy

   https://zenodo.org/records/557099/files/NB1_CpG.meth.bedGraph
   https://zenodo.org/records/557099/files/NB2_CpG.meth.bedGraph
   https://zenodo.org/records/557099/files/BT198_CpG.meth.bedGraph
   

2. Metilene ( Galaxy version 0.2.6.1) with the following parameters:

   • “Input group 1”: NB1_CpG.meth.bedGraph and NB2_CpG.meth.bedGraph
   • “Input group 2”: BT198_CpG.meth.bedGraph
   • “BED file containing regions of interest”: CpGIslands.bed

   Question

   Have a look at the produced pdf document. What is the data showing?

   Solution

   It shows the distribution of DMR differences, DMR length in nucleotides and number CpGs, DMR differences vs. q-values, mean methylation group 1 vs. mean methylation group 2 and DMR length in nucleotides vs. length in CpGs

Link to here | FAQs | Gitter Chat | Help Forum

You've Finished the Tutorial

Please also consider filling out the Feedback Form as well!

Key points

• The output of a methylation NGS is having a different distribution of the four bases. This is caused by the bisulfite treatment of the DNA.

• If there is a different level of methylation in the loci of a gene this can be a hint that something is wrong.

• To get useful results you need – data, data and data!

Frequently Asked Questions

• Tutorial FAQs
• Topic FAQs
• Galaxy FAQs
• GTN Matrix Chat
• Galaxy Help Forum

References

1. Lin, I.-H., D.-T. Chen, Y.-F. Chang, Y.-L. Lee, C.-H. Su et al., 2015 Hierarchical Clustering of Breast Cancer Methylomes Revealed Differentially Methylated and Expressed Breast Cancer Genes (O. El-Maarri, Ed.). PLOS ONE 10: e0118453. 10.1371/journal.pone.0118453

Feedback

Did you use this material as an instructor? Feel free to give us feedback on how it went.
Did you use this material as a learner or student? Click the form below to leave feedback.

Citing this Tutorial

1. Joachim Wolff, Devon Ryan, Verena Moosmann, DNA Methylation data analysis (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/epigenetics/tutorials/methylation-seq/tutorial.html Online; accessed Sat Jun 21 2025
2. Hiltemann, Saskia, Rasche, Helena et al., 2023 Galaxy Training: A Powerful Framework for Teaching! PLOS Computational Biology 10.1371/journal.pcbi.1010752
3. Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012

BibTeX

 Copy

@misc{epigenetics-methylation-seq,
author = "Joachim Wolff and Devon Ryan and Verena Moosmann",
	title = "DNA Methylation data analysis (Galaxy Training Materials)",
	year = "",
	month = "",
	day = "",
	url = "\url{https://training.galaxyproject.org/training-material/topics/epigenetics/tutorials/methylation-seq/tutorial.html}",
	note = "[Online; accessed Sat Jun 21 2025]"
}
@article{Hiltemann_2023,
	doi = {10.1371/journal.pcbi.1010752},
	url = {https://doi.org/10.1371%2Fjournal.pcbi.1010752},
	year = 2023,
	month = {jan},
	publisher = {Public Library of Science ({PLoS})},
	volume = {19},
	number = {1},
	pages = {e1010752},
	author = {Saskia Hiltemann and Helena Rasche and Simon Gladman and Hans-Rudolf Hotz and Delphine Larivi{\`{e}}re and Daniel Blankenberg and Pratik D. Jagtap and Thomas Wollmann and Anthony Bretaudeau and Nadia Gou{\'{e}} and Timothy J. Griffin and Coline Royaux and Yvan Le Bras and Subina Mehta and Anna Syme and Frederik Coppens and Bert Droesbeke and Nicola Soranzo and Wendi Bacon and Fotis Psomopoulos and Crist{\'{o}}bal Gallardo-Alba and John Davis and Melanie Christine Föll and Matthias Fahrner and Maria A. Doyle and Beatriz Serrano-Solano and Anne Claire Fouilloux and Peter van Heusden and Wolfgang Maier and Dave Clements and Florian Heyl and Björn Grüning and B{\'{e}}r{\'{e}}nice Batut and},
	editor = {Francis Ouellette},
	title = {Galaxy Training: A powerful framework for teaching!},
	journal = {PLoS Comput Biol}
}

                   

Galaxy Administrators: Install the missing tools

You can use Ephemeris's shed-tools install command to install the tools used in this tutorial.

