GENE PREDICTION

VIJAY

JRF

GIT,Bengaluru

•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

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

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

•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

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

GeneMark (http://exon.gatech.edu/genemark/) Trained 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

Performance evaluation •Sensitivity Sn = TP/(TP+FN) •Specificity Sp = TP/(TP+FP) •CC=TP.TN-FP.FN/([TP+FP][TN+FN][TP+TN])1/2

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)

•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

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 Zang’s Exon Finder uses QDA)

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

HHMs •GenScan (http://genes.mit.edu/GENSCAN.html) 5th-order 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

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/interfaces/est2genome.html) •Compares EST and cDNA to user sequence •TwinScan •Similar to GenomeScan

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/gencombver/index.php) •Combined HMMgene with GenScan •DIGIT (http://digit.gsc.riken.go.jp/cgi-bin/index.cgi) •Combines FGENESH, GENSCAN and HMMgene

Nucleotide Level Exon Level

Sn Sp CC Sn Sp (Sn+Sp)

/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

Accuracy

Chapter 9

Promoter and regulatory element prediction

•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

polymerase

ORF

-35 box -10 box

TF site

TF

•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

Prokaryotic gene

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/repression

TF site

TF site TATA Inr

Pol II

Eukaryotic gene structure

Ab initio methods •Promoter signals

•TATA boxes •Hexamer frequencies

•Consensus sequence matching •PSSM •Numerous FPs •HMMs incorporate neighboring information

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

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/cpgprod.html) • CG% inmoving window • Eponine (http://servlet.sanger.ac.uk:8080/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

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/consite) •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/applications/bayesian/bayes/bayes.align12.pl) •Aligns two sequences with Bayesian algorithm •Even weakly conserved regions identified

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/website/meme-intro.html) AlignACE (http://atlas.med.harvard.edu/cgi-bin/alignace.pl) Gibbs sampling algorithm

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