Gene Recognition

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

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

3. DNA transcription RNA translation Protein Gene expression CCTGAGCCAACTATTGATGAA CCUGAGCCAACUAUUGAUGAA PEPTIDE

4. Gene structure intron1 intron2 exon2 exon3 exon1 transcription splicing translation exon = coding intron = non-coding

5. Exon 3 Exon 1 Exon 2 Intron 1 Intron 2 5’ 3’ Stop codon TAG/TGA/TAA Start codon ATG Finding genes Splice sites

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

9. HMMs for single species gene finding: Generalized HMMs

10. T A A T A T G T C C A C G G G T A T T G A G C A T T G T A C A C G G G G T A T T G A G C A T G T A A T G A A Exon1 Exon2 Exon3 GHMM for gene finding This is a HMM similar to the GENSCAN HMM duration

11. Elements of the HMM • Duration of states – length distributions of • Exons • Introns • Signals at state transitions • ATG • Stop Codon TAG/TGA/TAA • Exon/Intron and Intron/Exon Splice Sites • Emissions • Coding potential and frame at exons • Intron emissions

12. 1. Durations of states

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

14. 2. Splice Site Detection Donor: 7.9 bits Acceptor: 9.4 bits (Stephens & Schneider, 1996) (http://www-lmmb.ncifcrf.gov/~toms/sequencelogo.html)

15. Donor site 5’ 3’ Position % 2. Splice Site Detection

16. 2. Splice Site Detection • WMM: weight matrix model = PSSM (Staden 1984) • WAM: weight array model = 1st 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 Si = ji2(Ci, Xj) • Choose i* such that Si* 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 G5G-1 G5G-1 A2 G5G-1 A2U6 G5 All donor splice sites not G5 G5 not G-1 G5G-1 not A2 G5G-1A2 not U6

17. atg caggtg ggtgag cagatg ggtgag cagttg ggtgag caggcc ggtgag tga

18. 3. Coding potential Amino Acid SLC DNA codons Isoleucine I ATT, ATC, ATA Leucine L CTT, CTC, CTA, CTG, TTA, TTG Valine V GTT, GTC, GTA, GTG Phenylalanine F TTT, TTC Methionine M ATG Cysteine C TGT, TGC Alanine A GCT, GCC, GCA, GCG Glycine G GGT, GGC, GGA, GGG Proline P CCT, CCC, CCA, CCG Threonine T ACT, ACC, ACA, ACG Serine S TCT, TCC, TCA, TCG, AGT, AGC Tyrosine Y TAT, TAC Tryptophan W TGG Glutamine Q CAA, CAG Asparagine N AAT, AAC Histidine H CAT, CAC Glutamic acid E GAA, GAG Aspartic acid D GAT, GAC Lysine K AAA, AAG Arginine R CGT, CGC, CGA, CGG, AGA, AGG Stop codons Stop TAA, TAG, TGA

19. 4. 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!

20. Results of GENSCAN • On the initial test dataset (Burset & Guigo) • 80% exact exon detection • 10% partial exons • 10% wrong exons • In general • Most accurate single sequence-based gene predictor • In practice it overpredicts human genes by ~2x

21. Comparison-based Methods

22. Comparison of 1196 orthologous genes(Makalowski et al., 1996) • Sequence identity between genes in human/mouse • exons: 84.6% • protein: 85.4% • introns: 35% • 5’ UTRs: 67% • 3’ UTRs: 69% • 27 proteins were 100% identical.

23. Human Mouse Human-mouse homology

25. Not always: HoxA human-mouse

26. 50 . : . : . : . : . : 247 GGTGAGGTCGAGGACCCTGCA CGGAGCTGTATGGAGGGCA AGAGC |: || ||||: |||| --:|| ||| |::| |||---|||| 368 GAGTCGGGGGAGGGGGCTGCTGTTGGCTCTGGACAGCTTGCATTGAGAGG 100 . : . : . : . : . : 292 TTC CTACAGAAAAGTCCCAGCAAGGAGCCACACTTCACTG |||----------|| | |::| |: ||||::|:||:-|| ||:| | 418 TTCTGGCTACGCTCTCCCTTAGGGACTGAGCAGAGGGCT CAGGTCGCGG 150 . : . : . : . : . : 332 ATGTCGAGGGGAAGACATCATTCGGGATGTCAGTG ---------------||||||||||||||||||||||:|||||||||||| 467 TGGGAGATGAGGCCAATGTCGAGGGGAAGACATCATTTGGGATGTCAGTG 200 . : . : . : . : . : 367 TTCAACCTCAGCAATGCCATCATGGGCAGCGGCATCCTGGGACTCGCCTA |||||:||||||||:||||||||||||||:|| ||:|||||:|||||||| 517 TTCAATCTCAGCAACGCCATCATGGGCAGTGGAATTCTGGGGCTCGCCTA Alignment

27. Twinscan • Twinscan is an augmented version of the Gencscan HMM. I E transitions duration emissions ACUAUACAGACAUAUAUCAU

28. Twinscan Algorithm • Align the two sequences (eg. from human and mouse) • Mark each human base as gap ( - ), mismatch ( : ), match ( | ) New “alphabet”: 4 x 3 = 12 letters  = { A-, A:, A|, C-, C:, C|, G-, G:, G|, U-, U:, U| }

29. Twinscan Algorithm • Run Viterbi using emissions ek(b) where b  { A-, A:, A|, …, T| } Note: Emission distributions ek(b) estimated from real genes from human/mouse eI(x|) < eE(x|): matches favored in exons eI(x-) > eE(x-): gaps (and mismatches) favored in introns

30. Example Human: ACGGCGACUGUGCACGU Mouse: ACUGUGAC GUGCACUU Alignment: ||:|:|||-||||||:| Input to Twinscan HMM: A| C| G: G| C: G| A| C| U- G| U| G| C| A| C| G: U| Recall, eE(A|) > eI(A|) eE(A-) < eI(A-) Likely exon

31. HMMs for simultaneous alignment and gene finding: Generalized Pair HMMs

32. 1 - 2 M P(xi, yj) 1-  - 2 1-  - 2   I P(xi) J P(yj)     A Pair HMM for alignments BEGIN I M J END

33. Exon3 Exon1 Exon2 Intron1 Intron2 5’ 3’ CNS CNS CNS Cross-species gene finding [human] [mouse] Exon = coding Intron = non-coding CNS = conserved non-coding

34. Generalized Pair HMMs

35. Ingredients in exon scores • Splice site detection (VLMM) • Length distribution (generalized) • Coding potential (codon freq. tables) • GC-stratification

36. Exon GPHMM 1.Choose exon lengths (d,e). 2.Generate alignment of length d+e. e d

37. The SLAM hidden Markov model

38. TBLASTX SLAM SLAM CNS SGP-2 VISTA Twinscan RefSeq Genscan Example: HoxA2 and HoxA3

39. length seq1 no. states length seq2 max duration Computational complexity

40. Approximate alignment Reduces TU -factor to hT

41. Measuring Performance • Definition: • Sensitivity (SN): (# correctly predicted)/(# true) • Specificity (SP): (# correctly predicted)/(# total predicted)

42. Measuring Performance