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BIOINFORMATICS LAB Episode VIII – ChIP-Seq Analysis Andrew N. Holding, PhD Federico M. Giorgi, PhD Chiara Cabrelle, TA Department of Pharmacy and Biotechnology First Cycle Degree in Genomics

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Page 1: BIOINFORMATICS LAB Episode VIII ChIP-Seq Analysis · BIOINFORMATICS LAB Episode VIII –ChIP-Seq Analysis Andrew N. Holding, PhD Federico M. Giorgi, PhD Chiara Cabrelle, TA Department

BIOINFORMATICS LAB

Episode VIII – ChIP-Seq

Analysis

Andrew N. Holding, PhDFederico M. Giorgi, PhD

Chiara Cabrelle, TA

Department of Pharmacy and Biotechnology

First Cycle Degree in Genomics

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• Ferrè and/or Giorgi

• 27 June, 10:00 AULA 1 SC FARMACEUTICHE Via Belmeloro 6

• 18 July, 10:00 AULA B Via Irnerio 42

• Giorgi Exam

– One tool picked from the list (1+ slides presentation)

– Oral Questions about the Lab topics

• Ferrè Exam

– Ask Ferrè

Exam Sessions - Bioinformatics

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Andrew Holdinghttp://andrewholding.com/

Proteomics Genomics Cancer Game Theory

http://andrewholding.com/
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Science in Cambridge!

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Science in Cambridge!Downing College

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Science in Cambridge!Gryffindor House

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Science in Cambridge!

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Science in Cambridge! Holding's Office (Former Giorgi's Office)

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UPDATE R

BA

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TRANSCRIPTION FACTORS

BA

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TRANSCRIPTION FACTORS

BA

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CHIP-SEQ

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CHIP-SEQ

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ESTROGEN RECEPTOR & BREAST CANCER

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ESTROGEN RECEPTOR & BREAST CANCER

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INSTALL DIFFBIND

if (!requireNamespace("BiocManager", quietly = TRUE))

install.packages("BiocManager")

BiocManager::install("DiffBind")

library(DiffBind)

First we must install DiffBind.

You may previously have come across EdgeR and DESeq2 as methods of analysing RNA-seq

data.

The methods we are going to apply to the analysis of ChIP-seq data are based on these two

packages. Hopefully many of the ideas we use will there for be familiar.

Load Rstudio and run the following commands.

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LOAD THE SAMPLE SHEET

DiffBind requires a sample sheet.

- A sample sheet provides the meta data on each of the samples.

- Metadata includes the tissue type, if the cells were resistant or not, and replicate numbers

- The sample sheet also provides a list of where the files that include where the reads and the peak

files (generated by MACS2) are stored .

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LOAD THE SAMPLE SHEET

DiffBind requires a sample sheet.

Helpfully there is example data included in the DiffBind package from a real experiment.

The following code lets us see what files are included in the DiffBind package.

pathDiffBind<-system.file("extra", package="DiffBind")

list.files(pathDiffBind)

Output:

The file we want is tamoxifen.csv

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LOAD THE SAMPLE SHEET

We can load the Sample Sheet using read.csv(). This function imports the CSV file into R.

pathDiffBind<-system.file("extra", package="DiffBind")

sampleSheet<-read.csv(paste0(pathDiffBind,"/","tamoxifen.csv"))

View(sampleSheet)

Then view with:

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TABLES

pathDiffBind<-system.file("extra", package="DiffBind")

sampleSheet<-read.csv(paste0(pathDiffBind,"/","tamoxifen.csv"))

View(sampleSheet[sampleSheet$Tissue=='MCF7',])

We can adapt this code if we only want to look at the samples labelled MCF7 in the Tissue column.

Exercise: How would we view only the resistant samples?

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CREATING THE DIFFBIND OBJECT

Now we know we can load the sample sheet, we can create the DiffBind Object.

