Alternative Splicing from ESTs
Eduardo EyrasBioinformatics UPF – February 2004
Intro
ESTs
Prediction of Alternative Splicing from ESTs
AAAAAAA5’ CAPMature mRNA
Splicing
5’
3’
3’
5’
pre-mRNA
Transcriptionexons
introns
Translation
Peptide
AAAAAAA5’ CAPMature mRNA
Different Splicing
5’
3’
3’
5’
pre-mRNA
Transcriptionexons
introns
Translation
Different Peptide
Alt splicing as a mechanism of gene regulation
Functional domains can be added/subtracted protein diversity
Can introduce early stop codons, resulting in truncated proteins or unstable mRNAs
It can modify the activity of the transcription factors, affecting the expression of genes
It is observed nearly in all metazoans
Estimated to occur in 30%-60% of human
Forms of alternative splicing
Exon skipping / inclusion
Alternative 3’ splice site
Alternative 5’ splice site
Mutually exclusive exons
Intron retention
Constitutive exon Alternatively spliced exons
How to study alternative splicing?
ESTs (Expressed Sequence Tags)
Single-pass sequencing of a small (end) piece of cDNA
Typically 200-500 nucleotides long
It may contain coding and/or non-coding region
ESTsCells from a specific organ, tissue or developmental stage
AAAAAA 3’5’
AAAAAA 3’5’
TTTTTT5’3’
AAAAAA 3’5’
TTTTTT5’3’
TTTTTT5’3’
AAAAAA 3’5’
TTTTTT5’3’
mRNA extraction
RNA
DNA
Double stranded cDNA
Add oligo-dT primer
Reverse transcriptase
Ribonuclease H
DNA polimerase Ribonuclease H
ESTs
AAAAAA 3’5’
TTTTTT5’3’Clone cDNA into a vector
Multiple cDNA clones5’ EST
3’ EST
Single-pass sequence reads
Splice variants
Genomic
Primary transcript
Splicing
cDNA clones(double stranded)
EST sequences (Single-pass sequence reads) 5’ 3’ 5’ 3’
Sampling the Transcriptome with ESTs
oligo-dT primer
Reverse transcriptase
Large scale EST-sequencing coupled to Genome sequencing
EST sequencing
Is fast and cheap
Gives direct information about the gene sequence
Partial information
Resulting ESTs Known gene(DB searches) Similar to known gene
ContaminantNovel gene
Number of public entries: 20,039,613
Summary by organism
Homo sapiens (human) 5,472,005Mus musculus + domesticus (mouse) 4,056,481Rattus sp. (rat) 583,841Triticum aestivum (wheat) 549,926Ciona intestinalis 492,511Gallus gallus (chicken) 460,385Danio rerio (zebrafish) 450,652Zea mays (maize) 391,417Xenopus laevis (African clawed frog) 359,901…
dbEST release 20 February 2004
EST lengths
Human EST length distribution (dbEST Sep. 2003 )
~ 450 bp
ESTs provide expression data
eVOC Ontologies http://www.sanbi.ac.za/evoc/
Anatomical System
Cell Type
The tissue, organ or anatomical system from which the sample was prepared. Examples are digestive, lung and retina.
Pathology
The precise cell type from which a sample was prepared. Examples are: B-lymphocyte, fibroblast and oocyte.
Developmental Stage
The pathological state of the sample from which the sample was prepared.Examples are: normal, lymphoma, and congenital.
Pooling
The stage during the organism's development at which the sample was prepared. Examples are: embryo, fetus, and adult.
Indicates whether the tissue used to prepare the library was derived from single or multiple samples. Examples are pooled, pooled donor and pooled tissue.
J Kelso et al. Genome Research 2002
ESTs provide expression data
eVOC Ontologies http://www.sanbi.ac.za/evoc/
Anatomical System
Cell Type Pathology Developmental Stage Pooling
…nervous
brain cerebellum …
Library 1 Library 2 …
ESTs ESTs
Linking the expression vocabulary to gene annotations
ESTs
GenesV Curwen et al. Genome Research (2004)
Gene expression vocabulary
Normalized vs. non-normalized libraries
The down side of the ESTs
Cannot detect lowly/rarely expressed genes or non-expressed sequences (regulatory)
Random sampling: the more ESTs we sequence the less new useful sequences we will get
Using ESTs to study Alternative Splicing
ESTs aligned to the genome
EST
True matchbest in genome
ParalogProcessed
pseudogene
GT AGPolyA
It defines the location of exons and intronsWe can verify the splice sites of introns check the correct strand of spliced ESTsIt helps preventing chimerasIt can avoid putting together ESTs from paralogous genesWe can prevent including pseudogenes in our analysis
*Stop
Must Clip poly A tails before aligning
Alternative Exons/ 3´ PolyA sites from ESTs
ESTs can also provide information about potential alternative splicing when aligned to the genome (and when aligned to mRNA data)
Aligning ESTs to the Genome
Many ESTs Fast programs, Fast computers
Nearly exact matches Coverage >= 97%Percent_id >= 97%
Splice sites: GT—AG, AT—AC, GC—AG
Genomics as a Technology
Development of special software:fast versus accurate alignment
Development of special technology:efficient use of computer farms (~2000 CPUs)
Recovering full transcripts from ESTs
Recover the mRNA from the ESTs
The Problem
What are the transcripts represented in this set of mapped ESTs?
