Modeling and Searching for Non-Coding RNA

Transcription

1 Modeling and Searching for Non-oding RN W.L. Ruzzo

2 Outline Why RN? Examples of RN biology omputational hallenges Modeling Search Inference

3 The entral Dogma DN RN Protein gene Protein DN (chromosome) cell RN (messenger)

4 lassical RNs mrn trn rrn snrn (small nuclear - spl icing) snorn (small nucleolar - guides for t/rrn modifications) RNseP (trn maturation; ribozyme in bacteria) SRP (signal recognition particle; co-translational targeting of proteins to membranes) telomerases

5 Non-coding RN Messenger RN - codes for proteins Non-coding RN - all the rest Before, say, mid 1990 s, 1-2 dozen known (critically important, but narrow roles: e.g. trn) Since mid 90 s dramatic discoveries Regulation, transport, stability/degradation E.g. microrn : 100 s in humans By some estimates, ncrn >> mrn

6 RN Secondary Structure: RN makes helices too Base pairs U U U U U

7 RN on the Rise In humans more RN- than DN-binding proteins? much more conserved DN than coding MUH more transcribed DN than coding In bacteria regulation of MNY genes involves RN dozens of classes & thousands of new examples in just last 5 years

8 Human Predictions Evofold S Pedersen, Bejerano, Siepel, K Rosenbloom, K Lindblad-Toh, ES Lander, J Kent, W Miller, D Haussler, "Identification and classification of conserved RN secondary structures in the human genome." PLoS omput. Biol., 2, #4 (2006) e33. 48,479 candidates (~70% FDR?)

9 RNz S Washietl, IL Hofacker, M Lukasser, H tenhofer, PF Stadler, "Mapping of conserved RN secondary structures predicts thousands of functional noncoding RNs in the human genome." Nat. Biotechnol., 23, #11 (2005) ,000 structured RN elements 1,000 conserved across all vertebrates. ~1/3 in introns of known genes, ~1/6 in UTRs ~1/2 located far from any known gene

10 FOLDLIN E Torarinsson, M Sawera, JH Havgaard, M Fredholm, J orodkin, "Thousands of corresponding human and mouse genomic regions unalignable in primary sequence contain common RN structure." enome Res., 16, #7 (2006) candidates from (of 100,000) pairs

11 Mfinder unpublished 6500 candidates in ENODE alone (again high FDR)

12 Fastest Human ene?

13 Origin of Life? Life needs information carrier: DN molecular machines, like enzymes: Protein making proteins needs DN + RN + proteins making (duplicating) DN needs proteins Horrible circularities! How could it have arisen in an abiotic environment?

14 Origin of Life? RN can carry information too (RN double helix) RN can form complex structures RN enzymes exist (ribozymes) The RN world hypothesis: 1st life was RN-based

15 ncrn Example: Xist large (12kb?) unstructured RN required for X-inactivation in mammals

16 ncrn Example: 6S medium size (175nt) structured highly expressed in e. coli in certain growth conditions sequenced in 1971; function unknown for 30 years

17 6S mimics an open promoter E.coli Barrick et al. RN 2005 Trotochaud et al. NSMB 2005 Willkomm et al. NR 2005

18 ncrn Example: IRE Iron Response Element: a short conserved stemloop, bound by iron response proteins (IRPs). Found in UTRs of various mrns whose products are involved in iron metabolism. E.g., the mrn of ferritin (an iron storage protein) contains one IRE in its 5' UTR. When iron concentration is low, IRPs bind the ferritin mrn IRE. repressing translation. Binding of multiple IREs in the 3' and 5' UTRs of the transferrin receptor (involved in iron acquisition) leads to increased mrn stability. These two activities form the basis of iron homeostasis in the vertebrate cell.