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CREATING THE DIFFBIND OBJECT

setwd(system.file("extra", package="DiffBind"))

pathDiffBind<-system.file("extra", package="DiffBind")

dbaObject<-dba(sampleSheet=paste0(pathDiffBind,"/","tamoxifen.csv"))

dbaObject

Now we know we can load the sample sheet, we can create the DiffBind Object.

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CLUSTERING THE SAMPLES

setwd(system.file("extra", package="DiffBind"))

pathDiffBind<-system.file("extra", package="DiffBind")

dbaObject<-dba(sampleSheet=paste0(pathDiffBind,"/","tamoxifen.csv"))

plot(dbaObject)

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CLUSTERING THE SAMPLES

dba.plotVenn(dbaObject,mask=dbaObject$masks$T47D)

## If you need to reload data

data(tamoxifen_peaks)

dbaObject<-tamoxifen

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CLUSTERING THE SAMPLES

dba.plotVenn(dbaObject,mask=dbaObject$masks$T47D)

## If you need to reload data

data(tamoxifen_peaks)

dbaObject<-tamoxifen

Exercise: How would we view the peak overlap of the resistant samples?

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PEAK OVERLAP

overlap<-dba.overlap(dbaObject,mode=DBA_OLAP_RATE)

plot(overlap)

## If you need to reload data

data(tamoxifen_peaks)

dbaObject<-tamoxifen

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READ COUNTS

dba.count(dbaObject, minOverlap=3)

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READ COUNTS

dba.count(dbaObject, minOverlap=3)

data(tamoxifen_counts)

dbaObject<-tamoxifen

plot(dbaObject)

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READ COUNTS

data(tamoxifen_counts)

dbaObject<-tamoxifen

plot(dbaObject)

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PRINCPAL COMPONENT ANALYSIS

dba.plotPCA(dbaObject)

Exercise: How would we view the 2nd and 3rd principal component,

and colour by condition?

data(tamoxifen_counts) #If you need to reload data

dbaObject<-tamoxifen

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PRINCPAL COMPONENT ANALYSIS

dba.plotPCA(dbaObject,components = 2:3,attributes=DBA_CONDITION)

data(tamoxifen_counts) #If you need to reload data

dbaObject<-tamoxifen

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DIFFBIND ANALYSIS

dbaObject<-dba.contrast(dbaObject, categories = DBA_CONDITION)

dbaObject<-dba.analyze(dbaObject)

dba.plotMA(dbaObject)

data(tamoxifen_counts) #If you need to reload data

dbaObject<-tamoxifen

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Alternative AnalysisExercise: Up to this point we have focused on the difference in ER binding between

Tamoxifen resistant and sensitive cells.

What if we want to look at the difference between two different types of breast cancer?

MCF7 cells have a heterozygous loss of Progesterone receptor, while T47D have a gain. How

does this change ER binding in a patient?

Start from count data.

data(tamoxifen_counts)

dbaObject<-tamoxifen

count contrast analyze plot

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MCF7 vs T47D

Load count data

dba.contrast

dba.analyze

dba.plotMA

dbaObject

minMembers=2

categories=DBA_TISSUE

dbaObject

dbaObject

contrast=?

data(tamoxifen_counts)

dbaObject<-tamoxifen

Suggested Parameters

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Alternative Analysis

#Load count data

data(tamoxifen_counts)

dbaObject<-tamoxifen

#Establish contrast

dbaObject_tissue<-dba.contrast(dbaObject, categories = DBA_TISSUE, minMembers = 2)

#Analyse

dbaObject_tissue<-dba.analyze(dbaObject_tissue)

#Gives BT474 vs MCF7, we need to choose correct contrast

dba.plotMA(dbaObject_tissue)

#list contrasts and let them find interesting ones.

dba.contrast(dbaObject_tissue,categories = DBA_TISSUE,minMembers = 2)

#plot correct contrast

dba.plotMA(dbaObject_tissue,contrast = 4)

