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Gene Recognition

Gene Recognition. Credits for slides: Marina Alexandersson Lior Pachter Serge Saxonov. Reading. GENSCAN SLAM Twinscan Optional: Chris Burge’s Thesis. Gene expression. DNA. transcription. RNA. translation. Protein. CCTGAGCCAACTATTGATGAA. CCU GAG CCA ACU AUU GAU GAA.

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Gene Recognition

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

  2. Reading • GENSCAN • SLAM • Twinscan Optional: Chris Burge’s Thesis Lecture 13, Thursday May 15, 2003

  3. Gene expression DNA transcription RNA translation Protein CCTGAGCCAACTATTGATGAA CCUGAGCCAACUAUUGAUGAA PEPTIDE Lecture 13, Thursday May 15, 2003

  4. Gene structure intron1 intron2 exon2 exon3 exon1 transcription splicing translation exon = coding intron = non-coding Lecture 13, Thursday May 15, 2003

  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 Lecture 13, Thursday May 15, 2003

  6. Approaches to gene finding • Homology • BLAST, Procrustes. • Ab initio • Genscan, Genie, GeneID. • Hybrids • GenomeScan, GenieEST, Twinscan, SGP, ROSETTA, CEM, TBLASTX, SLAM. Lecture 13, Thursday May 15, 2003

  7. HMMs for single species gene finding: Generalized HMMs

  8. intron exon exon intron intergene exon intergene HMMs for gene finding GTCAGAGTAGCAAAGTAGACACTCCAGTAACGC Lecture 13, Thursday May 15, 2003

  9. 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 Lecture 13, Thursday May 15, 2003

  10. Observed duration times Lecture 13, Thursday May 15, 2003

  11. Biology of Splicing (http://genes.mit.edu/chris/) Lecture 13, Thursday May 15, 2003

  12. Consensus splice sites Donor: 7.9 bits Acceptor: 9.4 bits (Stephens & Schneider, 1996) (http://www-lmmb.ncifcrf.gov/~toms/sequencelogo.html) Lecture 13, Thursday May 15, 2003

  13. Splice Site Models • 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 like algorithm to take significant pairwise dependencies into account Lecture 13, Thursday May 15, 2003

  14. Donor site 5’ 3’ Position % Splice site detection Lecture 13, Thursday May 15, 2003

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

  16. 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 Lecture 13, Thursday May 15, 2003

  17. Comparison of 1196 orthologous genes(Makalowski et al., 1996) • Sequence identity: • exons: 84.6% • protein: 85.4% • introns: 35% • 5’ UTRs: 67% • 3’ UTRs: 69% • 27 proteins were 100% identical. Lecture 13, Thursday May 15, 2003

  18. Human Mouse Human-mouse homology Lecture 13, Thursday May 15, 2003

  19. Lecture 13, Thursday May 15, 2003

  20. Not always: HoxA human-mouse Lecture 13, Thursday May 15, 2003

  21. 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 Lecture 13, Thursday May 15, 2003

  22. Twinscan • Twinscan is an augmented version of the Gencscan HMM. I E transitions duration emissions ACUAUACAGACAUAUAUCAU Lecture 13, Thursday May 15, 2003

  23. 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| } Lecture 13, Thursday May 15, 2003

  24. Twinscan Algorithm (cont’d) • 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 Lecture 13, Thursday May 15, 2003

  25. Example Human: ACGGCGACUGUGCACGU Mouse: ACUGUGAC GUGCACUU Align: ||:|:|||-||||||:| 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 Lecture 13, Thursday May 15, 2003

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

  27. A Pair HMM for alignments 1 - 2 M P(xi, yj) 1-  - 2 1-  - 2   I P(xi) J P(yj)     BEGIN I M J END Lecture 13, Thursday May 15, 2003

  28. Cross-species gene finding Exon3 Exon1 Exon2 Intron1 Intron2 5’ 3’ CNS CNS CNS [human] [mouse] Exon = coding Intron = non-coding CNS = conserved non-coding Lecture 13, Thursday May 15, 2003

  29. Generalized Pair HMMs Lecture 13, Thursday May 15, 2003

  30. Ingredients in exon scores • Splice site detection (VLMM) • Length distribution (generalized) • Coding potential (codon freq. tables) • GC-stratification Lecture 13, Thursday May 15, 2003

  31. Exon GPHMM 1.Choose exon lengths (d,e). 2.Generate alignment of length d+e. e d Lecture 13, Thursday May 15, 2003

  32. The SLAM hidden Markov model Lecture 13, Thursday May 15, 2003

  33. Example: HoxA2 and HoxA3 TBLASTX SLAM SLAM CNS SGP-2 VISTA Twinscan RefSeq Genscan Lecture 13, Thursday May 15, 2003

  34. length seq1 no. states length seq2 max duration Computational complexity Lecture 13, Thursday May 15, 2003

  35. Approximate alignment Reduces TU -factor to hT Lecture 13, Thursday May 15, 2003

  36. Measuring Performance • Definition: • Sensitivity (SN): (# correctly predicted)/(# true) • Specificity (SP): (# correctly predicted)/(# total predicted) Lecture 13, Thursday May 15, 2003

  37. Measuring Performance Lecture 13, Thursday May 15, 2003

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