gene prediction methods vijay

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  • 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

    http://exon.gatech.edu/genemark/http://www.cbcb.umd.edu/software/glimmer/http://linux1.softberry.com/ftp://ftp.tigr.org/

  • 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 Zangs 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

    http://genes.mit.edu/GENSCAN.htmlhttp://www.cbs.dtu.dk/services/HMMgene

  • 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

    http://genes.mit.edu/genomescan.htmlhttp://bioweb.pasteur.fr/seqanal/interfaces/est2genome.htmlhttp://bioweb.pasteur.fr/seqanal/interfaces/est2genome.html

  • 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

    http://www.bioinformatics.ubc.ca/gencombver/index.phphttp://www.bioinformatics.ubc.ca/gencombver/index.phphttp://digit.gsc.riken.go.jp/cgi-bin/index.cgihttp://digit.gsc.riken.go.jp/cgi-bin/index.cgihttp://digit.gsc.riken.go.jp/cgi-bin/index.cgi

  • 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

    http://www.softberry.com/http://sun1.softberry.com/

  • 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

    http://pbil.univ-lyon1.fr/software/cpgprod.htmlhttp://pbil.univ-lyon1.fr/software/cpgprod.htmlhttp://pbil.univ-lyon1.fr/software/cpgprod.htmlhttp://servlet.sanger.ac.uk:8080/eponine/http://zlab.bu.edu/cluster-buster/cbust.htmlhttp://zlab.bu.edu/cluster-buster/cbust.htmlhttp://zlab.bu.edu/cluster-buster/cbust.htmlhttp://rulai.cshl.org/tools/FirstEF/http://genes.mit.edu/McPromoter.html

  • 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 scorin

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