1. Leonardo Mariño-Ramírez, PhD NCBI / NLM / NIH
Analysis of genomes and transcriptomes using RNA-seq and ChIP-seq Practical session
Leonardo Mariño-Ramírez, PhD
NCBI / NLM / NIH
ICGEB – Practical Course
"Bioinformatics: Computer Methods in Molecular Biology” June / 2017

2. RNA-seq workflow for the tutorial

3. Slides
ftp://ftp.ncbi.nlm.nih.gov/pub/marino/teaching/ICGEB/2016/

4. Differential gene expression from RNA-Seq data

5. Differential gene expression from RNA-Seq data
1. Get to the RNA-Seq directory and launch R
cd marino-data/RNA-Seq/ ll
total
-rw-r user0 user Apr 7 20:58 GSE27003GPL9115_DGE_22ba48b764533b c3e8e01ae1.db
-rw-r user0 user Apr 7 20:58 GSE27003GPL9115_DGE_RNASeq_22ba48b764533b c3e8e01ae1_report.pdf R
R version ( ) -- "Supposedly Educational”
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
Natural language support but running in an English locale
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
>

6. Differential gene expression from RNA-Seq data
2. Load the cummeRbund library - and perform basic statistic operations
> library(cummeRbund)
Loading required package: BiocGenerics
Attaching package: 'BiocGenerics'
The following object(s) are masked from 'package:stats':
xtabs
The following object(s) are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, cbind,
colnames, duplicated, eval, get, intersect, lapply, mapply, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, rbind, rep.int,
rownames, sapply, setdiff, table, tapply, union, unique
Loading required package: RSQLite
Loading required package: DBI
Loading required package: ggplot2
Loading required package: reshape2
Loading required package: fastcluster
Attaching package: 'fastcluster'
hclust
Loading required package: rtracklayer
Loading required package: GenomicRanges
Loading required package: IRanges
Loading required package: Gviz
Loading required package: grid
>

7. Differential gene expression from RNA-Seq data
> list.files()
[1] "GSE27003GPL9115_DGE_22ba48b764533b c3e8e01ae1.db"
[2] "GSE27003GPL9115_DGE_RNASeq_22ba48b764533b c3e8e01ae1_report.pdf”
> cuff<-readCufflinks(dbFile="GSE27003GPL9115_DGE_22ba48b764533b c3e8e01ae1.db")
> dens<-csDensity(genes(cuff))
> dens
Warning messages:
1: Removed 1997 rows containing non-finite values (stat_density).
2: Removed 4973 rows containing non-finite values (stat_density).
>
The density plot will show you the distribution of your RNA-seq read counts (fpkm)

8. Differential gene expression from RNA-Seq data
3. Display a boxplot of the expression values and a volcano plot
> b<-csBoxplot(genes(cuff))
> b
>

9. Differential gene expression from RNA-Seq data
> v<-csVolcanoMatrix(genes(cuff))
> v
>

10. Differential gene expression from RNA-Seq data
4. Extract the differentially expressed genes and plot a heatmap for both conditions
> mySigGeneIds<-getSig(cuff,alpha=0.05,level='genes')
> myGenes<-getGenes(cuff,mySigGeneIds)
Getting gene information:
FPKM
Differential Expression Data
Annotation Data
Replicate FPKMs
Counts
Getting isoforms information:
Getting CDS information:
Getting TSS information:
Getting promoter information:
distData
Getting splicing information:
Getting relCDS information:
>

11. Differential gene expression from RNA-Seq data
> h.rep<-csHeatmap(myGenes,cluster='both',replicates=F)
Using tracking_id, sample_name as id variables
Using as id variables
> h.rep
>

12. Differential gene expression from RNA-Seq data
> h.rep<-csHeatmap(myGenes,cluster='both',replicates=T)
Using tracking_id, sample_name as id variables
Using as id variables
> h.rep
>

13. Differential gene expression from RNA-Seq data
> h.rep<-csHeatmap(myGenes,cluster='both',replicates=T)
Using tracking_id, sample_name as id variables
Using as id variables
> h.rep
>

14. Differential gene expression from RNA-Seq data

15. ChIP-seq analysis with DiffBind
This package is useful for manipulating ChIP-seq signal in R, for comparing signal across files and for performing tests of diffrential binding.
cd workspace/chipseq/extra/ ls
config.csv peaks reads tamoxifen_allfields.csv tamoxifen.csv tamoxifen_GEO.csv tamoxifen_GEO.R testdata
The dataset for this example consists of ChIPs against the transcription factor ERa using five breast cancer cell lines. Three of these cell lines are responsive to tamoxifen treatment, while two others are resistant to tamoxifen. There are at least two replicates for each of the cell lines, with one cell line having three replicates, for a total of eleven sequenced libraries. Of the five cell lines, two are based on MCF7 cells: the regular tamoxifen responsive line, as well as MCF7 cells specially treated with tamoxifen until a tamoxifen resistant cell line is obtained.