19 Iron Response Element IRE (partial seed alignment): Hom.sap. UUUUUUUUUU Hom.sap. UUUUU.UUUUUUU Hom.sap. UUUUUUUUUU. Hom.sap. UUUU..UUUU.U Hom.sap. UUUUUUUUUUU Hom.sap. UUU..UUUU.UU Hom.sap. UUU..UUUUUU Hom.sap. UUU..UUU.UU Hom.sap. UUU..UUU.UU av.por. UUUUUUUU Mus.mus. UUU..UUU.UU Mus.mus. UUUUUUUUU Mus.mus. UUUUUUUUU Rat.nor. UUU..UU.UU Rat.nor. UUUUUUUUUU SS_cons <<<<<...<<<<<...>>>>>.>>>>>

20 ncrn Example: MicroRNs short (~22 nt) unstructured RNs excised from ~75nt precursor hairpin approx antisense to mrn targets, often in 3 UTR regulate gene activity, e.g. by destabilizing (plants) or otherwise suppressing (animals) message several hundred, w/ perhaps thousands of targets, are known

21 ncrn Example: T-boxes

22 ncrn Example: Riboswitches UTR structure that directly senses/binds small molecules & regulates mrn widespread in prokaryotes some in eukaryotes

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24 ene Regulation: The MET Repressor SM Protein lberts, et al, 3e. DN

25 lberts, et al, 3e. The protein way Riboswitch alternatives (2 of 4 shown) orbino et al., enome Biol. 2005

26 Example: lycine Regulation How is glycine level regulated? Plausible answer: g TF g g TF g gce protein glycine cleavage enzyme gene g DN transcription factors (proteins) bind to DN to turn nearby genes on or off

27 The lycine Riboswitch ctual answer (in many bacteria): g g gce protein g g 5 3 gce mrn glycine cleavage enzyme gene DN Mandal et al. Science 2004

28 Why? RN s fold, and function Nature uses what works

29 Impact of RN homology search B. subtilis glycine riboswitch (Barrick, et al., 2004) operon L. innocua. tumefaciens V. cholera M. tuberculosis (and 19 more species)

30 Impact of RN homology search B. subtilis L. innocua glycine riboswitch (Barrick, et al., 2004) operon Using our techniques, we found. tumefaciens V. cholera M. tuberculosis (and 19 more species) (and 42 more species)

31 RN Informatics RN: Not just a messenger anymore Dramatic discoveries Hundreds of families (besides classics like trn, rrn, snrn ) Widespread, important roles omputational tools important Discovery, characterization, annotation BUT: slow, inaccurate, demanding

32 Q: What s so hard? U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U : Structure often more important than sequence

33 omputational hallenges Search - given related RN s, find more Modeling - describe a related family Meta-modeling - what s a good modeling framework? M-based search Hand-curated alignments -> Ms ovariance Models

34 Predict Structure from Multiple Sequences U U U U U U ompensatory mutations reveal structure, but in usual alignment algorithms they are doubly penalized.

35 How to model an RN Motif? onceptually, start with a profile HMM: from a multiple alignment, estimate nucleotide/ insert/delete preferences for each position given a new seq, estimate likelihood that it could be generated by the model, & align it to the model mostly del ins all

36 How to model an RN Motif? ovariance Models (aka profile SF ) Probabilistic models, like profile HMMs, but adding column pairs and pair emission probabilities for base-paired regions <<<<<<< paired columns >>>>>>>

37 RN sequence analysis using covariance models Eddy & Durbin Nucleic cids Research, 1994 vol 22 #11,

38 What probabilistic model for RN families The ovariance Model Stochastic ontext-free rammar generalization of a profile HMM lgorithms for Training From aligned or unaligned sequences utomates comparative analysis omplements Nusinov/Zucker RN folding lgorithms for searching

39 Main Results Very accurate search for trn (Precursor to trnscanse - current favorite) iven sufficient data, model construction comparable to, but not quite as good as, human experts Some quantitative info on importance of pseudoknots and other tertiary features

40 Probabilistic Model Search s with HMMs, given a sequence, you calculate llikelihood ratio that the model could generate the sequence, vs a background model You set a score threshold nything above threshold => a hit Scoring: Forward / Inside algorithm - sum over all paths Viterbi approximation - find single best path (Bonus: alignment & structure prediction)

