17 ½ Weeks in Leipzig, Saxonia. Andreas Gruber Institute for Theoretical Chemistry University of Vienna

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1 17 ½ Weeks in Leipzig, Saxonia Andreas Gruber Institute for Theoretical Chemistry University of Vienna

2 START Leipzig, Idea? RNAz FINISH Vienna,

3 START Leipzig, Idea? RNAz FINISH Vienna,

4 Bacterial Self Killing System

5 START Leipzig, Idea? RNAz FINISH Vienna,

6 START Leipzig, Idea? RNAz FINISH Vienna,

7 So far... Shigella sonnei Ss046 Serratia proteamaculans 568 Escherichia coli IAI1 Enterobacter sp. 638 Salmonella enterica Edwardsiella ictaluri Shigella boydii CDC Escherichia fergusonii ATCC Escherichia coli O157:H7 str. EC4115 Photobacterium profundum SS9 Proteus mirabilis HI4320 Klebsiella pneumoniae 342 Escherichia coli str. K-12 substr Photorhabdus luminescens Shigella flexneri 2a str. 2457T Shigella flexneri 2a str. 301 Shigella boydii Sb227 Klebsiella pneumoniae NTUH-K2044 Vibrio fischeri ES114 Salmonella enterica Cronobacter sakazakii ATCC BAA-894 Vibrio vulnificus CMCP6 Aliivibrio salmonicida LFI1238 Klebsiella pneumoniae Shigella dysenteriae Sd197 Shewanella baltica OS223 Aeromonas salmonicida

8 START Leipzig, Idea? RNAz FINISH Vienna,

9 START Leipzig, Idea? RNAz FINISH Vienna,

10 ncrna detection with RNAz functional ncrnas

11 ncrna detection with RNAz functional ncrnas structured ncrnas

12 ncrna detection with RNAz functional ncrnas Why focus on structured RNAs? Structured RNAs are the only class of functional RNAs that give at least some statistically relevant signals. structured ncrnas

13 ncrna detection with RNAz functional ncrnas Why focus on structured RNAs? Structured RNAs are the only class of functional RNAs that give at least some statistically relevant signals. Thermodynamic stability structured ncrnas

14 ncrna detection with RNAz functional ncrnas Why focus on structured RNAs? Structured RNAs are the only class of functional RNAs that give at least some statistically relevant signals. Thermodynamic stability Structural conservation structured ncrnas

15 START Leipzig, Idea? RNAz FINISH Vienna,

16 START Leipzig, Idea? RNAz FINISH Vienna,

17 Thermodynamic stability z-score = E - μbackground σbackground

18 Thermodynamic stability z-score = E - μbackground σbackground 1) Generate randomized sequences of the same length and same base composition

19 Thermodynamic stability z-score = E - μbackground σbackground 1) Generate randomized sequences of the same length and same base composition 2) Fold the sequences using RNAfold

20 Thermodynamic stability z-score = E - μbackground σbackground 1) Generate randomized sequences of the same length and same base composition 2) Fold the sequences using RNAfold 3) calculate μ and σ

21 START Leipzig, Idea? RNAz FINISH Vienna,

22 START Leipzig, Idea? RNAz FINISH Vienna,

23 The Wash way Explicit generation and folding of sequences is too costly

24 The Wash way Explicit generation and folding of sequences is too costly Clue: Regression - train a SVM instead that does the job

25 The Wash way Explicit generation and folding of sequences is too costly Clue: Regression - train a SVM instead that does the job for length ( by 50) { for C+G ( by 0.05) { for A/(A+U) ( by 0.05) { for C/(C+G) ( by 0.05) { 1) generate a synthetic sequence of given length with nucleotide frequencies derived from C+G, A/(A+U), and C/(C+G) 2) generate 1,000 shuffled sequences 3) fold those 1,000 sequences and calculate μ and σ } } } } ==> ~ 10,000 training examples to train a SVM for μ and σ, respectively.

26 The Wash way Explicit generation and folding of sequences is too costly Clue: Regression - train a SVM instead that does the job for length ( by 50) { for C+G ( by 0.05) { for A/(A+U) ( by 0.05) { for C/(C+G) ( by 0.05) { 1) generate a synthetic sequence of given length with nucleotide frequencies derived from C+G, A/(A+U), and C/(C+G) 2) generate 1,000 shuffled sequences 3) fold those 1,000 sequences and calculate μ and σ } } } } ==> ~ 10,000 training examples to train a SVM for μ and σ, respectively. Energy minimization is based on stacking energies

27 The Wash way Explicit generation and folding of sequences is too costly Energy minimization is based on stacking energies Clue: Regression - train a SVM instead that does the job for length ( by 50) { for C+G ( by 0.05) { for A/(A+U) ( by 0.05) { for C/(C+G) ( by 0.05) { 1) generate a synthetic sequence of given length with nucleotide frequencies derived from C+G, A/(A+U), and C/(C+G) 2) generate 1,000 shuffled sequences 3) fold those 1,000 sequences and calculate μ and σ } } } } ==> ~ 10,000 training examples to train a SVM for μ and σ, respectively. It would be better to consider dinucleotide composition as well

