1. Transcriptomics – towards RNASeq – part III
Federico M. Giorgi –
Analisi del Genoma e Bioinformatica Corso di Laurea Specialistica in Biotecnologie delle piante e degli animali

2. Overview of the course Transcription and Transcriptomics
Day 1
23/04/2012
Room β3
Transcriptomics methods: Microarrays
Exercises on Microarray analysis
RNASeq
Day 2
02/05/2012
Room 35
Further Applications of RNASeq
Day 3
07/05/2012
Room β3
RNASeq exercises

3. Differential Expression in RNASeq
The pipeline for performing Differential Expression of all genes between samples can be summarized into five steps:
Map reads onto a reference genome or transcriptome
Summarize: i.e. merge counts at a gene-, exon-, isoform- level
Normalize read counts
(optional)
Calculate fold change, p-values,
ranking, differential expression
Interprete the results
(Systems Biology)

4. Differential Expression in RNASeq
The pipeline for performing Differential Expression of all genes between samples can be summarized into five steps:
Map reads onto a reference genome or transcriptome
Filter reads
Tophat
(Spliced exon first aligner)
Summarize: i.e. merge counts at a gene-, exon-, isoform- level
Normalize read counts
(optional)
Cufflinks
Calculate fold change, p-values,
ranking, differential expression
Interprete the results
(Systems Biology)

5. Differential Expression in RNASeq
In real cases, raw reads are filtered prior to mapping:
Generate reads via deep sequencing (e.g. Illumina)

6. Differential Expression in RNASeq
In real cases, raw reads are filtered prior to mapping:
Generate reads via deep sequencing (e.g. Illumina)
Filter reads

7. Differential Expression in RNASeq
In real cases, raw reads are filtered prior to mapping:
Remove contaminant reads
- Bowtie aligner vs. NCBI-BLAST sequence database
- rNA (Vezzi et al., 2001)
Generate reads via deep sequencing (e.g. Illumina)
Filter reads

8. Differential Expression in RNASeq
In real cases, raw reads are filtered prior to mapping:
Remove contaminant reads
- Bowtie aligner vs. NCBI-BLAST sequence database
- rNA (Vezzi et al., 2001)
Trim reads for quality
- Trimmomatic
- rNA
Generate reads via deep sequencing (e.g. Illumina)
Filter reads

9. Differential Expression in RNASeq
In real cases, raw reads are filtered prior to mapping:
Remove contaminant reads
- Bowtie aligner vs. NCBI-BLAST sequence database
- rNA (Vezzi et al., 2001)
Trim reads for quality
- Trimmomatic
- rNA
Correct reads by kmer-frequency*
- Soap corrector
- Allpaths-LG corrector (within Trinity)
Generate reads via deep sequencing (e.g. Illumina)
Filter reads
*Seldom used in quantitative analysis
Mostly used in Transcriptome reconstruction based on unnormalized libraries

10. Differential Expression in RNASeq
In real cases, raw reads are filtered prior to mapping:
Remove contaminant reads
- Bowtie aligner vs. NCBI-BLAST sequence database
- rNA (Vezzi et al., 2001)
Trim reads for quality
- Trimmomatic
- rNA
Correct reads by kmer-frequency
- Soap corrector
- Allpaths-LG corrector (within Trinity)
Generate reads via deep sequencing (e.g. Illumina)
Filter reads
Map reads onto a reference genome or transcriptome
Tophat

11. A real example – contaminants in tomato genomic sequences (Solanum lycopersicum)
80%
Completely new transcripts, or mistakes? 6% 4%
Environmental samples, no species assigned
Unknown species
<0.5%
Possible true contaminants
<0.1%
Chloroplasts
Other «contaminants»:
Mitochondria
BLAST: assembled genomic contigsvs. NCBI nr/nt

12. Today: RNASeq excercises
I see and I forget
I read and I remember
I do and I understand
Confucius
551 B.C. – 479 B.C.

