Genomics 101 DNA sequencing Alignment Gene identification Gene expression Genome evolution …

Next Few Topics Gene Recognition Finding genes in DNA with computational methods Large-scale alignment & multiple alignment Comparing whole genomes, or large families of genes Gene Expression and Regulation Measuring the expression of many genes at a time Finding elements in DNA that control the expression of genes

Gene Recognition Credits for slides: Marina Alexandersson Lior Pachter Serge Saxonov

Reading GENSCAN EasyGene SLAM Twinscan Optional: Chris Burge’s Thesis

Gene expression Protein RNA DNA transcription translation CCTGAGCCAACTATTGATGAA PEPTIDEPEPTIDE CCUGAGCCAACUAUUGAUGAA

Gene structure exon1 exon2exon3 intron1intron2 transcription translation splicing exon = protein-coding intron = non-coding Codon: A triplet of nucleotides that is converted to one amino acid

Where are the genes?

In humans: ~22,000 genes ~1.5% of human DNA

Finding Genes 1.Exploit the regular gene structure ATG—Exon1—Intron1—Exon2—…—ExonN—STOP 2.Recognize “coding bias” CAG-CGA-GAC-TAT-TTA-GAT-AAC-ACA-CAT-GAA-… 3.Recognize splice sites Intron—cAGt—Exon—gGTgag—Intron 4.Model the duration of regions Introns tend to be much longer than exons, in mammals Exons are biased to have a given minimum length 5.Use cross-species comparison Gene structure is conserved in mammals Exons are more similar (~85%) than introns

Approaches to gene finding Homology  BLAST, Procrustes. Ab initio  Genscan, Genie, GeneID. Hybrids  GenomeScan, GenieEST, Twinscan, SGP, ROSETTA, CEM, TBLASTX, SLAM.

Start codon ATG 5’ 3’ Exon 1 Exon 2 Exon 3 Intron 1Intron 2 Stop codon TAG/TGA/TAA Splice sites 1. Exploit the regular gene structure

Next Exon: Frame 0 Next Exon: Frame 1

2. Recognize “coding bias” Each exon can be in one of three frames ag—gattacagattacagattaca—gtaagFrame 0 ag—gattacagattacagattaca—gtaagFrame 1 ag—gattacagattacagattaca—gtaagFrame 2 Frame of next exon depends on how many nucleotides are left over from previous exon Codons “tag”, “tga”, and “taa” are STOP  No STOP codon appears in-frame, until end of gene  Absence of STOP is called open reading frame (ORF) Different codons appear with different frequencies— coding bias

2. Recognize “coding bias” Amino AcidSLCDNA codons IsoleucineIATT, ATC, ATA LeucineLCTT, CTC, CTA, CTG, TTA, TTG ValineVGTT, GTC, GTA, GTG PhenylalanineFTTT, TTC MethionineMATG CysteineCTGT, TGC AlanineAGCT, GCC, GCA, GCG GlycineGGGT, GGC, GGA, GGG ProlinePCCT, CCC, CCA, CCG ThreonineTACT, ACC, ACA, ACG SerineSTCT, TCC, TCA, TCG, AGT, AGC TyrosineYTAT, TAC TryptophanWTGG GlutamineQCAA, CAG AsparagineNAAT, AAC HistidineHCAT, CAC Glutamic acidEGAA, GAG Aspartic acidDGAT, GAC LysineKAAA, AAG ArginineRCGT, CGC, CGA, CGG, AGA, AGG Stop codons StopTAA, TAG, TGA Can map 61 non-stop codons to frequencies & take log-odds ratios

atg tga ggtgag caggtg cagatg cagttg caggcc ggtgag

Biology of Splicing (

3. Recognize splice sites ( Donor: 7.9 bits Acceptor: 9.4 bits (Stephens & Schneider, 1996)

5’ 3’ Donor site Position  3. Recognize splice sites

WMM: weight matrix model = PSSM (Staden 1984) WAM: weight array model = 1 st order Markov (Zhang & Marr 1993) MDD: maximal dependence decomposition (Burge & Karlin 1997)  Decision-tree algorithm to take pairwise dependencies into account For each position I, calculate S i =  j  i  2 (C i, X j ) Choose i * such that S i* is maximal and partition into two subsets, until No significant dependencies left, or Not enough sequences in subset  Train separate WMM models for each subset All donor splice sites G5G5 not G 5 G 5 G -1 G 5 not G -1 G 5 G -1 A 2 G 5 G -1 not A 2 G 5 G -1 A 2 U 6 G 5 G -1 A 2 not U 6 3. Recognize splice sites

4.Model the duration of regions

GTCAGATGAGCAAAGTAGACACTCCAGTAACGCGGTGAGTACATTAA exon intron intergene Hidden Markov Models for Gene Finding Intergene State First Exon State Intron State

GTCAGATGAGCAAAGTAGACACTCCAGTAACGCGGTGAGTACATTAA exon intron intergene Hidden Markov Models for Gene Finding Intergene State First Exon State Intron State

TAAAAAAAAAAAAAAAATTTTTTTTTTTTTTTGGGGGGGGGGGGGGGCCCCCCC Exon1Exon2Exon3 duration Duration HMM for Gene Finding Duration Modeling Introns: regular HMM states—geometric duration Exons: special duration model V E0,0 (i) = max d=1…D { Prob[duration(E0,0)=d]  a Intron0,E0,0   j=i-d+1…i e E0,0 (x j ) } where i is an admissible exon-ending state, D is restricted by the longest ORF GENSCAN: Chris Burge and Sam Karlin, 1997 Best performing de novo gene finder HMM with duration modeling for Exon states

HMM-based Gene Finders GENSCAN (Burge 1997)  Big jump in accuracy of de novo gene finding  Currently, one of the best  HMM with duration modeling for Exon states FGENESH (Solovyev 1997)  Currently one of the best HMMgene (Krogh 1997) GENIE (Kulp 1996) GENMARK (Borodovsky & McIninch 1993) VEIL (Henderson, Salzberg, & Fasman 1997)

Better way to do it: negative binomial EasyGene: Prokaryotic gene-finder Larsen TS, Krogh A Negative binomial with n = 3

GENSCAN’s hidden weapon C+G content is correlated with:  Gene content (+)  Mean exon length(+)  Mean intron length (–) These quantities affect parameters of model Solution  Train parameters of model in four different C+G content ranges!

Evaluation of Accuracy (Slide by NF Samatova) Sensitivity (SN)Fraction of exons (coding nucleotides) whose boundaries are predicted exactly (that are predicted as coding) Specificity (Sp)Fraction of the predicted exons (coding nucleotides) that are exactly correct (that are coding) Correlation Coefficient (CC) Combined measure of Sensitivity & Specificity Range: -1 (always wrong)  +1 (always right) TP FP TN FN TP FN TN Actual Predicted Coding / No Coding TNFN FPTP Predicted Actual No Coding / Coding

Results of GENSCAN On the initial test dataset (Burset & Guigo)  80% exact exon detection 10% partial exons 10% wrong exons In general  HMMs have been best in de novo prediction  In practice they overpredict human genes by ~2x