28 START Leipzig, Idea? RNAz FINISH Vienna,

29 START Leipzig, Idea? RNAz FINISH Vienna,

30

31 Generate Sequences

32 Generate Sequences Shuffle

33 Generate Sequences Shuffle Vary Length Representative Set

34 Generate Sequences Shuffle Vary Length Representative Set μ, σ

35 Generate Sequences Shuffle Vary Length Representative Set μ, σ Split to Subsets

36 Generate Sequences Shuffle Vary Length Representative Set μ, σ Train Split to Subsets

37 START Leipzig, Idea? RNAz FINISH Vienna,

38 START Leipzig, Idea? RNAz FINISH Vienna,

39 NEW

40 NEW NEW

41 NEW NEW NEW

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43 START Leipzig, Idea? RNAz FINISH Vienna,

44 We did it

45 We did it

46 We did it

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48 START Leipzig, Idea? RNAz FINISH Vienna,

49 Structural vs. sequence based alignments

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52 On a genome wide scale

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54 START Leipzig, Idea? RNAz FINISH Vienna,

55 Manja agruber Dom agruber

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57 START Leipzig, Idea? RNAz FINISH Vienna,

58 J. Mattick

59 J. Mattick Alu repeats

60 J. Mattick Alu repeats Retrotransposition

61 J. Mattick Alu repeats Retrotransposition Pseudogenes

62 J. Mattick Alu repeats Retrotransposition Pseudogenes Expressed Sequence Tags (ESTs)

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64 START Leipzig, Idea? RNAz FINISH Vienna,

65 BLAST query: > 75% query coverage, > 95% identity

66 BLAST query: > 75% query coverage, > 95% identity?

67 START Leipzig, Idea? RNAz FINISH Vienna,

68 START Leipzig, Idea? RNAz FINISH Vienna,

69

70 Full length match

71 Full length match Partial match

72 77% of ESTs with U1 snrna sigantures are Chimeras Full length match Partial match Chimera

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75 What do we learn? Chimera are most likely artifacts caused during the process of library generation. There are expressed RNA pseudogenes. Small ncrnas can be found in ESTs but it is likely that they are fused to something else. In most cases only the protein component is annotated.

76 What do we learn? Chimera are most likely artifacts caused during the process of library generation. There are expressed RNA pseudogenes. Small ncrnas can be found in ESTs but it is likely that they are fused to something else. In most cases only the protein component is annotated. What's the plan? There are lots of ESTs and there are lots of sequenced genomes A pipeline for detection of chimeric ESTs and hence expressed small ncrnas is in production.

77 START Leipzig, Idea? RNAz FINISH Vienna,

78 START Leipzig, Idea? RNAz FINISH Vienna,

79 Some C. elegans snornas have a trna-like promoter

80 Some C. elegans snornas have a trna-like promoter Is there a already powerful box-a-box-b-finder?

81 START Leipzig, Idea? RNAz FINISH Vienna,

82 START Leipzig, Idea? RNAz FINISH Vienna,

83 Yes, there is trnascan-se Lowe TM, Eddy SR. trnascan-se: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. (1997) 25(5)

84 Yes, there is trnascan-se trnascan algorithm * screen for putative box A and box b motifs * then call the computationally more costly structure validation Lowe TM, Eddy SR. trnascan-se: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. (1997) 25(5)

85 Yes, there is trnascan-se trnascan algorithm * screen for putative box A and box b motifs * then call the computationally more costly structure validation All we need to do is to take the trnascan FALSE POSITVES Lowe TM, Eddy SR. trnascan-se: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. (1997) 25(5)

86 Yes, there is trnascan-se trnascan algorithm * screen for putative box A and box b motifs * then call the computationally more costly structure validation snornas and snorna predictions Screen 200 nt upstream of each known snorna to identify those that have trna-like promoters Screen 200 nt upstream of snoreport hits in P. pacificus to lend some hits additional reliability All we need to do is to take the trnascan FALSE POSITVES Lowe TM, Eddy SR. trnascan-se: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. (1997) 25(5)

87 Yes, there is trnascan-se trnascan algorithm * screen for putative box A and box b motifs * then call the computationally more costly structure validation snornas and snorna predictions Screen 200 nt upstream of each known snorna to identify those that have trna-like promoters Screen 200 nt upstream of snoreport hits in P. pacificus to lend some hits additional reliability Genome-wide scale Take trnascan FP-hits All we need to do is to take the trnascan FALSE POSITVES Cut out 70 nt down stream or search for poly T stretch Call blastlcust and look at inter-species clusters Lowe TM, Eddy SR. trnascan-se: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. (1997) 25(5)

88 START Leipzig, Idea? RNAz FINISH Vienna,

89 START Leipzig, Idea? RNAz FINISH Vienna,

90 Thanks to the whole Bierinformatik

91 Thanks to the whole Bierinformatik MOTIVATION Support on almost everything I worked on

92 Thanks to the whole Bierinformatik MOTIVATION Support on almost everything I worked on Talking about RNA, Gott und die Welt...

93 Thanks to the whole Bierinformatik MOTIVATION Support on almost everything I worked on Talking about RNA, Gott und die Welt... For being the Mr. Nice Guy at my back

94 Thanks to the whole Bierinformatik MOTIVATION Support on almost everything I worked on Talking about RNA, Gott und die Welt... For being the Mr. Nice Guy at my back For being Peter F. Stadler