1. Transcriptome Analysis
Song Li

2. Introduction to Transcriptome Analysis
Goal of transcriptome analysis
Differentially expressed genes/small RNA
Clustering/network analysis
Identify novel transcripts (w/o reference genome)
Types of transcriptome analysis
RNA-seq
Small RNA-seq
Ribo-seq, PEAT-seq, PolyA-seq
Normalized RNA-seq (for lincRNA)

3. Workflow for Transcriptome Analysis
RNA-seq reads
De-multiplexing
Quality Accessment
Adapter trimming
FASTX-toolkits, Btrim, FASTQC
Map reads to reference genome/transcriptome
De novo assembly
Bowtie, BWA, tophat, STAR and more
Trinity, Trans-Abyss, Soap-denovo
Differential Expression (gene, small RNA)
Differential Splicing
Merge, compare and filtering
HTseq, edgeR, DESeq,
BayesSeq and more
Cufflinks, DEXSeq MISO,MATS
BLAST like tools
Always follow up with wet-bench validation !

4. Introduction Steps for transcriptome analysis
Read mapping: bowtie/tophat
(Splicing variant detection: cufflinks)
Read counting: HTseq
Differential expression analysis: edgeR/DESeq

5. Software Installation
For bowtie/tophat/cufflinks, looking for binary for your platform
Bowtie2
bowtie linux-x86_64.zip
Tophat2
tophat Linux_x86_64.tar.gz
Cufflinks2
cufflinks Linux_x86_64.tar.gz

6. Data # Data from the read mapping class (Fall 2014)
# Genome annotation file
ITAG2.4_gene_models.chr01.gff3
# Genomic sequence
SL2.50ch01.fa
# RNAseq reads, three replicates per sample
Sohab_LA1777_apmer_r1.c01.fq; Sohab_LA1777_apmer_r2.c01.fq
Sohab_LA1777_apmer_r3.c01.fq
Solyc_M82_apmer_r1.c01.fq; …
Sopen_LA0716_apmer_r1.c01.fq; …

7. Read Mapping (1) Build reference genome index # buildgenome.sh
# define your working directory
WORKDIR=/home/li/Research/BioinforClass/Transcriptome/
# define bowtie home
BOWTIEHOME=$WORKDIR/softwares/bowtie
# change the working directory to where the index files will be located
cd $WORKDIR/data/bowtie2index
# build index
$BOWTIEHOME/bowtie2-build ../SL2.50ch01.fa SL2index >bt2index.log

8. Read Mapping (1) Expected output
# log file for the bowtie2-build program
bt2index.log
# 6 index files
SL2index.1.bt2
SL2index.2.bt2
SL2index.3.bt2
SL2index.4.bt2
SL2index.rev.1.bt2
SL2index.rev.2.bt2

9. Read Mapping (2) Use Tophat for read alignment
Splicing aware alignment
# tophatalign_Sohab1.sh
WORKDIR=/home/li/Research/BioinforClass/Transcriptome/
TOPHATHOME=$WORKDIR/softwares/tophat Linux_x86_64
BOWTIEINDEX=$WORKDIR/data/bowtie2index/SL2index
INPUTDATA=$WORKDIR/data/Sohab_LA1777_apmer_r1.c01.fq
OUTDIR=$WORKDIR/results/Sohab1
cd $OUTDIR
$TOPHATHOME/tophat2 -o $OUTDIR $BOWTIEINDEX $INPUTDATA 2> tophat.log

10. Read Mapping (2) Expected output accepted_hits.bam # alignment file
unmapped.bam # unmapped reads
junctions.bed # splice junctions
deletions.bed # deletions
insertions.bed # insertions
prep_reads.info # number of input/output reads
align_summary.txt # alignment rate (% of reads aligned)
Logs # other log files
tophat.log # runtime log file

11. Read Mapping (2) Useful parameters
-N/--read-mismatches discard if MM is more than this number
-r/--mate-inner-dist paired-end distance
-a/--min-anchor-length anchor length
-i/--min-intron-length length of introns
-I/--max-intron-length length of introns
-g/--max-multihits number of multihits
--report-secondary-alignments
-G/--GTF use genome annotation
--transcriptome-index

12. Introduction Steps for transcriptome analysis
Read mapping: bowtie/tophat (~20 lines of code)
Read counting: HTseq
Differential expression analysis: edgeR/DESeq

13. Counting Reads Install htseq-count for counting reads
HTseq-count (Python program)
Download from:
Installation:
$python setup.py install --user
Installed directory (add this to your PATH):
~/.local/bin

14. Counting Reads
Mode of overlapping

15. Counting Reads Script for read count
workdir=/home/li/Research/BioinforClass/Transcriptome
gff=$workdir/data/ITAG2.4_gene_models.chr01.gff3
sam=$workdir/results/Sohab1/sorted.sam
output=$workdir/results/counts/Sohab1.count
htseq-count --format=sam \
--stranded=no \
--order=name \
--type=exon \
--idattr=Parent \
$sam $gff > $output

16. Counting Reads Expected output: ./HTseqcount.sopen.sh
72727 GFF lines processed.
SAM alignment records processed.
SAM alignment records processed.
SAM alignments processed.
In the result folder:
Sohab1.count Sohab3.count Solyc2.count Sopen1.count Sopen3.count Sohab2.count Solyc1.count Solyc3.count Sopen2.count

