1. Applications of HMMs Yves Moreau 2003-2004

2. Overview Profile HMMs Estimation Database search Alignment Gene finding Elements of gene prediction Prokaryotes vs. eukaryotes Gene prediction by homology GENSCAN

3. Profile HMM Hidden Markov model for the modeling of protein families and for multiple alignment Example Part of the alignment of the SH3 domain Two conserved regions separated by a variable region GGWWRGdy.ggkkqLWFPSNYV IGWLNGynettgerGDFPGTYV PNWWEGql..nnrrGIFPSNYV DEWWQArr..deqiGIVPSK-- GEWWKAqs..tgqeGFIPFNFV GDWWLArs..sgqtGYIPSNYV GDWWDAel..kgrrGKVPSNYL -DWWEArslssghrGYVPSNYV GDWWYArslitnseGYIPSTYV GEWWKArslatrkeGYIPSNYV GDWWLArslvtgreGYVPSNFV GEWWKAkslsskreGFIPSNYV GEWCEAgt.kngq.GWVPSNYI SDWWRVvnlttrqeGLIPLNFV LPWWRArd.kngqeGYIPSNYI RDWWEFrsktvytpGYYESGYV EHWWKVkd.algnvGYIPSNYV IHWWRVqd.rngheGYVPSSYL KDWWKVev..ndrqGFVPAAYV

4. Profile HMMs Hidden Markov Models for multiple alignments Match, insert, and delete states BgnEnd Match Insertion Deletion

5. Silent deletion states Deletions could be modeled by shortcut jumps between states Problem: number of transitions grows quadratically Other solution: use parallel states that do not produce any symbol (silent state)

6. HMM from multiple alignment GGWWRGdy.ggkkqLWFPSNYV IGWLNGynettgerGDFPGTYV PNWWEGql..nnrrGIFPSNYV DEWWQArr..deqiGIVPSK-- GEWWKAqs..tgqeGFIPFNFV GDWWLArs..sgqtGYIPSNYV GDWWDAel..kgrrGKVPSNYL -DWWEArslssghrGYVPSNYV GDWWYArslitnseGYIPSTYV GEWWKArslatrkeGYIPSNYV GDWWLArslvtgreGYVPSNFV GEWWKAkslsskreGFIPSNYV GEWCEAgt.kngq.GWVPSNYI SDWWRVvnlttrqeGLIPLNFV LPWWRArd.kngqeGYIPSNYI RDWWEFrsktvytpGYYESGYV EHWWKVkd.algnvGYIPSNYV IHWWRVqd.rngheGYVPSSYL KDWWKVev..ndrqGFVPAAYV Multiple alignment (+ conserved columns) Parameter estimation = estimation with known paths.85 Corresponding profile HMM

7. Pseudocounts Zero probabilities in HMM causes the rejection of sequences containing previously unseen residues To avoid this problem, add pseudocounts (add extra counts as if prior data was available) New profile HMM.85.33

8. Database search with profile HMM The estimated model can be used to detect new members of the protein family in a sequence database (more sensitive than PSI-BLAST) For each sequence in the database, we compute P(x,  * | M) (Viterbi) or P(x | M) (forward-backward) In practice we work with log-odds (w.r.t. the random model P(x | R) )

9. Alignment to profile HMM Through Viterbi (search for the best alignment path), we can align sequences w.r.t a profile HMM Training sequences Database matches

10. Multiple alignment with profile HMM If the sequences are not aligned, it is possible to train a profile HMM to align them Initialization: choose the length of the profile HMM Length of profile HMM is number of match states  sequence length Training: estimate the model via Viterbi training or Baum-Welch training Heuristics to avoid local minimas Multiple alignment: use Viterbi decoding to align sequences

11. Extensions More sophisticated pseudocounts are possible Dirichlet mixtures Different types of local alignments can be done with HMMs Methods are available to weigh sequences in function of evolutionary distances

12. Protein families PFAM http://www.sanger.ac.uk/Software/Pfam/search.shtml Collection of protein families and protein domains Provides multiple alignment of the protein families for the domains Provides the domain organization of proteins Provides profile HMMs of the domains

13. Software for profile HMMs SAM: University of California Santa Cruz http://www.cse.ucsc.edu/research/compbio/sam.html Web service: http://www.cse.ucsc.edu/research/compbio/HMM- apps/HMM-applications.html (takes time)http://www.cse.ucsc.edu/research/compbio/HMM- apps/HMM-applications.html Hmmer (‘hammer’): Washington University, St. Louis http://genome.wustl.edu/eddy/hmmer.html

14. Gene finding

15. Overview Elements of gene prediction Prokaryotes vs. eukaryotes Gene prediction by homology GENSCAN

17. DNA makes RNA makes proteins

18. Evidence for gene prediction Sources of evidence (positive and negative) Sequence similarity to known genes (e.g., found by BLASTX) Statistical measure of codon bias Template matches to functional sites (e.g., splice site) Similarity to features not likely to overlap coding sequence (e.g., Alu repeats) The structure must respect the biological grammar (promoter, exon, intro,...)

