Eukaryotic Gene Finding
Adapted in part from http://online.itp.ucsb.edu/online/infobio01/burge/
Prokaryotic vs. Eukaryotic Genes
Prokaryotessmall genomes
high gene density
no introns (or splicing)
no RNA processing
similar promoters
overlapping genes
Eukaryoteslarge genomes
low gene density
introns (splicing)
RNA processing
heterogeneous promoters
polyadenylation
exonic enhancers
5’ splice signal 3’ splice signalpolyY
branch signal
intronic enhancers
exonic repressor
U2A
F6
5
U2A
F3
5
U1
snRN
P
SR proteins
U1
snRN
P
U2
snRN
Pintronic repressor
5’ splice signal
exon definitionintron definition
Pre-mRNA Splicing
...
...
(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.
5' splice signal
3' splice signal
Human Splice Signal Motifs
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
• 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.
• 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.
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
)|()|()|( ii MaaPaacodonPMcodonP
•Allow for sequencing errors (deletions/insertions)•Add a 3-state intron model
GeneWise Model
GeneWise Intron Model
central
PY tract
spacer
5’ site 3’ site
GeneWise Model
• Viterbi algorithm -> “best” alignment of DNA to protein domain
• Alignment gives exact exon-intron boundaries
• Parameters learned from species-specific 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
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