Gene Prediction

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Chapter 8 Gene Prediction
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• Automated sequencing of genomes require automated
  gene assignment
• Includes detection of open reading frames (ORFs)
• Identification of the introns and exons
• Gene prediction a very difficult problem in
  pattern recognition
• Coding regions generally do not have conserved
  sequences
• Much progress made with prokaryotic gene
  prediction
• Eukaryotic genes more difficult to predict
  correctly

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• Ab initio methods
• Predict genes on given sequence alone
• Uses gene signals
• Start/stop codon
• Intron splice sites
• Transcription factor binding sitesribosomal
  binding sites
• Poly-A sites
• Codon demand multiple of three nucleotides
• Gene content
• Nucleotide composition use HMMs
• Homology based methods
• Matches to known genes
• Matches to cDNA
• Consensus based
• Uses output from more than one program

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• Prokaryotic gene structure
• ATG (GTG or TTG less frequent) is start codon
• Ribosome binding site (Shine-Dalgarno sequence)
  complementary to 16S rRNA of ribosome
• AGGAGGT
• TAG stop codon
• Transcription termination site (?-independent
  termination)
• Stem-loop secondary structure followed by string
  of Ts

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• Translate sequence into 6 reading frames
• Stop codon randomly every 20 codons
• Look for frame longer that 30 codons (normally
  50-60 codons)
• Presence of start codon and Shine-Dalgarno
  sequence
• Translate putative ORF into protein, and search
  databases
• Non-randomness of 3rd base of codon, more
  frequently G/C
• Plotting wobble base GC can identify ORFs
• 3rd base also repeats, thus repetition gives clue
  on gene location

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• Markov chains and HMMs
• Order depends on k previous positions
• The higher the order of a Markov model to
  describe a gene, the more non-randomness the
  model includes
• Genes described in codons or hexamers
• HMMs trained with known genes
• Codon pairs are often found, thus 6 nucleotide
  patterns often occur in ORFs 5th-order Markov
  chain
• 5th-order HMM gives very accurate gene
  predictions
• Problem may be that in short genes there are not
  enough hexamers
• Interpolated Markov Model (IMM) samples different
  length Markov chains
• Weighing scheme places less weight on rare k-mers
• Final probability is the probability of all
  weighted k-mers
• Typical and atypical genes

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GeneMark (http//exon.gatech.edu/genemark/) Traine
d on complete microbial genomes Most closely
related organism used for predictions Glimmer
(Gene Locator and Interpolation Markov
Model) (http//www.cbcb.umd.edu/software/glimmer/)
FGENESB (http//linux1.softberry.com/) 5th-order
HMM Trained with bacterial sequences Linear
discriminant analysis (LDA) RBSFinder
(ftp//ftp.tigr.org )Takes output from Glimmer
and searches for S-D sequences close to start
sites
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• Performance evaluation
• Sensitivity Sn TP/(TPFN)
• Specificity Sp TP/(TPFP)
• CCTP.TN-FP.FN/(TPFPTNFNTPTN)1/2

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Gene prediction in Eukaryotes Low gene density
(3 in humans) Space between genes very large
with multiply repeated sequences and transposable
elements Eukaryotic genes are split
(introns/exons) Transcript is capped (methylation
of 5 residue) Splicing in spliceosome Alternative
splicing Poly adenylation (250 As added)
downstream of CAATAAA(T/C) consensus box Major
issue identification of splicing sites GT-AG rule
(GTAAGT/ Y12NCAG 5/3 intron splice
junctions) Codon use frequencies ATG start
codon Kozak sequence (CCGCCATGG)
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• Ab initio programs
• Gene signals
• Start/stop
• Putative splice signals
• Consensus sequences
• Poly-A sites
• Gene content
• Coding statistics
• Non-random nucleotide distributions
• Hexamer frequencies
• HMMs

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• Discriminant analysis
• Plot 2D graph of coding length versus 3 splice
  site
• Place diagonal line (LDA) that separates true
  coding from non-coding sequences based on learnt
  knowledge
• QDA fits quadratic curve
• FGENES uses LDA
• MZEF(Michael Zangs Exon Finder uses QDA)

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• Neural Nets
• A series of input, hidden and output layers
• Gene structure information is fed to input layer,
  and is separated into several classes
• Hexamer frequencies
• splice sites
• GC composition
• Weights are calculated in the hidden layer to
  generate output of exon
• When input layer is challenged with new sequence,
  the rules that was generated to output exon is
  applied to new sequence

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• HHMs
• GenScan (http//genes.mit.edu/GENSCAN.html)5th-or
  der HMM
• Combined hexamer frequencies with coding signals
• Initiation codons
• TATA boxes
• CAP site
• Poly-A
• Trained on Arabidopsis and maize data
• Extensively used in human genome project
• HMMgene (http//www.cbs.dtu.dk/services/HMMgene)
• Identified sub regions of exons from cDNA or
  proteins
• Locks such regions and used HMM extension into
  neighboring regions

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• Homology based programs
• Uses translations to search for EST, cDNA and
  proteins in databases
• GenomeScan (http//genes.mit.edu/genomescan.html)
• Combined GENSCAN with BLASTX
• EST2Genome (http//bioweb.pasteur.fr/seqanal/inter
  faces/est2genome.html)
• Compares EST and cDNA to user sequence
• TwinScan
• Similar to GenomeScan