16. ChIP-seq analysis with DiffBindR
R version ( ) -- "Supposedly Educational"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
Natural language support but running in an English locale
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(DiffBind)

17. ChIP-seq analysis with DiffBind
> list.files()
[1] "config.csv" "peaks"
[3] "reads" "tamoxifen_allfields.csv"
[5] "tamoxifen_GEO.csv" "tamoxifen_GEO.R"
[7] "tamoxifen.csv" "testdata"

18. ChIP-seq analysis with DiffBind
> read.csv("tamoxifen.csv")
SampleID Tissue Factor Condition Treatment Replicate
1 BT4741 BT ER Resistant Full-Media
2 BT4742 BT ER Resistant Full-Media
3 MCF71 MCF7 ER Responsive Full-Media
4 MCF72 MCF7 ER Responsive Full-Media
5 MCF73 MCF7 ER Responsive Full-Media
6 T47D1 T47D ER Responsive Full-Media
7 T47D2 T47D ER Responsive Full-Media
8 MCF7r1 MCF7 ER Resistant Full-Media
9 MCF7r2 MCF7 ER Resistant Full-Media
10 ZR751 ZR ER Responsive Full-Media
11 ZR752 ZR ER Responsive Full-Media
bamReads ControlID bamControl
1 reads/Chr18_BT474_ER_1.bam BT474c reads/Chr18_BT474_input.bam
2 reads/Chr18_BT474_ER_2.bam BT474c reads/Chr18_BT474_input.bam
3 reads/Chr18_MCF7_ER_1.bam MCF7c reads/Chr18_MCF7_input.bam
4 reads/Chr18_MCF7_ER_2.bam MCF7c reads/Chr18_MCF7_input.bam
5 reads/Chr18_MCF7_ER_3.bam MCF7c reads/Chr18_MCF7_input.bam
6 reads/Chr18_T47D_ER_1.bam T47Dc reads/T47D_input.bam
7 reads/Chr18_T47D_ER_2.bam T47Dc reads/T47D_input.bam
8 reads/Chr18_TAMR_ER_1.bam TAMRc reads/TAMR_input.bam
reads/TAMR_ER_2.bam TAMRc reads/TAMR_input.bam
10 reads/Chr18_ZR75_ER_1.bam ZR75c reads/ZR75_input.bam
11 reads/Chr18_ZR75_ER_2.bam ZR75c reads/ZR75_input.bam
Peaks PeakCaller
1 peaks/BT474_ER_1.bed.gz bed
2 peaks/BT474_ER_2.bed.gz bed
3 peaks/MCF7_ER_1.bed.gz bed
4 peaks/MCF7_ER_2.bed.gz bed
5 peaks/MCF7_ER_3.bed.gz bed
6 peaks/T47D_ER_1.bed.gz bed
7 peaks/T47D_ER_2.bed.gz bed
8 peaks/TAMR_ER_1.bed.gz bed
9 peaks/TAMR_ER_2.bed.gz bed
10 peaks/ZR75_ER_1.bed.gz bed
11 peaks/ZR75_ER_2.bed.gz bed
>

19. ChIP-seq analysis with DiffBind
> ta <- dba(sampleSheet="tamoxifen.csv")
BT4741 BT474 ER Resistant Full-Media 1 bed
BT4742 BT474 ER Resistant Full-Media 2 bed
MCF71 MCF7 ER Responsive Full-Media 1 bed
MCF72 MCF7 ER Responsive Full-Media 2 bed
MCF73 MCF7 ER Responsive Full-Media 3 bed
T47D1 T47D ER Responsive Full-Media 1 bed
T47D2 T47D ER Responsive Full-Media 2 bed
MCF7r1 MCF7 ER Resistant Full-Media 1 bed
MCF7r2 MCF7 ER Resistant Full-Media 2 bed
ZR751 ZR75 ER Responsive Full-Media 1 bed
ZR752 ZR75 ER Responsive Full-Media 2 bed
> ta
11 Samples, 2845 sites in matrix (3795 total):
ID Tissue Factor Condition Treatment Replicate Caller Intervals
1 BT4741 BT ER Resistant Full-Media bed
2 BT4742 BT ER Resistant Full-Media bed
3 MCF71 MCF7 ER Responsive Full-Media bed
4 MCF72 MCF7 ER Responsive Full-Media bed
5 MCF73 MCF7 ER Responsive Full-Media bed
6 T47D1 T47D ER Responsive Full-Media bed
7 T47D2 T47D ER Responsive Full-Media bed
8 MCF7r1 MCF7 ER Resistant Full-Media bed
9 MCF7r2 MCF7 ER Resistant Full-Media bed
10 ZR751 ZR ER Responsive Full-Media bed
11 ZR752 ZR ER Responsive Full-Media bed
> pdf("Correlation-occupancy-data.pdf")
> plot(ta)
> dev.off()
null device 1
>

