Download Eukaryotic Gene Finding

Eukaryotic Gene Finding
Adapted in part from
http://online.itp.ucsb.edu/online/infobio01/burge/
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
Pre-mRNA Splicing
exon definition
intron definition
SR proteins
...
5 ’ splice signal
exonic repressor
branch signal
intronic enhancers
3 ’ splice signal
5 ’ splice signal
polyY
exonic enhancers
intronic repressor
(assembly of
spliceosome, catalysis)
...
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.
Human Splice Signal Motifs
5' splice signal
3' splice signal
Semi-Markov HMM Model
Genscan HSMM
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
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
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
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)
GenomeScan
• 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.
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
Sample GeneWise Output
Developing GeneWise Model
• Start with a PFAM domain HMM
• Replace AA emissions with codon
emissions
P(codon | Mi )   P(codon | aa)P(aa | Mi )
•Allow for sequencing errors
(deletions/insertions)
•Add a 3-state intron model
GeneWise Model
GeneWise Intron Model
PY tract
central
5’ site
spacer
3’ site
GeneWise Model
• Viterbi algorithm -> “best” alignment of
DNA to protein domain
• Alignment gives exact exon-intron
boundaries
• Parameters learned from speciesspecific statistics
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
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