19. Search by signal vs. search by content Search by signal Detect short signals in the genome E.g., splice site, signal peptide, glycosylation site Neural networks can be useful here Search by content Detect extended regions in the genome e.g., coding regions, CpG islands Hidden Markov Models are useful here Gene finding algorithms combine both

20. Probabilistic prediction vs. homology Hidden Markov Models can be used to predict genes Homology to a known gene is also a strong method for detecting genes More and more gene prediction packages combine both approaches

21. Search by signal vs. content

22. Signals in prokaryotes Transcription start and stop -35 region TATA box Translation start and stop Open Reading Frames Shine-Delgarno motif Start ATG/GTG Stop TAA/TAG/TGA Stem-loops Operon

23. Problems for prokaryotes Short genes are hard to detect Operons Overlapping genes

24. Signals in eukaryotes Transcription Promotor/enhancer/silencer TATA box Introns/exons Donor/acceptor/branch PolyA Repeats Alu, satellites CpG islands Cap/CCAAT&GC boxes Translation 5’ and 3’ UTR Kozak consensus Start ATG Stop TAA/TAG/TGA

25. Open reading frames Translate the sequence into the six possible reading frames Check for start and stop codons

26. Codon bias In coding sequences, genomes have specific biases for the use of codons encoding the same amino acid

27. Coding potential Most coding potentials are based on analysis of codon usage The HMMs keeps track of some kind of average coding potential around each position The increase and decrease of the coding potential will “push” the HMM in and out of the exons

28. Promoter region Promoter region contains the elements that control the expression of the gene Prediction of the promoter region (e.g., prediction of the TATA- box) is difficult

29. Intron-exon splicing Consensus 5’ Donor (A,C)AG/GT(A,G)AGT 3’ Acceptor TTTTTNCAG/GCCCCC Branch CT(G,A)A(C,T) Neural networks can predict splice sites; they can detect complex correlation between positions in a functional site

30. Gene prediction by homology

31. Coding regions evolve more slowly than noncoding ones (conserved by natural selection because of their functional role) Not only the protein sequence but also the gene structure can be conserved Use standard homology methods Gene syntax must be respected

32. Gene prediction by homology

33. Procrustes Find potentially related with BLASTX (= model sequences) Find all possible blocks (exons) on the basis of acceptor/donor location Look which blocks can be aligned with model sequences Look for best alignment of blocks with the query sequence

34. Gene prediction by homology Advantages Recognition of short exons and atypical exons Correct assembly of complex genes (> 10 exons) Disadvantages Genes without known homologs are missed Good homologs necessary for the prediction of the gene structure Very sensitive to sequencing errors

35. GENSCAN

36. GENSCAN was used for the annotation of the human genome in the Human Genome Project Gene prediction with Hidden Semi-Markov Models Different models in function of GC-content ( 57%)

37. Typical gene structure

38. Signal: human splice site 5’ splice site 3’ splice site

39. Hidden semi-Markov model

40. Example Nodes of HSMM Position-weight matrix (signal) Higher-order position-weight matrix HMM (content)

41. Architecture of GENSCAN

42. Training of HSMM Viterbi algorithm Viterbi algorithm for HSMMs

43. Gene structure prediction Current performance on exon prediction is acceptable However, grouping the correct exons into the genes is still problematic In many cases, a significant proportion of the predicted genes will not be correct

45. CpG islands In mammalians, CpG islands have higher G+C and CG dinucleotide content than the rest of the DNA CpG islands arise in active regions where no deactivation by methylation takes place (CG dinucleotides in methylated regions disappear by deamination) CpG islands may be used as gene markers in mammalians

46. Repeats Repeats make up a large part of the human genome Alu repeats Long Interspersed Elements (LINEs) Short Interspersed Elements (SINEs) Important to mask repeats when searching for genes

47. Promoter, enhancers, and silencers

48. Promotor, enhancers en silencers

49. Polyadenylation signal Polyadenylation (cleavage of pre-mRNA 3' end and synthesis of poly-(A) tract) is a very important early step of pre-mRNA processing The most well-known signal involved in this process is AATAAA, located 15-20 nucleotides upstream from the poly-(A) site (site of cleavage) Real AATAAA signals can differ from AATAAA consensus sequence. The most frequent natural variant, ATTAAA, is nearly as active as the canonical sequence.

50. Problem: alternative splicing

51. Problem: pseudogenes Loss of promoter, extra stop codon, frameshift Translocation, duplication

52. Problem: RNA genes rRNA (ribosomal) tRNA (transfer) snRNA (splicing) tmRNA (telomerase) microRNAs

53. Neural networks for exon prediction GRAIL uses a neural network to predict the score of a candidate exon