13. Alternative approaches
Exercise of today
Purpose: Differential Expression Analysis
Tools: Tophat/Cufflinks software
Alternative approaches
«Red» way
«Green» way
Completely relies on the available annotation of the Transcriptome
Use reads to identify novel junctions and/or transcripts

14. Use a transcript database to quantify transcript
Workflow
Raw reads
format: fastQ
Trimming
(rNA)
Trimmed reads
format: fastQ
Mapping (TopHat)
Mappings
format: SAM
Use a transcript database to quantify transcript

15. Use a transcript database to quantify transcript
Workflow
Raw reads
format: fastQ
Trimming
(rNA)
Trimmed reads
format: fastQ
Mapping (TopHat)
Mappings
format: SAM
Use a transcript database to quantify transcript
«Red» way

16. Workflow Raw reads format: fastQ Trimming (rNA)
«Green» way
Trimming
(rNA)
Use reads to identify novel junctions and/or transcripts
Trimmed reads
format: fastQ
Mapping (TopHat)
Mappings
format: SAM
Use a transcript database to quantify transcript
«Red» way

17. Workflow Raw reads format: fastQ Trimming (rNA)
«Green» way
Trimming
(rNA)
Use reads to identify novel junctions and/or transcripts
Generate an updated transcript database
Trimmed reads
format: fastQ
Mapping (TopHat)
Mappings
format: SAM
Use a transcript database to quantify transcript
«Red» way

18. Simple, treated vs. control with replicates:
Experimental design
Simple, treated vs. control with replicates:
Treated A
Control A
vs.
Treated B
Control B
Every sample has two fastQ files, because we are gonna work on paired reads
This time, we don’t have a real dataset, like with microarrays, because a full RNASeq pipeline study may take days on a cluster of computers

19. Prepare the directory structure
Open the terminal
cd /home/ngs/data_crunching/RNAseq
mkdir cuffcompare_dir
mkdir cuffmerge_dir
mkdir cuffdiff_red
mkdir cuffdiff_green
mkdir /home/ngs/data_crunching/sequences/rna/trimmed
cd /home/ngs/data_crunching/sequences/rna

20. Look at the reads How long are these reads?
cd /home/ngs/data_crunching/sequences/rna

21. Look at the reads How long are these reads?
cd /home/ngs/data_crunching/sequences/rna
head controlA_R1.fastq

22. Look at the reads How long are these reads?
cd /home/ngs/data_crunching/sequences/rna
head controlA_R1.fastq
How many reads do we have?

23. (Common RNASeq fastQ file: 16,337,998 reads, >2Gbytes)
Look at the reads
How long are these reads?
cd /home/ngs/data_crunching/sequences/rna
head controlA_R1.fastq
How many reads do we have?
wc -l controlA_R1.fastq
grep controlA_R1.fastq | wc -l
(Common RNASeq fastQ file: 16,337,998 reads, >2Gbytes)

24. We will use many for loops to save time:
Trim the reads
We will use many for loops to save time:
for alldir in controlA controlB treatedA treatedB do
echo "$alldir" is a cool directory
done

25. Trim the reads for alldir in controlA controlB treatedA treatedB do
rNA --filter-for-assembly
--query1 "$alldir"_R1.fastq
--query2 "$alldir"_R2.fastq
--threads 1
--min-phred-value-CLC 20
--min-mean-phred-quality 20
--min-size 50
--output trimmed/"$alldir"
done

26. Index the reference GENOME
Go to the genome directory:
cd /home/ngs/data_crunching/reference/
Create a GENOME index for the aligner (TopHat):
bowtie-build Populus_minusculus.fa Populus_minusculus

27. Trapnell suite of software
Trapnell, Nature Protocols, 2012

28. Trapnell suite of software
Trapnell, Nature Protocols, 2012

29. Tophat Aligns RNA-Seq reads to a genome
Able to identify exon-exon splice junctions
Built on the short read aligner Bowtie
Split reads alignment (for reads spanning two exons)
Or google it!!
Read
Reference
Exon
Intron
Exon

