Eukaryotic Gene Finding

1
Eukaryotic Gene Finding
Adapted in part from http//online.itp.ucsb.edu/on
line/infobio01/burge/
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

3
(No Transcript)
4
Pre-mRNA Splicing
exon definition
intron definition
...
(assembly of
spliceosome,
catalysis)
...
5
(No Transcript)
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 3UTR is about 450 bp but both can be
  much longer.

7
Human Splice Signal Motifs
5' splice signal
3' splice signal
8
(No Transcript)
9
(No Transcript)
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 -gt stop codon)
• Einit initial exon (translation start -gt donor
  splice site)
• Ek phase k internal exon (acceptor splice site
  -gt donor splice site)
• Eterm terminal exon (acceptor splice site -gt
  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

14
(No Transcript)
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(S2S1)Pn(SnSn-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

• 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.

18
(No Transcript)
19
(No Transcript)
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
5 site
3 site
25
GeneWise Model

• Viterbi algorithm -gt 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