1. Transcriptome analysis
Edouard Severing

2. Overview Introduction: Transcriptome complexity
Transcriptome reconstruction
Without a genome
With a genome
Transcript abundances
Differential expression
Transcript abundances models
(Maximum likelihood)

3. Gene-expression/Phenotypes
What are the gene expression differences that underly these phenotypic differences?
Gene expression measured by assessing the abundance of mRNA molecules

4. Transcriptome vs. genome
Initial assumption N
mRNA
Molecules N
Proteins N
Protein coding
genes
Assumption is based on studies that were performed on bacterial systems

5. Complexity and gene count
genes
genes

6. Transcriptome vs. genes in eukaryotes
Current view
X N
mRNA
Molecules
? N
Proteins
What happens here ? N
Protein coding
genes

7. Splicing Splicing 5’- -3’ 5’- -3’ 5’- -3’ Pre-mRNA Exon Intron Exon
Gene

8. Alternative splicing II (Alternative splicing)
Pre-mRNA
5’-
-3’
5’-
-3’
Splicing
5’-
-3’
5’-
-3’
Splicing

9. Complexity and AS 90% genes have AS 42% genes have AS
The average number of transcripts produced by human genes is
also higher than the average number of transcripts produced by plant genes

10. Extremes
Dscam gene produces over 35,000 transcripts

11. AS type difference Humans Plants
In humans exon skipping is most frequent AS event type
In plants intron retention are the most common AS event type
Humans
Exon skipping
Plants
Intron retention

12. RNA editing (Base modification)
Primary transcript
(Predicted sequence) C U C
5’- A G
U - 3’ A
RNA-Editing
After editing
(Observed sequence) A C U U
5’- A G
U - 3’ A
Difficulty: Distinguish genuine RNA-editing from sequencing errors

13. Translation or decay
A large fraction (>30%) of transcripts of protein coding genes are degraded by the nonsense-mediated decay (NMD) pathway.
The position of the stop codon is used to predict whether a transcript is likely to be degraded by the NMD pathway

14. NMD target prediction Pre-mRNA 5’- -3’ Exon/Exon junctions mRNA 5’-
Open reading frame M
5’-
-3’
Stop d
Transcripts containing a Stop codon more than 55 nt upstream of the last exon/exon
junction are predicted to be targets for the NMD-pathway.

15. Remember
The number of unique mRNA molecules is much larger than the number of genes.
A large fraction of the mRNA molecules is degraded by the NMD pathway.
NMD provides a means to regulate gene-expression at the post-transcriptional level

16. Transcriptome analysis.
Reconstruction of the expressed transcripts given the sequencing data (Fragmented).
Without a reference genome
Trinity, TransABySS and Velvet
With a reference genome
Cufflinks, Scripture
Determining the relative abundances of the predicted transcripts (cufflinks)
Differential analysis (cufflinks)
Gene-expression
Alternative splicing

17. Without genome I

18. Without genome II

19. With a genome (Spliced alignment)
5’-
-3’
mRNA

20. With a genome

21. With Genome II

22. Assignment Transcriptome reconstruction
Mapping of reads to the genome using tophat
Reconstruction of the transcriptome using cufflinks
Blast analysis of the assembly result

23. Your login
barshap berryk cizara dennisv dirkv dunyac giorgiot heleenw hildam ioannism jitskel joelk kamleshs leilas luigif mushtar patricial peterve roberte seyeda taox tristanj weic xiaoxues yanickh
allemaal hetzelfde pw: wvdABcv12

24. Change password ssh <yourlogin>@137.224.100.201 passwd Exit
Enter your password
Change it to new password
Type new password again
Exit

25. Details ssh –X <yourlogin>@137.224.100.212
cd /mnt/geninf15/work/bif_course_2012
assignments are in assignment.txt

26. Estimating Expression levels
Would be easy if only full length transcripts were recovered.
However, we have transcript fragments.
Simply counting the number of reads mapping to a gene or transcript is not good enough (Normalization is needed)
The number of fragments that can be produced from a transcript not only depends its abundance but also its length.

27. Expression levels
FPKM is analogous to RPKM
One fragment
One read

28. Back to gene level expression (I)

29. Back to gene level expression (II)

30. Differential expression analysis
A genes is differentially expressed under two conditions if its expression difference
is statistically significant. Larger that you would expect based random natural
variation
- In order to estimate the variance it is important to have experimental replicates .
(Variation between biological replicates is larger than that between technical replicates).

31. Expression assignment
Estimate the expression levels of predicted transcripts / genes in Arabidopsis roots and flower buds. (Cufflinks)
Differential expression analysis of transcript abundances in Arabidopsis roots and flower buds (Cuffdiff)