30. Tophat alignment
Change directory, point the annotation to the right file:
cd /home/ngs/data_crunching/RNAseq
annotation=/home/ngs/data_crunching/genes/Populus_minusculus.gff3
head $annotation
reference=/home/ngs/data_crunching/reference/Populus_minusculus
head "$reference".fa
Align every read pair against the reference:
for alldir in controlA controlB treatedA treatedB do
mkdir "$alldir"
mkdir "$alldir"/tophat
tophat -o "$alldir"/tophat -G $annotation --max-multihits 10 --initial-read-mismatches 1 --segment-mismatches 0 --segment-length 25 $reference ../sequences/rna/trimmed/"$alldir"_1.fastq ../sequences/rna/trimmed/"$alldir"_2.fastq
done

31. Some Tophat options
-G: provide TopHat with a file of annotation. TopHat will initially map on the transcriptome and then map the remaining reads to the genome (exon-first spliced approach)
-g/--max-multihits: ignore reads with more than this alignments.
--initial-read-mismatches: max number of mismatches for read alignment
--segment-mismatches: max number of mismatches for split segments
--segment-length: length of each segment in which reads are splitted for alignment to the genome

32. First approach: red way
Completely relies on the available annotation of the Transcriptome

33. Run Cuffdiff
Cuffdiff is one of the Cufflinks sub-packages, for simple Differential Expression Analysis
cd /home/ngs/data_crunching/RNAseq
annotation=/home/ngs/data_crunching/genes/Populus_minusculus.gff3
cuffdiff -o cuffdiff_red/ -L Control,Treated $annotation controlA/tophat/accepted_hits.bam,controlB/tophat/accepted_hits.bam treatedA/tophat/accepted_hits.bam,treatedB/tophat/accepted_hits.bam

34. Analyze results (1) There is more than one file called *.diff
cd cuffdiff_red
head gene_exp.diff
There is more than one file called *.diff
Let’s open the one named “isoform_exp.diff” with LibreOffice Calc
A status “NOTEST” means that for the gene you don’t have enough data. We only can work on genes with “OK”

35. Analyze results (2) How many significantly changed genes do we have?
Sort by q-value (corrected p-value)
The “significant” column has the value “yes” if the p-value of the differential expression is <0.05 after correction for multiple testing
This is the set on which you should focus your interest for downstream analysis such as MapMan

36. Analyze results (3)
Cuffdiff/Cufflinks doesn’t provide raw read counts, but calculates RPKMs
RPKM (Read per kilobase of exon model
per million mapped reads): accounts for both library size and gene length effect
To be even more precise, these are called FPKMs, to account for paired reads (Fragments per kilobase per million mapped pairs of reads)

37. Second approach: green way
Use reads to identify novel junctions and/or transcripts

38. The green way
The first step of the green route is still mapping of reads using TopHat (which in turn relies on Bowtie). We can use the same parameters
Now, we will exploit Cufflinks to predict new transcripts
Cufflinks takes a *.bam format file as input. Authors’ suggest to use the output of TopHat as Cufflink input

39. Run Cufflinks (1) First, we need to index the ALIGNMENTS
Cufflinks requires indexed alignments...
cd /home/ngs/data_crunching/RNAseq
for alldir in controlA controlB treatedA treatedB do
samtools index "$alldir"/tophat/accepted_hits.bam
done

40. Run Cufflinks (2) And then, we run Cufflinks itself:
annotation=/home/ngs/data_crunching/genes/Populus_minusculus.gff3
for alldir in controlA controlB treatedA treatedB do
cufflinks -g $annotation -o "$alldir"/cufflinks "$alldir"/tophat/accepted_hits.bam
done
You can change this –g with –G.
-g: use the annotation to guide assembly (green way)
-G: use the annotation and do not perform assembly (if you use the gff you supplied to tophat, then using –G will be the same as following the red way)