41 Example: searching for trns

42 lignment Quality

43 omparison to TRNSN Fichant & Burks - best heuristic then 97.5% true positive 0.37 false positives per MB M 1415 (trained on trusted alignment) > 99.98% true positives <0.2 false positives per MB urrent method-of-choice is trnscanse, a Mbased scan with heuristic pre-filtering (including TRNSN?) for performance reasons. Slightly different evaluation criteria

44 M Structure : Sequence + structure B: the M guide tree : probabilities of letters/ pairs & of indels Think of each branch being an HMM emitting both sides of a helix (but 3 side emitted in reverse order)

45 Overall M rchitecture One box ( node ) per node of guide tree BE/MTL/INS/DEL just like an HMM MTP & BIF are the key additions: MTP emits pairs of symbols, modeling base-pairs; BIF allows multiple helices

46 M Viterbi lignment x i = i th letter of input x ij = substring i,..., j of input T yz = P(transition y " z) y E xi,x j = P(emission of x i, x j from state y) S ij y = max # log P(x ij generated starting in state y via path #)

47 Viterbi, cont. S ij y = max " log P(x ij generated starting in state y via path ") S ij y = % z max z [S i+1, j#1 ' z max z [S i+1, j ' z & max z [S i, j#1 ' z ' max z [S i, j (' max i<k$ j [S i,k y left y + logt yz + log E xi,x j y + logt yz + log E xi ] match pair ] match/insert left + logt yz + log E x j ] match/insert right + logt yz ] delete y + S right k +1, j ] Time O(qn 3 ), q states, seq len n compare: O(qn) for profile HMM bifurcation

48 Model Training

49 Mutual Information M ij = f " f xi,xj xi,xj log xi,xj 2 f xi f xj ; 0 # M ij # 2 Max when no seq conservation but perfect pairing MI = expected score gain from using a pair state Finding optimal MI, (i.e. opt pairing of cols) is hard(?) Finding optimal MI without pseudoknots can be done by dynamic programming

50 M.I. Example (rtificial) * * MI: U U U U U U U U U U U U U U U U U U U U 2 0 U U 1 U U U U U U U U U U U U U U U U U U ols 1 & 9, 2 & 8: perfect conservation & might be basepaired, but unclear whether they are. M.I. = 0 ols 3 & 7: No conservation, but always W- pairs, so seems likely they do base-pair. M.I. = 2 bits U ols 7->6: unconserved, but each letter in 7 has only 2 possible mates in 6. M.I. = 1 bit.

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53 MI-Based Structure-Learning # S i+1, j % S S i, j = max$ i, j"1 S i+1, j"1 + M i, j &% max i< j<k S i,k + S k +1, j just like Nussinov/Zucker folding BUT, need enough data---enough sequences at right phylogenetic distance

54 Pseudoknots disallowed allowed # n % & " max j M $ i=1 i, j (/2 '

55

56 ccelerating M search Zasha Weinberg & W.L. Ruzzo Recomb 04, Bioinformatics 04, 06

57 Rfam database (Release 7.0, 3/2005) 503 ncrn families 280,000 annotated ncrns 8 riboswitches, 235 small nucleolar RNs, 8 spliceosomal RNs, 10 bacterial antisense RNs, 46 microrns, 9 ribozymes, 122 cis RN regulatory elements,

58 Rfam Input (hand-tuned): MS SS_cons Score Thresh T Window Len W Output: M scan results IRE (partial seed alignment): Hom.sap. UUUUUUUUUU Hom.sap. UUUUU.UUUUUUU Hom.sap. UUUUUUUUUU. Hom.sap. UUUU..UUUU.U Hom.sap. UUUUUUUUUUU Hom.sap. UUU..UUUU.UU Hom.sap. UUU..UUUUUU Hom.sap. UUU..UUU.UU Hom.sap. UUU..UUU.UU av.por. UUUUUUUU Mus.mus. UUU..UUU.UU Mus.mus. UUUUUUUUU Mus.mus. UUUUUUUUU Rat.nor. UUU..UU.UU Rat.nor. UUUUUUUUUU SS_cons <<<<<...<<<<<...>>>>>.>>>>>

59 ovariance Model Key difference of M vs HMM: Pair states emit paired symbols, corresponding to base-paired nucleotides; 16 emission probabilities here.