20. ChIP-seq analysis with DiffBind
Go to:

21. ChIP-seq analysis with DiffBind
> data(tamoxifen_counts)
> ta2 <- tamoxifen
> ta2 <- dba.contrast(ta2, categories=DBA_CONDITION)
> ta2 <- dba.analyze(ta2)
converting counts to integer mode
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
> pdf("Correlation-significantly-differentially-bound.pdf")
> plot(ta2, contrast=1)
> dev.off()
null device 1
> ta2
11 Samples, 2845 sites in matrix:
ID Tissue Factor Condition Treatment Replicate Caller Intervals FRiP
1 BT4741 BT ER Resistant Full-Media counts
2 BT4742 BT ER Resistant Full-Media counts
3 MCF71 MCF7 ER Responsive Full-Media counts
4 MCF72 MCF7 ER Responsive Full-Media counts
5 MCF73 MCF7 ER Responsive Full-Media counts
6 T47D1 T47D ER Responsive Full-Media counts
7 T47D2 T47D ER Responsive Full-Media counts
8 MCF7r1 MCF7 ER Resistant Full-Media counts
9 MCF7r2 MCF7 ER Resistant Full-Media counts
10 ZR751 ZR ER Responsive Full-Media counts
11 ZR752 ZR ER Responsive Full-Media counts
1 Contrast:
Group1 Members1 Group2 Members2 DB.DESeq2
1 Resistant Responsive
>

22. ChIP-seq analysis with DiffBind
Go to:

23. ChIP-seq analysis with DiffBind
> data(tamoxifen_counts)
> ta2 <- tamoxifen
> ta2 <- dba.contrast(ta2, categories=DBA_CONDITION)
> ta2 <- dba.analyze(ta2)
converting counts to integer mode
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
> pdf("Correlation-significantly-differentially-bound.pdf")
> plot(ta2, contrast=1)
> dev.off()
null device 1
>

24. ChIP-seq analysis with DiffBind
> ta2
11 Samples, 2845 sites in matrix:
ID Tissue Factor Condition Treatment Replicate Caller Intervals FRiP
1 BT4741 BT ER Resistant Full-Media counts
2 BT4742 BT ER Resistant Full-Media counts
3 MCF71 MCF7 ER Responsive Full-Media counts
4 MCF72 MCF7 ER Responsive Full-Media counts
5 MCF73 MCF7 ER Responsive Full-Media counts
6 T47D1 T47D ER Responsive Full-Media counts
7 T47D2 T47D ER Responsive Full-Media counts
8 MCF7r1 MCF7 ER Resistant Full-Media counts
9 MCF7r2 MCF7 ER Resistant Full-Media counts
10 ZR751 ZR ER Responsive Full-Media counts
11 ZR752 ZR ER Responsive Full-Media counts
1 Contrast:
Group1 Members1 Group2 Members2 DB.DESeq2
1 Resistant Responsive
>

25. ChIP-seq analysis with DiffBind
> tadb <- dba.report(ta2)
> tadb
GRanges object with 677 ranges and 6 metadata columns:
seqnames ranges strand | Conc Conc_Resistant
<Rle> <IRanges> <Rle> | <numeric> <numeric>
chr18 [ , ] * |
chr18 [ , ] * |
chr18 [ , ] * |
chr18 [ , ] * |
chr18 [ , ] * |
chr18 [ , ] * |
chr18 [ , ] * |
chr18 [ , ] * |
chr18 [ , ] * |
chr18 [ , ] * |
Conc_Responsive Fold p-value FDR
<numeric> <numeric> <numeric> <numeric>
e e-07
e e-07
e e-06
e e-06
e e-06
seqinfo: 1 sequence from an unspecified genome; no seqlengths
>

26. ChIP-seq analysis with DiffBind
> counts <- dba.report(ta2, bCounts=TRUE)
> x <- mcols(counts)[1,-c(1:6)]
> x <- unlist(x)
> (xord <- x[match(ta2$samples$SampleID, names(x))])
BT4741 BT4742 MCF71 MCF72 MCF73 T47D1 T47D2 MCF7r1 MCF7r2 ZR751 ZR752
> cond <- factor(ta2$samples[,"Condition"])
> condcomb <- factor(paste(ta2$samples[,"Condition"], ta2$samples[,"Tissue"]))
> pdf("Counts-over-the-conditions.pdf")
> par(mar=c(15,5,2,2))
> stripchart(log(xord) ~ condcomb, method="jitter", vertical=TRUE, las=2,
ylab="log2 normalized counts")
> dev.off()
pdf 2
>

27. ChIP-seq analysis with DiffBind
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