41. Merge assemblies (1)
Now we are comparing not only different mappings, but also different assemblies, each produced by Cufflinks on a different set of reads
In order to compare counts from different cufflinks assemblies/mappings, we need a further merging step
Create a manifest file, that points cuffmerge to the assemblies produced by the two separate Cufflinks runs:
cd /home/ngs/data_crunching/RNAseq
nano manifest.txt
controlA/cufflinks/transcripts.gtf
controlB/cufflinks/transcripts.gtf
treatedA/cufflinks/transcripts.gtf
treatedB/cufflinks/transcripts.gtf
Fill the file with four lines
Ctrl + o (save)
ENTER
Ctrl + x (exit)

42. Merge assemblies (2) Simply run cuffmerge:
cuffmerge -o cuffmerge_dir -g $annotation manifest.txt

43. Run Cuffdiff
Cuffdiff will now work as in the red way: it will just compare comparable gene counts:
cd /home/ngs/data_crunching/RNAseq
newannotation=cuffmerge_dir/merged.gtf
cuffdiff -o cuffdiff_green/ -L Control,Treated $newannotation controlA/tophat/accepted_hits.bam,controlB/tophat/accepted_hits.bam treatedA/tophat/accepted_hits.bam,treatedB/tophat/accepted_hits.bam
This is an annotation generated by cuffmerge: it’s an extension of the default one

44. Analyze results cd cuffdiff_green head gene_exp.diff
How many significantly changed genes do we have now?
Is this identical to the red way?
You can notice that NEW genomic areas previously not known to be transcribed were found by Cufflinks in the green way

45. Conclusions
You now have a list of genes which are differentially expressed between two conditions
You can assess the effects of this treatment by checking who these genes are
In this case, we used a (dummy) dataset from Populus minuscula, a not very studied plant, therefore before using Mapman we must assign a functionto the (known and newly found by Cufflinks) gene sequences
BLAST
Mercator

46. Final slide
And thanks to
Fabio Marroni
who provided the data!

47. CummeRbund is an R package to load and visualize Cuffdiff outputs
Install CummeRbund:
Here, select
«BioC Software» R
setRepositories()
install.packages("cummeRbund")
library("cummeRbund")
Load the cuffdiff "green way" output:
cuff<-readCufflinks(dir="/home/ngs/data_crunching/RNAseq/cuffdiff_green/")
cuff

48. CummeRbund is an R package to load and visualize Cuffdiff outputs
Some plots describing the dataset:
disp<-dispersionPlot(genes(cuff))
disp

49. CummeRbund is an R package to load and visualize Cuffdiff outputs
Some plots describing the dataset:
brep<-csBoxplot(genes(cuff),replicates=T)
brep

50. CummeRbund is an R package to load and visualize Cuffdiff outputs
Some plots describing the dataset:
dens<-csDensity (genes(cuff),replicates=T)
dens

51. CummeRbund is an R package to load and visualize Cuffdiff outputs
Some plots describing the dataset:
s<-csScatter(genes(cuff),“Treated",“Control",smooth=T) s

52. CummeRbund is an R package to load and visualize Cuffdiff outputs
Some plots describing the dataset:
dend<-csDendro(genes(cuff),replicates=T)
dend

53. CummeRbund is an R package to load and visualize Cuffdiff outputs
Some plots describing the dataset:
v<-csVolcano(genes(cuff), "Treated", "Control") v

54. CummeRbund
CummeRbund can do much more... But nothing that R itself can’t do already

55. CummeRbund
CummeRbund can do much more... But nothing that R itself can’t do already

56. Coexpression between transcripts
CummeRbund
CummeRbund can do much more... But nothing that R itself can’t do already
Coexpression between transcripts

57. Final slide (2)