DNA Methylation data analysis
OverviewQuestions:
Objectives:
What is methylation and why it cannot be recognised by a normal NGS procedure?
Can a different methylation influence the expression of a gene? How?
Which tools you can use to analyse methylation data?
Requirements:
Learn how to analyse methylation data
Get a first intuition what are common pitfalls.
- Introduction to Galaxy Analyses
- slides Slides: Quality Control
- tutorial Hands-on: Quality Control
- slides Slides: Mapping
- tutorial Hands-on: Mapping
Time estimation: 3 hoursSupporting Materials:Published: Feb 16, 2017Last modification: Jun 25, 2024License: Tutorial Content is licensed under Creative Commons Attribution 4.0 International License. The GTN Framework is licensed under MITpurl PURL: https://gxy.io/GTN:T00142rating Rating: 4.0 (1 recent ratings, 11 all time)version Revision: 23
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.
AgendaIn this tutorial, we will deal with:
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
Create a new history
To create a new history simply click the new-history icon at the top of the history panel:
Import the two example datasets from Zenodo or the shared data library:
https://zenodo.org/record/557099/files/subset_1.fastq https://zenodo.org/record/557099/files/subset_2.fastq
- 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
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:
- Go into Data (top panel) then Data libraries
- 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.
- Select the desired files
- Click on Add to History galaxy-dropdown near the top and select as Datasets from the dropdown menu
In the pop-up window, choose
- “Select history”: the history you want to import the data to (or create a new one)
- Click on Import
Quality Control
The first step in any analysis should always be quality control. We will use the FastQC tool to asses the quality of our reads and determine if we need to perform any data cleaning before proceeding with our analysis.
Hands-on: Quality Control
- FastQC tool with the following parameters:
- param-files “Raw read data from your current history”:
subset_1.fastq.gz
andsubset_2.fastq.gz
- Click on param-files Multiple datasets
- Select several files by keeping the Ctrl (or COMMAND) key pressed and clicking on the files of interest
Go to the web page result page and have a closer look at ‘Per base sequence content’
Question
- Note the GC distribution and percentage of “T” and “C”. Why is this so weird?
- Is everything as expected?
- 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.
- Yes it is. Always be careful and have the specific characteristics of your data in mind during the interpretation of FastQC results.
Alignment
Hands-on: Mapping with bwamethWe will map now the imported dataset against a reference genome.
- bwameth tool with the following parameters:
- Select for the option
Select a genome reference from your history or a built-in index?
Use a built-in index
and here the humanhg38
genome.- Choose for the option
Is this library mate-paired?
Paired-end
and use the two imported datasets as an input. Compute now the alignment. Please notice that depending on your system this computation can take some time. If you want to skip this, we provide for you a precomputed alignment. Importaligned_subset.bam
to your history.QuestionWhy we need other alignment tools for bisulfite sequencing data?
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.
Methylation bias and metric extraction
Hands-on: Methylation biasIn this step we will have a look at the distribution of the methylation and will look at a possible bias.
- MethylDackel tool with the following parameters:
- Choose at the first option
Load reference genome from
Local cache
and forUsing reference genome
the valuehg38
.- Select for the option
sorted_alignments.bam
the computed bam file of step 4 of thebwameth
alignment.- Use for
What do you want to do?
the valueDetermine the position-dependent methylation bias in the dataset, producing diagnostic SVG images
.- Set the parameters
By default, if only one read in a pair aligns (a singleton) then it's ignored.
andBy default, paired-end alignments with the properly-paired bit unset in the FLAG field are ignored. Note that the definition of concordant and discordant is based on your aligner settings.
toYes
.Question
- Consider the
original top strand
output. Is there a methylation bias?- If we would trim, what would be the start and the end positions?
- 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.
- To trim the reads we would include for the first strand only the positions 0 to 145, for the second 6 to 149.
Hands-on: Methylation extraction with MethylDackelWe 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.
- MethylDackel tool with the following parameters:
- Choose at the first option
Load reference genome from
the value:Local cache
and forUsing reference genome
the value:hg38
.- Select for the option
sorted_alignments.bam
the computed bam file of step 4 of thebwameth
alignment.- Use for
What do you want to do?
the valueExtract methylation metrics from an alignment file in BAM/CRAN format
.- Choose
Yes
for the optionMerge per-Cytosine metrics from CpG and CHG contexts into per-CPG or per-CHG metrics
.- Set the parameter
Extract fractional methylation (only) at each position. This is mutually exclusive with --counts, --logit, and --methylKit
toYes
.- All other options use the default value.
Visualization
Hands-onIn 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.
- Wig/BedGraph-to-bigWig tool with the following parameters:
Use the result of MethylDackel to transform it to a bigWig file.
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)
.- computeMatrix tool with the following parameters:
- Use the file
CpGIslands.bed
asRegions to plot
and the in the previous step created bigwig file as thescore file
.- Use for the option
computeMatrix has two main output options
the valuereference-point
.- plotProfile tool with the following parameters:
- Choose for
Matrix file from the computeMatrix tool
the computed matrix from the toolcomputeMatrix
.The output should look like this:
Lets see how the methylation looks for a few provided files:
- Galaxy tool: Import the files
NB1_CpG.meth.bedGraph
from the data library- Wig/BedGraph-to-bigWig tool with the following parameters:
- Use the imported file to transform it to a bigWig file.
QuestionThe execution fails. Do you have an idea why?
A conversion to bigWig would fail right now, probably with 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
Replace information file
for hg38 chromosome: Download and import it to Galaxy.- Replace column tool:
- Choose for
File in which you want to replace some values
the previous usedNB1_CpG.meth.bedGraph
file and forReplace information file
conversion file. ForWhich column should be replaced?
chooseColumn: 1
, forSkip this many starting lines
a1
and forDelimited by
Tab
.- To save compute time we prepared the converted files for you. Import the files:
NB1_CpG.meth_ucsc.bedGraph
,NB2_CpG.meth_ucsc.bedGraph
,BT089_CpG.meth_ucsc.bedGraph
,BT126_CpG.meth_ucsc.bedGraph
,BT198_CpG.meth_ucsc.bedGraph
andMCF7_CpG.meth_ucsc.bedgraph
.- Compute the matrix and plot the profile as described above.
More information about deepTools can be found here: https://deeptools.readthedocs.io
Metilene
Hands-on: MetileneWith metilene it is possible to detect differentially methylated regions (DMRs) which is a necessary prerequisite for characterizing different epigenetic states.
- Galaxy tool: Import from the data library the files
NB1_CpG.meth.bedGraph
,NB2_CpG.meth.bedGraph
andBT198_CpG.meth.bedGraph
.- Metilene tool:
- Choose for the first option
Input group 1
the imported files starting withNB
and forInput group 2
the imported filesBT198_CpG.meth.bedGraph
.- Select for the option
BED file containing regions of interest
the imported BED file CpGIslands.bed.More information about metilene can be found here: https://www.bioinf.uni-leipzig.de/Software/metilene
QuestionHave a look at the produced pdf document. What is the data showing?
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