17. Counting Reads Expected output: mRNA:Solyc01g112300.2.1 13
__no_feature
__ambiguous
__too_low_aQual 0
__not_aligned 0
__alignment_not_unique

18. Introduction Steps for transcriptome analysis
Read mapping: bowtie/tophat (~20 lines of code)
Read counting: Htseq (~10 lines of code)
Differential expression analysis: edgeR/DESeq

19. Combine Read Counts MergeReads.R # in source code MergeReads.R
filenames=dir('results')
filenames=grep('csv',filenames,value=TRUE)
# in console
> filenames
[1] "Sohab1.csv" "Sohab2.csv" "Sohab3.csv" "Solyc1.csv" "Solyc2.csv" "Solyc3.csv"
[7] "Sopen1.csv" "Sopen2.csv" "Sopen3.csv"

20. Differential Expression
MergeReads.R
# in console: check the size of the inputs
> tail(tmpmat) X1
Solyc01g
__no_feature
__ambiguous
__too_low_aQual
__not_aligned
__alignment_not_unique 25744
> dim(tmpmat)
[1]

21. Differential Expression
MergeReads.R
# in source code MergeReads.R
# create data matrix
datmat<-matrix(0, ncol=9,nrow=4293)
colnames(datmat)<-filenames
rownames(datmat)<-rownames(tmpmat)[1:4293]

22. Differential Expression
MergeReads.R
# in source code MergeReads.R
# use loop to read all the files
for(eachfn in filenames) {
print(eachfn)
tmpmat<-read.table(eachfn,sep='\t',
as.is=T,col.names=1)
datmat[,eachfn]<-tmpmat[1:4293,1] }

23. Differential Expression
MergeReads.R
# in console: check the data matrix
> datmat[1:3,1:3]
Sohab1 Sohab2 Sohab3
Solyc01g
Solyc01g
Solyc01g
# in source: save the data matrix for future use
write.table(datmat,'mergedcounts.csv')

24. Introduction Steps for transcriptome analysis
Read mapping: bowtie/tophat (~20 lines of code)
Read counting: Htseq (< 10 lines of code)
Differential expression analysis: edgeR/DESeq
~15 lines of code for data preparation

25. Differential Expression
Install edgeR
source('
biocLite('edgeR')

26. Differential Expression
CallDiffGene.R
# load data
datmat<-read.table('mergedcounts.csv',as.is=T)
# set group
group <- c(1,1,1,2,2,2,3,3,3)
# create an object for the count data
dge <- DGEList(counts=datmat,group=group)
# filter low exp genes
keep<-rowSums(cpm(dge))>1
dge<-dge[keep,]

27. Differential Expression
CallDiffGene.R
# MDS plot
plotMDS(dge)

28. Differential Expression
CallDiffGene.R
# normalization
dge<-calcNormFactors(dge)
#make design matrix
groupf<-factor(group)
design<-model.matrix(~0+groupf)
#estimate dispersion
dge<-estimateGLMCommonDisp(dge,design)
dge<-estimateGLMTagwiseDisp(dge,design)
fit<-glmFit(dge,design)

29. Differential Expression
Normalization (TMM)
> dge$samples
group lib.size norm.factors
Sohab
Sohab
Sohab
Solyc
Solyc
Solyc
Sopen
Sopen
Sopen

30. Differential Expression
Design matrix
> design
groupf1 groupf2 groupf3

31. Differential Expression
Biological Coefficient of Variation
dge<-estimateGLMCommonDisp(dge,design)
dge<-estimateGLMTagwiseDisp(dge,design)

32. Differential Expression
CallDiffGene.R
# normalization
dge<-calcNormFactors(dge)
#make design matrix
groupf<-factor(group)
design<-model.matrix(~0+groupf)
#estimate dispersion
dge<-estimateGLMCommonDisp(dge,design)
dge<-estimateGLMTagwiseDisp(dge,design)
fit<-glmFit(dge,design)

33. Differential Expression
CallDiffGene.R
# doing LRT test
lrt.habvslyc<-glmLRT(fit,contrast = c(1,-1,0))
# select subset of genes
tmpdiff<-topTags(lrt.habvslyc,n=1000)
# filter by FDR
diff.habvslyc<-tmpdiff$table[tmpdiff$table[,5]<0.05,]

34. Differential Expression
CallDiffGene.R
# check number of differentially expressed genes
> dim(diff.habvslyc)
[1]
> dim(diff.habvspen)
[1]
> dim(diff.lycvspen)
[1]

35. Differential Expression
CallDiffGene.R
> diff.habvslyc[1:2,]
logFC logCPM
Solyc01g
Solyc01g
LR PValue
Solyc01g e-25
Solyc01g e-19
FDR
Solyc01g e-22
Solyc01g e-16

36. Summary Steps for transcriptome analysis
Read mapping: bowtie/tophat (~20 lines of code)
Read counting: Htseq (< 10 lines of code)
Differential expression analysis: edgeR/DESeq
~15 lines of code for data preparation
~20-30 lines of code for differential expression