Answer

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Alternative to MA plot

dbaObject<-dba.contrast(dbaObject, categories = DBA_CONDITION)

dbaObject<-dba.analyze(dbaObject)

dba.plotMA(dbaObject)

data(tamoxifen_analysis) #If you need to reload data

dbaObject<-tamoxifen

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Alternative to MA plot

dbaObject<-dba.contrast(dbaObject, categories = DBA_CONDITION)

dbaObject<-dba.analyze(dbaObject)

dba.plotVolcano

data(tamoxifen_analysis) #If you need to reload data

dbaObject<-tamoxifen

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Alternative to MA plot

dbaObject<-dba.contrast(dbaObject, categories = DBA_CONDITION)

dbaObject<-dba.analyze(dbaObject)

dba.plotVolcano

data(tamoxifen_analysis) #If you need to reload data

dbaObject<-tamoxifen

There is something

missing here! Can

you guess what?

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REPORT

View(as.data.frame(dba.report(dbaObject,contrast = 1)))

data(tamoxifen_analysis) #If you need to reload data

dbaObject<-tamoxifen

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REPORTif (!requireNamespace("BiocManager", quietly = TRUE))

install.packages("BiocManager")

if (!requireNamespace("ChIPpeakAnno", quietly = TRUE))

BiocManager::install("ChIPpeakAnno")

library(ChIPpeakAnno)

### Reload data

data(tamoxifen_analysis)

dbaObject<-tamoxifen

### Export report

dfReport<-dba.report(dbaObject,contrast = 1)

### Annotate

data(TSS.human.GRCh37)

dfReport <- annotatePeakInBatch(dfReport, AnnotationData=TSS.human.GRCh37)

View(as.data.frame(dfReport))

You should now have a feature column with Ensembl IDs with for each peak

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REPORTif (!requireNamespace("BiocManager", quietly = TRUE))

install.packages("BiocManager")

if (!requireNamespace("org.Hs.eg.db", quietly = TRUE))

BiocManager::install("org.Hs.eg.db")

if (!requireNamespace("ChIPpeakAnno", quietly = TRUE))

BiocManager::install("ChIPpeakAnno")

library(ChIPpeakAnno)

### Reload data

data(tamoxifen_analysis)

dbaObject<-tamoxifen

### Export report and annotate

dfReport<-dba.report(dbaObject,contrast = 1)

data(TSS.human.GRCh37)

dfReport <- annotatePeakInBatch(dfReport, AnnotationData=TSS.human.GRCh37)

### Add gene symbols

library(org.Hs.eg.db)

dfReport<- addGeneIDs(annotatedPeak=dfReport,

orgAnn="org.Hs.eg.db",

IDs2Add="symbol")

View(as.data.frame(dfReport))

You should now have a symbol column with the gene symbol

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Exercise!Visualize your peaks on the Human Genome

# Generate a BED file

output<-as.data.frame(dba.report(dbaObject,contrast = 1))

bed<-output[,1:3]

outfile<-paste0(file.path(Sys.getenv("USERPROFILE"),"Desktop"),"/example.bed")

write.table(bed,quote=FALSE,row=FALSE,col=FALSE,file=outfile,sep="\t")

Open the BED file: it contains ChIP-Seq peak coordinates

-> Go to UCSC Genome Browser

Click on Genomes, Human, hg19

My Data, Custom Tracks, Choos File, Select your BED file, submit

https://genome-euro.ucsc.edu/
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Exercise!

Click on GO!

See where the peaks are (ER binding sites)

You already know which genes it binds (on chromosome 18)

Check those genes on the Genome Browser!

View(as.data.frame(dfReport))

Last column is what you need (symbol)

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Exercise!

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Further Readings

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Further Readings

Page 47: BIOINFORMATICS LAB Episode VIII ChIP-Seq Analysis · BIOINFORMATICS LAB Episode VIII –ChIP-Seq Analysis Andrew N. Holding, PhD Federico M. Giorgi, PhD Chiara Cabrelle, TA Department

www.giorgilab.org

Andrew N. Holding, PhD

Department of Pharmacy and Biotechnology

[email protected]


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