 Copy

shed-tools install [-g GALAXY] [-a API_KEY] -t <(curl https://training.galaxyproject.org/training-material/api/topics/epigenetics/tutorials/methylation-seq/tutorial.json | jq .admin_install_yaml -r)

Alternatively you can copy and paste the following YAML

 Copy

---
install_tool_dependencies: true
install_repository_dependencies: true
install_resolver_dependencies: true
tools:
- name: deeptools_compute_matrix
  owner: bgruening
  revisions: fd1275e01605
  tool_panel_section_label: deepTools
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: deeptools_compute_matrix
  owner: bgruening
  revisions: a60c359ec43c
  tool_panel_section_label: deepTools
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: deeptools_plot_profile
  owner: bgruening
  revisions: aac8444d6681
  tool_panel_section_label: deepTools
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: deeptools_plot_profile
  owner: bgruening
  revisions: 6662b1a8d326
  tool_panel_section_label: deepTools
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: pileometh
  owner: bgruening
  revisions: 906db57d5d65
  tool_panel_section_label: Peak Calling
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: pileometh
  owner: bgruening
  revisions: d6787bab7b11
  tool_panel_section_label: Peak Calling
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: replace_column_by_key_value_file
  owner: bgruening
  revisions: cc18bac5afdb
  tool_panel_section_label: Text Manipulation
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: replace_column_by_key_value_file
  owner: bgruening
  revisions: d533e4b75800
  tool_panel_section_label: Text Manipulation
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: text_processing
  owner: bgruening
  revisions: e39fceb6ab85
  tool_panel_section_label: Text Manipulation
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: text_processing
  owner: bgruening
  revisions: d698c222f354
  tool_panel_section_label: Text Manipulation
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: fastqc
  owner: devteam
  revisions: 484e86282f4b
  tool_panel_section_label: FASTA/FASTQ
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: bwameth
  owner: iuc
  revisions: a6ea26c1f225
  tool_panel_section_label: Mapping
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: bwameth
  owner: iuc
  revisions: cf1322aeb137
  tool_panel_section_label: Mapping
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: falco
  owner: iuc
  revisions: 959a14c1f2dd
  tool_panel_section_label: FASTA/FASTQ
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: metilene
  owner: rnateam
  revisions: 37abd09a0ae6
  tool_panel_section_label: Epigenetics
  tool_shed_url: https://toolshed.g2.bx.psu.edu/

Feedback

t{ hist[0] | to_stars }}	7
t{ hist[0] | to_stars }}	2
t{ hist[0] | to_stars }}	1
t{ hist[0] | to_stars }}	3

October 2024

• 3 stars: Liked: option along with version numbres help
• 4 stars: Disliked: more example

April 2024

• 4 stars: Disliked: Actually, I encountered an error while transforming the MethylDackel file to a bedgraph file. The error message was: "unrecognized line 1 of trackless: chr1 5190715 5190717 100 1 0". After that, I wasn’t able to replicate the subsequent steps. I suggest providing clearer instructions for each step. Additionally, I would like to know more about how to group the data for analysis. This part feels a bit too concise. Overall, this material has been helpful for me to apply it to my own data, although I may need to explore additional tools&Analysis on my own. Thank you! And I was wondering if it is possible to build a comprehensive analysis workflow for Methyl-BS-Seq data in Galaxy similar to the cross-package Bioconductor workflow for analyzing methylation array data (published in F1000Research). :)

June 2022

• 1 stars: Liked: nothing Disliked: everything

January 2022

• 1 stars: Disliked: Data set can not be found

November 2021

• 1 stars: Liked: The programs were not available at Galaxy

September 2021

• 5 stars: Disliked: It would be great if this can be updated based on the newest version of the packages used in the tutorial. Some of the parameters cannot be set as shown in this tutorial or should be set in a different way.
• 5 stars: Disliked: Instructions about finding tutorial data in a Data Library need to be updated. In this tutorial and probably others. Tutorial data is now nested at usegalaxy.* servers -- and that change is confusing some learners. I've been telling people to search data libraries with the keyword "GTN" then to navigate down through the topic to the specific tutorial. Not all public servers will host the data in Data Libs, so even that advice needs a tune up to be consistent/accurate across tutorials. (@jennaj)

March 2020

• 5 stars: Liked: The degree of detail. I loved that even the parameters for the different tools were explained. I don't know if this is comparable to a real experiment analysis, but it surely felt as that. Congrats! Disliked: The Genrich tool is only available at the GalaxyEurope server. It would be nice that a warning would be given at the beginning of the tutorial, so that the whole analysis could be done there and avoid migrating data between Galaxy servers.

February 2020

• 5 stars: Liked: The tutorial is self explanatory and very easy to follow for individual hands on

September 2019

• 5 stars: Liked: The good explanations during hands-on training Disliked: Maybe too different analysis for one day (HiC and epigentics), maybe a longer session for each would help understanding