1. Eukaryotic Gene Finding
Adapted in part from

2. Prokaryotic vs. Eukaryotic Genes
Prokaryotes
small genomes
high gene density
no introns (or splicing)
no RNA processing
similar promoters
overlapping genes
Eukaryotes
large genomes
low gene density
introns (splicing)
RNA processing
heterogeneous promoters
polyadenylation

4. Pre-mRNA Splicing ... ... U 1 s n R N P 2 intronic repressor 5 ’
splice signal U 2 A F 6 5 3 1 s n R N P
SR proteins
intron definition
exon definition
exonic enhancers 5 ’
splice signal 3
polyY
branch signal
intronic enhancers
exonic repressor
...
(assembly of
spliceosome,
catalysis)
...

6. Some Statistics On average, a vertebrate gene is about 30KB long
Coding region takes about 1KB
Exon sizes can vary from double digit numbers to kilobases
An average 5’ UTR is about 750 bp
An average 3’UTR is about 450 bp but both can be much longer.

7. Human Splice Signal Motifs

10. Semi-Markov HMM Model

11. Genscan HSMM

12. GenScan States N - intergenic region P - promoter
F - 5’ untranslated region
Esngl – single exon (intronless) (translation start -> stop codon)
Einit – initial exon (translation start -> donor splice site)
Ek – phase k internal exon (acceptor splice site -> donor splice site)
Eterm – terminal exon (acceptor splice site -> stop codon)
Ik – phase k intron: 0 – between codons; 1 – after the first base of a codon; 2 – after the second base of a codon

13. GenScan features Model both strands at once
Each state may output a string of symbols (according to some probability distribution).
Explicit intron/exon length modeling
Advanced splice site modeling
Parameters learned from annotated genes
Separate parameter training for different CpG content groups

15. GenScan Signal Modeling
PSSM: P(S) = P1(S1)•P2(S2) •…•Pn(Sn)
PolyA signal
Translation initiation/termination signal
Promoters
WAM: P(S) = P1(S1) •P2(S2|S1)•…•Pn(Sn|Sn-1)
5’ and 3’ splice sites

16. HMM-based Gene Finding
GENSCAN (Burge 1997)
FGENESH (Solovyev 1997)
HMMgene (Krogh 1997)
GENIE (Kulp 1996)
GENMARK (Borodovsky & McIninch 1993)
VEIL (Henderson, Salzberg, & Fasman 1997)

17. GenomeScan proteins are available.
Idea: We can enhance our gene prediction by using external information: DNA regions with homology to known proteins are more likely to be coding exons.
Combine probabilistic ‘extrinsic’ information (BLAST hits) with a probabilistic model of gene structure/composition (GenScan)
Focus on ‘typical case’ when homologous but not identical
proteins are available.

20. GeneWise [Birney, Amitai]
Motivation: Use good DB of protein world (PFAM) to help us annotate genomic DNA
GeneWise algorithm aligns a profile HMM directly to the DNA

21. Sample GeneWise Output

22. Developing GeneWise Model
Start with a PFAM domain HMM
Replace AA emissions with codon emissions
Allow for sequencing errors (deletions/insertions)
Add a 3-state intron model

23. GeneWise Model

24. GeneWise Intron Model
PY tract
central
spacer
5’ site
3’ site

25. GeneWise Model
Viterbi algorithm -> “best” alignment of DNA to protein domain
Alignment gives exact exon-intron boundaries
Parameters learned from species-specific statistics

26. GeneWise problems
Only provides partial prediction, and only where the homology lies
Does not find “more” genes
Pseudogenes, Retrotransposons picked up
CPU intensive
Solution: Pre-filter with BLAST

27. Summary
Genes are complex structures which are difficult to predict with the required level of accuracy/confidence
Different approaches to gene finding:
Ab Initio : GenScan
Ab Initio modified by BLAST homologies: GenomeScan
Homology guided: GeneWise