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• Consensus-based programs
• Uses several different programs to generate lists
  of predicted exons
• Only common predicted exons are retained
• GeneComber (http//www.bioinformatics.ubc.ca/genco
  mbver/index.php)
• Combined HMMgene with GenScan
• DIGIT (http//digit.gsc.riken.go.jp/cgi-bin/index.
  cgi)
• Combines FGENESH, GENSCAN and HMMgene

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Accuracy
Nucleotide Level Nucleotide Level Nucleotide Level Exon Level Exon Level Exon Level Exon Level Exon Level
Sn Sp CC Sn Sp (SnSp)/2 ME WE
FGENES 0.86 0.88 0.83 0.67 0.67 0.67 0.12 0.09
GeneMark 0.87 0.89 0.83 0.53 0.54 0.54 0.13 0.11
Genie 0.91 0.90 0.88 0.71 0.70 0.71 0.19 0.11
GenScAN 0.95 0.90 0.91 0.71 0.70 0.70 0.08 0.09
HMMgene 0.93 0.93 0.91 0.76 0.77 0.76 0.12 0.07
Morgan 0.75 0.74 0.74 0,.46 0.41 0.43 0.20 0.28
MZEF 0.70 0.73 0.66 0.58 0.59 0.59 0.32 0.23
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Chapter 9 Promoter and regulatory element
prediction
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• Promoters are short regions upstream of
  transcription start site
• Contains short (6-8nt) transcription factor
  recognition site
• Extremely laborious to define by experiment
• Sequence is not translated into protein, so no
  homology matching is possible
• Each promoter is unique with a unique combination
  of factor binding sites thus no consensus
  promoter

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Prokaryotic gene
TF site
polymerase
TF
ORF
-35 box
-10 box

• ?70 factor binds to -35 and -10 boxes and recruit
  full polymerase enzyme
• -35 box consensus sequence TTGACA
• -10 box consensus sequence TATAAT
• Transcription factors that activate or repress
  transcription
• Bind to regulatory elements
• DNA loops to allow long-distance interactions

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Eukaryotic gene structure
TF site
Pol II
TF site
TATA
Inr
Polymerase I, II and III Basal transcription
factors (TFIID, TFIIA, TFIIB, etc.) TATA box
(TATA(A/T)A(A/T) Housekeeping genes often do
not contain TATA boxes Initiatior site (Inr)
(C/T) (C/T) CA(C/T) (C/T) coincides with
transcription start Many TF sites Activation/repre
ssion
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• Ab initio methods
• Promoter signals
• TATA boxes
• Hexamer frequencies
• Consensus sequence matching
• PSSM
• Numerous FPs
• HMMs incorporate neighboring information

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• Promoter prediction in prokaryotes
• Find operon
• Upstream offirst gene is promoter
• Wang rules (distance between genes, no
  ?-independent termination, number of genomes that
  display linkage)
• BPROM (http//www.softberry.com)
• Based of arbitarry setting of operon egen
  distances
• 200bop uopstream of first gene
• many FPs
• FindTerm (http//sun1.softberry.com)
• Searches for ?-independent termination signals

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Prediction in eukaryotes

• Searching for consensus sequences in databases
  (TransFac)
• Increase specuificity by searching for CpG
  islands
• High density fo trasncription factor binding
  sitres
• CpGProD (http//pbil.univ-lyon1.fr/software/cpgpro
  d.html)
• CG inmoving window
• Eponine (http//servlet.sanger.ac.uk8080/eponine/
  )
• Matches TATA box, CCAAT bvox, CpG island to PSSM
• Cluster-Buster (http//zlab.bu.edu/cluster-buster/
  cbust.html)
• Detects high concentrations of TF sites
• FirstEF (http//rulai.cshl.org/tools/FirstEF/)
• QDA of fisrt exonboundary
• McPromoter (http//genes.mit.edu/McPromoter.html)
• Neural net of DNA bendability, TAT box,initator
  box
• Trained for Drosophila and human sequences

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Phylogenetic footprinting technique

• Identify conserved regulatory sites
• Human-chimpanzee too close
• Human fish too distant
• Human0-mouse appropriate
• ConSite (http//mordor.cgb.ki.se/cgi-bin/CONSITE/c
  onsite)
• Align two sequences by global alignment
  algorithm
• Identify conserved regions and compare to
  TRANSFAC database
• High scoring hits returned as positives
• rVISTA (http//rvista.dcode.org)
• Identified TRANSFAC sites in two orthologous
  sequences
• Aligns sequences with local alignment algorithm
• Highest identity regions returned as hits
• Bayes aligner (http//www.bioinfo.rpi.edu/applicat
  ions/bayesian/bayes/bayes.align12.pl)
• Aligns two sequences with Bayesian algorithm
• Even weakly conserved regions identified

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Expression-profiling based method
Microarray analyses allows identification of
co-regulated genes Assume that promoters contain
similar regulatory sites Find such sites by EM
and Gibbs sampling using iteration of
PSSM Co-expressed genes may be regulated at
higher levels MEME (http//meme.sdsc.edu/meme/webs
ite/meme-intro.html) AlignACE (http//atlas.med.ha
rvard.edu/cgi-bin/alignace.pl) Gibbs sampling
algorithm
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Web humour