PROMOTER

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Biological MotivationGene Finding in Eukaryotic
Genomes

• Anne R. Haake
• Rhys Price Jones

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Recall from our previous discussion of gene
finding in prokaryotes

• The major strategies in gene finding programs
  are to look for
• Signals/Features
• Content/Composition
• Similarity to known genes (BLAST!)

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3 Major Categories of Information used in Gene
Finding Programs

• Signals/features a sequence pattern with
  functional significance e.g. splice donor
  acceptor sites, start and stop codons, promoter
  features such as TATA boxes, TF binding sites
• Content/composition -statistical properties of
  coding vs. non-coding regions.
• e.g. codon-bias length of ORFs in prokaryotes
  CpG islands GC content
• Similarity-compare DNA sequence to known
  sequences in database
• Not only known proteins but also ESTs, cDNAs

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In Prokaryotic Genomes

• We usually start by looking for an ORF
• A start codon, followed by (usually) at least 60
  amino acid codons before a stop codon occurs
• Or by searching for similarity to a known ORF
• Look for basal signals
• Transcription (the promoter consensus and the
  termination consensus)
• Translation (ribosome binding site the
  Shine-Dalgarno sequence)
• Look for differences in sequence content between
  coding and non-coding DNA
• GC content and codon bias

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The Complicating factors in Eukaryotes

• Interrupted genes (split genes)
• introns and exons
• Large genomes
• Most DNA is non-coding
• introns, regulatory regions, junk DNA (unknown
  function)
• About 3 coding
• Complex regulation of gene expression
• Regulatory sequences may be far away from start
  codon

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Some numbers to consider

• Vertebrate genes average about 30Kb long
• varies a lot
• Coding region is only about 1-2 Kb
• Exon sizes and numbers vary a lot
• Average is 6 exons, each about 150 bp long
• An average 5 UTR is about 750 bp
• An average 3UTR is about 450 bp
• (both can be much longer)
• There are huge deviations from all of these
  numbers
• e.g. dystrophin is 2.4 Mb long factor VIII gene
  has 26 exons, introns are up to 32 Kb (one intron
  produces 2 transcripts unrelated to the gene!)
• There are genes without introns called
  single-exon or intronless genes

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Eukaryotic Gene Structure
www.bio.purdue.edu/courses/biol516/eukgenestructur
e.gif
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Given a long eukaryotic DNA sequence

• How would you determine if it had a gene?
• How would you determine which substrings of the
  sequence contained protein-coding regions?

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• In prokaryotic genomes we usually start by
  looking for ORFs.
• Is this a good approach for the eukaryotic
  genome?
• Why or why not?

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So, whats the problem with looking for ORFs?

• split genes make it difficult to define ORFs
• Where are the stops and stops?
• What problems do introns introduce?
• What would you predict for the size of ORFs?
• (you cant with any certainty!)

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Most Programs Concentrate on Finding Exons

• Exon the region of DNA within a gene that codes
  for a polypeptide chain or domain
• Intron non-coding sequences found in the
  structural genes

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Splice Sites used to Define Exons

• Splice donor (exon-intron boundary) and splice
  acceptor (intron-exon boundary)
• are consensus sequences
• A statistical determination of the
  patternapproximates the pattern
• C(orA)AG/GTA(orG)AGT "donor" splice site
• T(orC)nNC(orT)AG/G "acceptor" splice site

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Gene finding programs look for different types of
exon

• single exon genes begin with start codon end
  with stop codon
• initial exons begin with start codon end with
  donor site
• internal exons begin with acceptor end with
  donor
• terminal exons begin with acceptor end with
  stop codon

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How are correct splice sites identified?

• There are many occurrences of GT or AG within
  introns that are not splice sites
• Statistical profiles of splice sites are used

http//www.lclark.edu/lycan/Bio490/pptpresentatio
ns/mutation/sld016.htm
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Other Biologically Important Signals Used in Gene
Finding Programs

• Transcriptional Signals
• Transcription Start characterized by cap signal
• A single purine (A/G)
• TATA box (promoter) at 25 relative to start
• Polyadenylation signal AATAAA (3 end)
• Major Caveat not all genes have these signals
• Makes it difficult to define the beginning and
  end of a gene

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Upstream Promoter Sites

• Transcription Factor (TF) sites
• Transcription factors are sequence-specific
  DNA-binding proteins
• Bind to consensus DNA sequences
• e.g. CAAT transcription factor and CAAT box
• Many of these
• Vary in sequence, location, interaction with
  other sites
• Further complicates the problem of delineating a
  gene

