tutorial.md

layout	title	zenodo_link	questions	time_estimation	objectives	key_points	contributors
tutorial_hands_on	Infinium Human Methylation BeadChip	https://zenodo.org/record/1251211#.WwREQ1Mvz-Y	Which DNA regions and positions are diffrentialy methylated in pre MAPKi treatment and post MAPKi resistance Melanomas GSE65183? How to analyse and visualise Infinium Human Methylation BeadChip's?	1h	Learn how to perform reproducible Infinium Human Methylation BeadChip analysis Visualise differentially methylated positions using UCSC browser	Infinium Human Methylation BeadChip is an array based technology to generate DNA methylation profiling at individual CpG loci in the human genome based on Illumina’s bead technology. Time and cost efficiency followed by high sample output, and overall quantitative accuracy and reproducibility made Infinium Human Methylation BeadChip one of the most widely used arrays on the market.	kpbioteam kpoterlowicz

Agenda

In this tutorial we will do:

1. TOC {:toc} We will use a small subset of the original data. If we run the tutorial on the orginal dataset, analysis will be time consuming and not reproducible Infinium Human Methylation BeadChip computation on the orginal data can be found at case study.

{: .agenda}

This tutorial is based on Hugo W, Shi H, Sun L, Piva M et al.: Non-genomic and Immune Evolution of Melanoma Acquiring MAPKi Resistance..

The data we use in this tutorial are available at Zenodo.

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Introduction

{:.no_toc}

The field of cancer genomics has demonstrated the power of massively parallel sequencing techniques to inform on genes and specific alterations that drive tumor onset and progression. Although large comprehensive sequence data sets continue to be made increasingly available, data analysis remains an ongoing challenge, particularly for laboratories lacking dedicated resources and bioinformatics expertise. To address this, we have provided training based on Galaxy Infinium Human Methylation BeadChip tool that represents many popular algorithms for detecting somatic genetic alterations from genome and exome data.

This exercise uses datasets from the Cell publication by {% cite Hugo2015 %}. with the goal being the identification of differentially methylated regions and positions associated with treatment resistant melanomas. Datasets include the Infinium Human Methylation BeadChip array performed in melanoma tumors in a sample of patients pre and post MAPKi and BRAFi treatment with different outcomes (sensitive and resistant). For each sample there is raw green (methylated) and red (unmethylated) colour arrays containing the summarised bead information generated by the Infinium Human Methylation BeadChip scanner.

The Infinium Human Methylation BeadChip uses two different bead types to detect changes in DNA methylation levels. In the figure we can see M - methylated and U - unmethylated bead types. In our study unmethylated and methylated bead signals are reported as green and red colors respectively.

Accession	Sensitivity	Treatment
GSM1588704	baseline	pre-treatment
GSM1588705	baseline	pre-treatment
GSM1588706	resistant	BRAFi
GSM1588707	resistant	BRAFi

The workflow combines 5 main steps, starting with raw intensity data loading (.idat) and then optional preprocessing and normalisation of the data. The next quality control step performs an additional sample check to remove low-quality data, which normalisation cannot detect. The workflow gives the user the opportunity to perform any of these preparation and data cleaning steps, including a highly recommended genetic variation annotation step resulting in single nucleotide polymorphism identification and removal. Finally, the dataset generated through all of these steps can be used to hunt (find) differentially-methylated positions (DMP) and regions (DMR) with respect to a phenotype covariate.

Raw intensity data loading

The first step of the Infinium Human Methylation BeadChip array analysis is raw methylation data loading (intensity information files for each two colour micro array)

{% icon hands_on %} Hands-on: Data Loading

1. Create a new history for this tutorial and give it a proper name

   {% snippet faqs/galaxy/histories_create_new.md %}

2. Import the following IDAT files from Zenodo or from the data library (ask your instructor)

   • GSM1588704_8795207135_R01C02_Red.idat
   • GSM1588705_8795207119_R05C02_Red.idat
   • GSM1588706_8795207135_R02C02_Red.idat
   • GSM1588707_8795207119_R06C02_Red.idat
   • GSM1588704_8795207135_R01C02_Grn.idat
   • GSM1588705_8795207119_R05C02_Grn.idat
   • GSM1588706_8795207135_R02C02_Grn.idat
   • GSM1588707_8795207119_R06C02_Grn.idat