60 M s are good, but slow Rfam Reality EMBL Our Work EMBL Rfam oal EMBL Blast Z junk M hits 1 month, 1000 computers M hits ~2 months, 1000 computers junk M hits 10 years, 1000 computers

61 Oversimplified M (for pedagogical purposes only) U U U U

62 M to HMM 25 emisions per state 5 emissions per state, 2x states U U U U U U U U M HMM

63 Key Issue: 25 scores 10 M U U U U HMM Need: log Viterbi scores M HMM

64 Viterbi/Forward Scoring Path π defines transitions/emissions Score(π) = product of probabilities on π NB: ok if probabilities aren t, e.g. E.g. in M, emissions are odds ratios vs 0thorder background For any nucleotide sequence x: Viterbi-score(x) = max{ score(π) π emits x} Forward-score(x) = score(π) π emits x}

65 Key Issue: 25 scores 10 M U U U L R U HMM Need: log Viterbi scores M HMM P L + R P L + R P L + R P U L + R U P L + R P L + R P L + R P L + R P U L + R U P L + R NB:HMM not a prob. model

66 Rigorous Filtering ny scores satisfying the linear inequalities give rigorous filtering P L + R P L + R P L + R P U L + R U P L + R Proof: M Viterbi path score corresponding HMM path score Viterbi HMM path score (even if it does not correspond to any M path)

67 Some scores filter better P U = 1 L U + R P U = 4 L U + R ssuming U 25% Option 1: Opt 1: L U = R = R = 2 L U + (R + R )/2 = 4 Option 2: Opt 2: L U = 0, R = 1, R = 4 L U + (R + R )/2 = 2.5

68 Optimizing filtering For any nucleotide sequence x: Viterbi-score(x) = max{ score(π) π emits x } Forward-score(x) = score(π) π emits x } Expected Forward Score E(L i, R i ) = x Forward-score(x)*Pr(x) NB: E is a function of L i, R i only Under 0th-order background model Optimization: Minimize E(L i, R i ) subject to score L.I.s This is heuristic ( forward Viterbi filter ) But still rigorous because subject to score L.I.s

69 alculating E(L i, R i ) E(L i, R i ) = x Forward-score(x)*Pr(x) Forward-like: for every state, calculate expected score for all paths ending there, easily calculated from expected scores of predecessors & transition/ emission probabilities/scores

70 Minimizing E(L i, R i ) alculate E(L i, R i ) symbolically, in terms of emission scores, so we can do partial derivatives for numerical convex optimization algorithm "E(L 1, L 2,...) "L i Forward: Viterbi:

71 What should the probabilities be? onvex optimization problem onstraints: enforce rigorous property Objective function: filter as aggressively as possible Problem sizes: variables inequality constraints

72 Estimated Filtering Efficiency (139 Rfam 4.0 families) Filtering fraction # families (compact) # families (expanded) break even < verages 283 times faster than M

73 Results: buried treasures Name # found BLST + M # found rigorous filter + M # new Pyrococcus snorn Iron response element Histone 3 element Purine riboswitch Retron msr Hammerhead I Hammerhead III U4 snrn S-box U6 snrn U5 snrn U7 snrn

74 What if filtering is poor? Profile HMM filter discards structure info Surprise is that they usually do very well But not always; e.g. a dozen families with filtering >.01, including stars like trn Three ideas: Sub M: graft in SM for a critical part Store Pair: retain a few critical pairs Filter hains: run fast, crude filters first

75 Full M Profile HMM Sub-M filters U U U UUU Sub-M sub-m U UU Sub-profile-HMM

76 Store-pair filters Full M U U U Store pair UUU Profile HMM:

77 T Filter hains Rigorous filter Rigorous filter Rigorous filter M ncrns

78 Why run filters in series? Filter 1 Filter 2 M Filtering fraction N/ Run time (sec/kbase) M alone: 200 s/kb Filter 2 M: *200 = 12 s/kb Filter 1 Filter 2 M: * *200 = 5.5 s/kb

79 Run time (sec/kb) Store pair 1E Filtering fraction Sub-M 1E Properties of a filter: Filtering fraction Run time (sec/kb)

