Lesson 8 on Gene Ontologies, Gene Finding and Hidden Markox Models

2. FBW 09-12-2010 Wim Van Criekinge

12. Sequence

18. DAG Structure Directed acyclic graph: each child may have one or more parents

19. Example - Molecular Function

20. Example - Biological Process

21. Example - Cellular Location

22. AmiGO browser

25. Problem: Given a very long DNA sequence, identify coding regions (including intron splice sites) and their predicted protein sequences Computational Gene Finding

26. Eukaryotic gene structure Computational Gene Finding

28. Genefinder

30. GENE STRUCTURE INFORMATION - POSITION ON PHYSICAL MAP This gene structure corresponds to the position on the physical map

33. GENE STRUCTURE INFORMATION - PREDICTED GENE STRUCTURE This gene structure relates to the predicted gene structures Boxes are Exons, thin lines (or springs) are Introns

34. Find the open reading frames GAAAAAGCTCCTGCCCAATCTGAAATGGTTAGCCTATCTTTCCACCGT Any sequence has 3 potential reading frames (+1, +2, +3) Its complement also has three potential reading frames (-1, -2, -3) 6 possible reading frames The triplet, non-punctuated nature of the genetic code helps us out 64 potential codons 61 true codons 3 stop codons (TGA, TAA, TAG) Random distribution app. 1/21 codons will be a stop E K A P A Q S E M V S L S F H R K K L L P N L K W L A Y L S T K S S C P I * N G * P I F P P

40. blastn (EST) For raw DNA sequence analysis blastx is extremely useful Will probe your DNA sequence against the protein database A match (homolog) gives you some ideas regarding function One problem are all of the genome sequences Will get matches to genome databases that are strictly identified by sequence homology – often you need some experimental evidence

43. Borodovsky et al., 1999, Organization of the Prokaryotic Genome (Charlebois, ed) pp. 11-34 New generation of programs to predict gene coding sequences based on a non-random repeat pattern (eg. Glimmer, GeneMark) – actually pretty good

47. Exon/intron boundaries

55. Marchov Chain for DNA

56. Markov chain with begin and end

62. Hidden Markov Models

63. Hidden Markov Models: The occasionally dishonest casino

64. Hidden Markov Models: The occasionally dishonest casino

68. Example TMHMM Beyond Kyte-Doolitlle …

77. Length preference 5’ ss intcomp branch 3’ ss

79. Contents-Schedule RNA genes Besides the 6000 protein coding-genes, there is: 140 ribosomal RNA genes 275 transfer RNA gnes 40 small nuclear RNA genes >100 small nucleolar genes ? pRNA in  29 rotary packaging motor ( Simpson et el. Nature 408:745-750,2000) Cartilage-hair hypoplasmia mapped to an RNA (Ridanpoa et al. Cell 104:195-203,2001) The human Prader-Willi ciritical region (Cavaille et al. PNAS 97:14035-7, 2000)

84. RNA genes can be hard to detects UGAGGUAGUAGGUUGUAUAGU C.elegans let-27; 21 nt (Pasquinelli et al. Nature 408:86-89,2000) Often small Sometimes multicopy and redundant Often not polyadenylated (not represented in ESTs) Immune to frameshift and nonsense mutations No open reading frame, no codon bias Often evolving rapidly in primary sequence miRNA genes

86. Let-7 (lethal-7) was also mapped to a ncRNA gene with a 21-nucleotide product The small let-7 RNA is also thought to be a post-transcriptional negative regulator for lin-41 and lin-42 100% conserved in all bilaterally symmetrical animals (not jellyfish and sponges) Sometimes called stRNAs, small temporal RNAs Let-7 (Pasquinelli et al. Nature 408:86-89,2000)

95. Basic CFG “ production rules” S -> aS S -> Sa S -> aSu S -> SS Context-free grammers A CFG “derivation” S -> aS

96. Basic CFG “ production rules” S -> aS S -> Sa S -> aSu S -> SS Context-free grammers A CFG “derivation” S -> a S S -> a aS

97. Basic CFG “ production rules” S -> aS S -> Sa S -> aSu S -> SS Context-free grammers A CFG “derivation” S -> aS S -> aa S S -> aa SS

98. Basic CFG “ production rules” S -> aS S -> Sa S -> aSu S -> SS Context-free grammers A CFG “derivation” S -> aS S -> aaS S -> aa S S S -> aa gSc uS

99. Basic CFG “ production rules” S -> aS S -> Sa S -> aSu S -> SS Context-free grammers A CFG “derivation” S -> aS S -> aaS S -> aaS S S -> aagSc uS

100. Basic CFG “ production rules” S -> aS S -> Sa S -> aSu S -> SS Context-free grammers A CFG “derivation” S -> aS S -> aaS S -> aaSS S -> aag S cu S S -> aag aSu cu gSc

101. Basic CFG “ production rules” S -> aS S -> Sa S -> aSu S -> SS Context-free grammers A CFG “derivation” S -> aS S -> aaS S -> aaSS S -> aagScuS S -> aagaSucugSc S -> aaga S aucugg S cc S -> aaga cSg aucuggc gSc cc

102. Basic CFG “ production rules” S -> aS S -> Sa S -> aSu S -> SS Context-free grammers A CFG “derivation” S -> aS S -> aaS S -> aaSS S -> aagScuS S -> aagaSucugSc S -> aagaSaucuggScc S -> aagacSgaucuggcgSccc S -> aagacuSgaucuggcgSccc S -> aagacuuSgaucuggcgaSccc S -> aagacuucSgaucuggcgacSccc S -> aagacuucgSgaucuggcgacaSccc S -> aagacuucggaucuggcgacaccc

103. Basic CFG “ production rules” S -> aS S -> Sa S -> aSu S -> SS Context-free grammers A CFG “derivation” S -> aS S -> aaS S -> aaSS S -> aagScuS S -> aagaSucugSc S -> aagaSaucuggScc S -> aagacSgaucuggcgSccc S -> aagacuSgaucuggcgSccc S -> aagacuuSgaucuggcgaSccc S -> aagacuucSgaucuggcgacSccc S -> aagacuucgSgaucuggcgacaSccc S -> aagacuucggaucuggcgacaccc

104. Basic CFG “ production rules” S -> aS S -> Sa S -> aSu S -> SS Context-free grammers A CFG “derivation” S -> aS S -> aaS S -> aaSS S -> aagScuS S -> aagaSucugSc S -> aagaSaucuggScc S -> aagacSgaucuggcgSccc S -> aagacuSgaucuggcgSccc S -> aagacuuSgaucuggcgaSccc S -> aagacuucSgaucuggcgacSccc S -> aagacuucgSgaucuggcgacaSccc S -> aagacuucggaucuggcgacaccc A C G U * A A A A A G G G G G C C C C C C C U U U * * * * *

108. Compensatory substitutions that maintain the structure U U C G U A A U G C A UCGAC 3’ G C 5’

109. Evolutionary conservation of RNA molecules can be revealed by identification of compensatory substitutions

110. …………

112. Function on ncRNAs

113. ncRNAs & RNAi