   {% icon details %} List of Zenodo URLs

   https://zenodo.org/record/1251211/files/GSM1588704_8795207135_R01C02_Red.idat
   https://zenodo.org/record/1251211/files/GSM1588706_8795207135_R02C02_Red.idat
   https://zenodo.org/record/1251211/files/GSM1588705_8795207119_R05C02_Red.idat
   https://zenodo.org/record/1251211/files/GSM1588707_8795207119_R06C02_Red.idat
   https://zenodo.org/record/1251211/files/GSM1588704_8795207135_R01C02_Grn.idat
   https://zenodo.org/record/1251211/files/GSM1588706_8795207135_R02C02_Grn.idat
   https://zenodo.org/record/1251211/files/GSM1588705_8795207119_R05C02_Grn.idat
   https://zenodo.org/record/1251211/files/GSM1588707_8795207119_R06C02_Grn.idat
   

   {: .details}

   {% snippet faqs/galaxy/datasets_import_via_link.md %}

   {% snippet faqs/galaxy/datasets_import_from_data_library.md %}

3. Run Infinium Human Methylation BeadChip {% icon tool %} with the following parameters:

   • {% icon param-files %} "red channel files": all files ending in _Red
   • {% icon param-files %} "green channel files": all files ending in Grn {: .hands_on}

.idat preprocessing

Preprocessing and data quality assurance is an important step in Infinium Methylation Assay analysis. Idat dataset represents two colour data with a green and a red channel and can be converted into methylated and unmethylated signals or into Beta values. The Infinium Human Methylation BeadChip tool extracts and plots the quality control data frame with two columns mMed and uMed which are the medians of methylation signals (Meth and Unmeth). Comparing them against one another allows users to detect and remove low-quality samples.

{% icon hands_on %} Hands-on: Preprocessing

Ilumina methylation array data can be mapped to the genome with or without additional preprocessing methods. Incomplete annotation of genetic variations such as single nucleotide polymorphism (SNP) may affect DNA measurements and disrupt downstream analysis of results. {% cite Hansen %} It is highly recommended to remove the probes that contain either an SNP at the methylated loci interrogation or at the single nucleotide extension. In this tutorial we will remove probes affected by genetic variation by selecting (Optional) Preprocessing Method {% icon tool %}.

{: .hands_on}

{% icon comment %}(optional) Normalisation of the data

If your files require normalisation, you might prefer to use one of the other preprocessing tools provided in Infinium Human Methylation BeadChip tool i.e. Preprocess Funnorm or Preprocess Quantile look for recommendation at {% cite Hansen %}.

{: .comment}

Differentially methylated regions and positions analysis

The main goal of the Infinium Human Methylation BeadChip analysis is to simplify the way differentially methylated loci sites are detected. The Infinium Human Methylation BeadChip pipeline contains differentially methylated positions (DMPs) detection with respect to a phenotype covariate, and more complex solutions for finding differentially methylated regions (DMRs). Genomic regions that are differentially methylated between two conditions can be tracked using a bumphunting algorithm. The algorithm first implements a t-statistic at each methylated loci location, with optional smoothing, then groups probes into clusters with a maximum location gap and a cutoff size to refer the lowest possible value of genomic profile hunted by our tool.

{% icon comment %} Phenotype table

Phenotype table can be in different sizes with different arguments, however the second column is required to contain phenotype covariate information for each sample. {: .comment} However, for the purpose of this tutorial we would like you to upload phenotype table from Zenodo repository.

{% icon hands_on %} Hands-on: Import phenotypeTable.txt from Zenodo or data library:

https://zenodo.org/record/1251211/files/phenotypeTable.txt

Set the following parameters:

• "maxGap Size":250 We will use the default gap of 250 base pairs (bps), i.e. any two points more than 250 bps away are put in a new cluster.
• "Cutoff Size":0.1 In order to find segments that are positive, near zero, and negative. We need a cutoff which is one number in which case “near zero” default 0.1

• "Number of Resamples":0 Default value 0 for permutation method apply selection of randomized cases with replacement from the original data while using 'bootstrap' method.
• "nullMethod":permutation Method used to generate null candidate regions, must be one of ‘bootstrap’ or ‘permutation’ (defaults to ‘permutation’).
• "Phenotype Type":categorical Identify regions where methylation is associated with a continuous or categorical phenotype.

