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
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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