ESTs
Genome
Transcript predictions
ESTs
Predict Transcripts from ESTs
Merge ESTs according to splicing structure compatibility
Redundant ESTsConsider 2 ESTs in a Genomic Cluster with more ESTS
xz
z gives redundant splicing information, we could keep only x x
zw
However, the relation with other ESTs in the cluster is important: a third EST, w, is compatible with z but not with x.--> keep all relations
x + z
x + zz + w
Extension of the exon structureConsider 2 ESTs in a Genomic Cluster with more ESTS
xy
y extends x, we can assume that they are from the same mRNA
xzw
Our success will depend on the coverage of the exons.However, ESTs are 3’and 5’ biased (ESTs like z not so frequent), hence we will have fragmentation.
x + y
Representation
Extension
Inclusion zx
y
x
For every 2 ESTs in a Genomic Cluster, we decide if they represent equivalent splicing structures
The compatibility relation is a graph:
xy
xz
E Eyras et al. Genome Research (2004)
Criteria of “merging”
Allow internal mismatches
Allow intron mismatches
Allow edge-exon mismatches
mismatches
Is this intron real?
Transitivity
Extension
Inclusion wz
y
x
w
x
This reduces the number of comparisons needed
xyz
xzw
ClusterMerge graph
z
x
x
y
y
z
w
Each node defines an inclusion sub-tree
Extensions form acyclic graphs
yxz
xyzw
E Eyras et al. Genome Research (2004)
Mergeable sets
1
32
4
65
Example
7
Mergeable sets
1
32
4
65
Example
7
1
4
2
6
5
3
7
Mergeable sets
1
32
4
65
Example
7
1
4
2
6
5
3
7
Leaves
Root
Mergeable sets
1
32
4
65
Example
7
1
4
2
6
5
3
7
Lists produced: (1,2,3,5,6,7) ( 1,2,3,4,5,7)
Leaves
Root
Deriving the transcripts from the lists
Internal Splice Sites: external coordinates of the 5’ and 3’ exons are not allowed to contribute
Deriving the transcripts from the lists
Splice Sites: are set to the most common coordinate
5’ and 3’ coordinates: are set to the exon coordinate that extends the potential UTR the most
Single exon transcripts
Reject resulting single exon transcripts when using ESTs
Alternative splicing and comparative genomics
Conservation of Alternative Splicing
Degree of conservation: 30-60%
Methods:
1.- compare single events
2.- Cross-alignment of full transcripts
Exon Skipping Events
Introns flanking alternatively spliced (skipped) exons have high sequence conservation.Higher on average than constitutive inrons.
R Sorek & G Ast. Genome Research 13:1631-1637, 2003
Sequences regulating the (Alternative) splicing
Overrepresented sequences in conserved introns (between human and mouse) may beInvolved in the regulation of alternative splicing.
Overrepresented: found in these introns more often than expected at random AND not foundin intronic sequences flanking constitutive exons (and upstream of skipped ones)
R Sorek & G Ast. Genome Research (2003) 13:1631-1637
ConservedAlternative
ExonFlankingIntrons
Overrepresented hexamer (downstream)
Sequences regulating the (Alternative) splicing
Not all types of events are equally conserved.Introns flanking alternative 5´and 3´exons, and retained introns, have higher sequence conservation.
Sugnet CW, Kent WJ, Ares M Jr, Haussler D. Pac Symp Biocomput. 2004;:66-77
ConservedAlternative
ExonFlankingIntrons
Overrepresented hexamer
Frame preservation
Frame preserving Constitutive exons Alternative exons
All exons 39.7% (Human)39.5% (Mouse)
41.6% (Human)44.7% (Mouse)
ConservedExon
40.9% (Human)38% (Mouse)
51.8% (Human)51.9% (Mouse)
A Resch et al. Nucleic Acids Research 2004, 32 (4) 1261-1269
Predicting alternative exons
Features Differentiating Between Alternatively splice and Constitutively spliced exons
Alternative exons
Constitutive exons
Average size 87 128
length = mutliple of 3 73% 37%
Average human-mouse exon conservation 94% 89%
(A) Exons with upstream intron conserved in mouse
92% 45%
(B) Exons with downstream intron conserved in mouse
82% 35%
(A) + (B) 77% 17%
R Sorek et al. Genome Research (2004) 14:1617-1623
(A), (B) : conservation is considered if at least there 12 consecutive matches over 100bp of the intron
Build a classifier to make predictions
• Rule: Set of conditions over the parameters:
e.g. “at least 99% conservation with mouse AND divisible by 3, etc…”
• Try all the possible combinations of parameters
• Select the rule that would correctly identify a maximum number of true
alternative exons minimizing the number of false positives
At least 95% identity with mouse orthologous exon
Exon size is a multiple of 3
An upstream intronic alignment of at least 15bp with at least 85% identity
A downstream intronic exact alignment of at least 12bp
R Sorek et al. Genome Research (2004) 14:1617-1623
This rule achieved 31% sensitivity and no false positives in a set of known exons:
SummaryAlternative splicing is a mechanism to generate function diversity
We can study alternative splicing using ESTs (Expressed Sequence Tags)
EST data is fragmented and full of noise: need to be processed
Some alternative splicing is conserved across species (Human-Mouse)
Prediction of alternative (conserved) exons is possible (a classifier) but no ab initio
Evolution of alternative splicing?
THE END