Search for UCSC Main in the tool search bar (top left)

• "qCutoff Size":0.5 Diffrentialy methylated positions with an FDR q-value greater than this value will not be returned.
• "Variance Shrinkage": TRUE Default TRUE as it is recommended when sample sizes are small <10
• "Genome Table": wgEncodeHaibMethyl450 ... Click on UCSC Main {% icon tool %}. You will be taken to the UCSC table browser Set the following options: - "clade": Mammal - "genome": Human - "assembly": Feb. 2009 (GRCh37/hg19) - "group": Regulation - "track": HAIB Methyl450 - "table": GM12878 (wgEncodeHaibMethyl450Gm12878SitesRep1) - "region": genome - "output format": GTF - gene transfer (limited) - "Send output to": Galaxy (only) Click on the get output button at the bottom of the screen On the next page, click on the Send Query to Galaxy button Wait for the upload to finish We will now map the imported datasets against phenotype covariate and reference genome obtained from UCSC. Click on the Differentially_Methylated_Positions.bed output in your history to expand it. Set the database build of your dataset to Human Feb. 2009 (GRCh37/hg19) (hg19)(if it is not set automatically)

{% snippet faqs/galaxy/datasets_change_dbkey.md dbkey="hg19" %}

Click on display at UCSC towards the bottom of the history item. This will launch UCSC Genome Browser with your Custom Track {: .hands_on}

{% icon question %} Questions

How do we define phenotype covariate?

{% icon solution %} Solution

Phenotype covariate is the set of observable characteristics of an individual resulting from the gene-environment interactions {: .solution} {: .question}

Annotation and visualization

In addition to downstream analysis users can annotate the differentially methylated loci at the promoter regions of genes with gene function descriptions, and relationships between these concepts.

{% icon hands_on %} Hands-on: Annotate Differentially Methylated Position

1. Run chipeakanno annopeaks {% icon tool %}on the output of minfi_dmp with the following parameters

• {% icon param-file %} "Differentialy methylated data": output of minfi dmp {% icon tool %}

• "bindingType": StartSite

• "bindingRegionStart":-5000

• "bindingRegionEnd":3000

• "Additional Column of Score":8

  Position of column of score optional value if it is required

2. Cut {% icon tool %} on the previous output adjusting the following parameters to cut "gene_name" column from table of annotated peaks and then get a list of genes

• "Cut columns": c16hhh
• "Delimited by": Tab
• {% icon param-file %} "From": output of chipeakanno annopeaks {% icon tool %}

3. Remove beginning {% icon tool %} of Gene List with the following parameters

• "Remove first": 1
• {% icon param-file %} "from": output of Cut {% icon tool %}

4. Run clusterProfiler bitr {% icon tool %} on the previous output adjusting the following parameters to convert the list of genes to list of entrez ID

• "Input Type Gene ID": SYMBOL
• "Output Type Gene ID": ENTREZID

5. Use the output of the clusterProfiler bitr {% icon tool %} to run a GO Enrichment Analysis using clusterProfiler go {: .hands_on}

ID	Description	pvalue	qvalue	geneID	Count
GO:0048732	gland development	1.38E-58	4.23E-55	PTGS2 / KCNC1 / FZD1 /SLC22A18 /SLC22A3 (...)	372
GO:1901652	response to peptide	3.99E-57	8.13E-54	SULF1/ LAMA5/ MED1 /CFLAR/ MSX2 (...)	359
GO:0048545	response to steroid hormone	1.38EE-54	2.11E-51	HDAC9/ RAB10/ CFLAR/ WDTC1 (...)	394

Conclusion

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Epigenetic aberrations which involve DNA modifications give researchers an interest in identifying novel non-genetic factors responsible for complex human phenotypes such as height, weight, and disease. To identify methylation changes researchers need to perform complicated and time consuming computational analysis. Here, the EWAS suite becomes a solution for this inconvenience and provides a simplified downstream analysis available as a ready to run pipline in supplementary materials.