Eukaryotic Gene Finding

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

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Pre-mRNA Splicing
exon definition
intron definition
...
(assembly of
spliceosome,
catalysis)
...
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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.

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Human Splice Signal Motifs
5' splice signal
3' splice signal
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Semi-Markov HMM Model
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GHMM

• A finite Set Q of states
• Initial state distribution ?
• Transition probabilities Ti,j for
• Length distribution f of the states (fq is the
  length distribution of state q)
• Probability model for each state

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GHMM contin.

• A parse ? of a sequence S of length L is an
  ordered sequence of states (q1, . . . , qt) with
  an associated duration di to each state

The most probable pass ?opt can be computed as in
Veterbi algorithm
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Genscan HSMM
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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

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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
• Complete intron/exon annotation for sequence
• Able to predict multiple genes and partial/whole
  genes
• Parameters learned from annotated genes
• Separate parameter training for different CpG
  content groups (lt 43, 43-51, 51-57,gt57 CG
  content)

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Various parameters in GENSCAN
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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

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GENSCAN Performance

• gt 80 correct exon predictions, and gt 90 correct
  coding/non coding predictions by bp.
• BUT - the ability to predict the whole gene
  correctly is much lower

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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)

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Using Sequence Similarity for Gene Finding

1. Compare genomic sequence with expressed sequence
   tags (ESTs) (e.g. by BLASTN), to identify regions
   corresponding to processed mRNA
2. Compare genomic sequence to Protein DB (e.g. by
   BLASTX), to identify probably coding regions
3. Spliced Alignment of genomic sequence of a
   complete gene with a homologous protein sequence
   (e.g. by PROCRUSTES) may enable exon/intron
   reconstruction
4. Compare predicted peptides (e.g. by GENSCAN) with
   protein DB to assign confidence to predictions
   and functional annotations
5. Compare Genomic sequence with homologous from
   close organisms/species (e.g. by BLAST, CLASTW),
   to identify conserved regions which might
   correspond to coding regions and DNA signals

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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.

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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

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Sample GeneWise Output
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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

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GeneWise Model
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GeneWise Intron Model
5 site
3 site
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GeneWise Model

• Viterbi algorithm -gt best alignment of DNA to
  protein domain
• Alignment gives exact exon-intron boundaries
• Parameters learned from species-specific
  statistics

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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

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Other Sequence Usage

• Search translated genomic sequences for the
  occurrences of the shot peptide motifs that are
  characteristic of common protein families (e.g.
  zinc finger, ATP/GTP binding modifs etc.)
• Identify sequences which are probably NON coding
  identify known classes of interspersed repeates
  (e.g LINE SINE) in none coding regions. Can be
  essential to remove these before simple BLAST is
  done against ESTs.

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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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Future Directions

• Find genes not for proteins (tRNA, rRNA, smRNA)
  hard !
• Deal better with overlapping genes, multiple
  genes in a single sequence
• Alternative splicing/transcription/translation
  a whole separate issue
• The mechanisms governing it, the signals
• predicting the various genes
• Very important !