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Translation Signals

• Kozak sequence
• The signal for initiation of translation in
  vertebrates
• Consensus is GCCACCatgG
• And of course..
• Translation stop codons

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Codon Biasin Eukaryotic Genomes

• Yeast Genome arg specified by AGA 48 of time
  (other five equivalent codons 10 each)
• Fruitfly Genome arg specified by CGC 33 of time
  (other five 13 each)

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GC Content in Eukaryotes

• Overall GC content does not vary between species
  as it does in prokaryotes
• GC content is still important in gene finding
  algorithms
• CpG Islands
• CG dinucleotides occur at low frequency overall
  in the genome
• Exception CpG islands near promoters
• CG dinucleotides occur at level predicted by
  chance
• -1,500 to 500 (relative to transcription start
  site)

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CpG Islands

• Occurrence related to methylation
• Methylation of C in CG dinucleotides
• Methylation of C makes CpG prone to mutation
  (e.g. to TpG or CpA)
• Level of methylation is low in actively
  transcribed genes
• Transcription requires a methyl-free promoter

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Gene Finding Strategies

• Homology-based approach
• Find sequences that are similar to known gene
  sequences
• ab initio-based approach is to identify genes by
  
• Signal sequences
• Composition

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List of Gene Finding Programs

• http//www.hku.hk/bruhk/sggene.html

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Homology-Based Approaches in Eukaryotic Genomes

• More complicated than prokaryotes due to split
  genes
• Genome sequence -gt first identify all candidate
  exons
• Use a spliced alignment algorithm to explore all
  possible exon assemblies compare to known
• e.g. Procrustes
• Limitations
• must have similar sequence in the database with
  known exon structure
• Sensitive to frame shift errors

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Procrustes

• Gene Recognition via spliced alignment
• Given a genomic sequence and a set of candidate
  exons, the spliced alignment algorithm explores
  all possible exon assemblies and finds a chain of
  exons with the best fit to a related target
  protein
• http//hto-13.usc.edu/software/procrustes/salign

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GenScan

• Allows integration of multiple types of
  information
• Earlier programs considered features of gene
  structure in isolation
• Uses a generalized HMM (one state might use a
  weight matrix model, another an HMM)
• http//genes.mit.edu/GENSCAN.html

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GenScan

• Probabilistic Model of Genes
• Accounts for many of the known structural
  compositional properties of genes including
• typical gene density
• typical number of exons per gene
• distribution of exon sizes for different types of
  exon
• compositional properties of coding vs. non-coding
• translation initiation (Kozak)
• termination signals
• TATA box, cap site and poly-adenylation signals
• donor and acceptor splice sites

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GenScan

• Uses as a training set 238 multi-exon genes and
  142 single-exon genes from GenBank to compute
  parameters
• Initial state probabilities
• Transition probabilities
• State length distributions

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GenScan

• Probabilistic models for the states
• The states correspond to different functional
  units on a gene e.g promoter regions, exon
• Transitions ensure that the order that the model
  marches through the states is biologically
  consistent
• Length distributions take into account that
  different functional units have different lengths.

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GenScan

• Signal models used by GenScan
• - WMM weight matrix model
• for transcriptional and translational signals
  (translation initiation, polyadenylation signals,
  TATA box etc.) e.g. polyadenylation signal
  is modeled as a 6 bp WMM with AATAAA as the
  consensus sequence
• -WAM weight array model assumes some
  dependencies between adjacent positions in the
  sequence
• e.g. used for the pyrimidine-rich region and
  the splice acceptor site
• -Maximal dependency decomposition
• e.g. used for donor splice sites

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GenScan

• does not use similarity search
• uses double stranded genomic sequence model
• potential genes on both strands are analysed
  simultaneously
• Limitations
• cannot handle overlapping transcription unit
• does not address alternative splicing

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GRAIL

• GRAIL (Gene Recognition and Assembly Internet
  Link)
• uses a number of sensor algorithms to evaluate
  coding potential of a DNA sequence
• features include 6-mer composition, GC
  composition and splice junction recognition
• the output of the sensor algorithms is input to
  a neural network, which uses empirical data for
  training.

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GRAIL-exp

• http//compbio.ornl.gov/grailexp/gxpfaq1.html