80 Run time (sec/kb) Store pair 1E Filtering fraction Sub-M 1E Simplified performance model (selectivity and speed) Independence assumptions for base pairs Use dynamic programming to rapidly explore base pair combinations

81 Run time (sec/kb) Store pair 1E Filtering fraction Sub-M 1E Optimal rigorous filter series E

82 Estimated M time (days) faster Results: faster M: 30 years (your career) Filters: 1 month (time between school terms) 100 faster 10 faster Rigorous series of filters + M time (days)

83 Results: more sensitive than BLST # with BLST+M # with rigorous filters + M # new Rfam trn roup II intron Iron response element tmrn Lysine riboswitch nd more

84 Is there anything more to do? Rigorous filters can be too cautious E.g., 10 times slower than heuristic filters Yet only 1-3% more sensitive We want to Run scans faster with minimal loss of sensitivity Know empirically what sensitivity we re losing

85 Input Multiple Sequence lignment U <.> Heuristic Profile HMMs M Base paired columns Infinite Multiple sequence alignments U U <.> U U... (Weinberg & Ruzzo, 2006) Profile HMM

86 RO-like curves (lysine riboswitch) Filter sensitivity HMM BLST Filter sends 80% of hits to M Filtering fraction

87 trnscan-se: the leading brand (Lowe & Eddy 1997) Designed for trns Used in virtually every genome project Uses Ms Heuristics: selected 2 trn detection programs n ambitious target to shoot for

88 trnscan-se vs. heuristic HMMs rchaea Eubacteria. elegans Drosophila Human Sensitivity (%) t-se HMM Run time (hours) t-se HMM rigor (Each filter sends same number of nucleotides to M)

89 trnscan-se vs. heuristic HMMs Time to create heuristic filters for trns trnscan-se 8 papers HMM 10 seconds to type a command 15 minutes to create & calibrate HMM Point is not that heuristic HMM is better than trnscan- SE --- it s not; point is that it s in the ballpark, so may be easy way to get useful results for new families.

90 Building M s Hand-curated alignments + structure as in Rfam are great, but it doesn t scale Example pplication: iven 5-20 upstream regions (~500 nt) of orthologous bacterial genes, some (but not all) plausibly regulated by a common riboswitch, could we find it?

91 Importance of lignment Blue boxes, e.g., should be lined up. Structure is invisible otherwise.

92 SI () and SPS (B) as functions of the sequence identity for dataset 2 (see text for details) ardner, P. P. et al. Nucl. cids Res : ; doi: /nar/gki 541 opyright restrictions may apply.

93 Pfold (KH03) Test Set D Trusted alignment lustalw lignment Evolutionary Distance Knudsen & Hein, Pfold: RN secondary structure prediction using stochastic context-free grammars, Nucleic cids Research, 2003, v 31,

94 MFinder Harder: Finding Ms without alignment Yao, Weinberg & Ruzzo, Bioinformatics, 2006 Folding predictions Smart heuristics andidate alignment M Search Realign

95 Mfinder ccuracy (on Rfam families with flanking sequence) /W /W

96 Summary of Rfam test families and results

97 L i = column i; σ = (α, β) the 2 ary struct, α = unpaired, β = paired cols With MLE params, I ij is the mutual information between cols i and j

98 an find it via a simple dynamic programming alg.

99 omputational Pipeline for High Throughput Discovery of cis Regulatory Noncoding RN in Prokaryotes to appear, PLoS omp Biol Zizhen Yao 1,*, Jeffrey Barrick 4,6, Zasha Weinberg 5, Shane Neph 1,2, Ronald Breaker 3,4,5, Martin Tompa 1,2 and Walter L. Ruzzo 1,2

100 n approach for cis-regulatory RN discovery in bacteria 1. et all sequenced bacterial genomes 2. roup upstream sequences per DD 3. Find most promising genes, based on sequence motifs conserved in group 4. From those, find most promising candidates, incorporating structure in the motifs 5. From those, genome-wide searches for more instances 6. Expert analyses (Breaker Lab, Yale)

101 Identify DD group members 2946 DD groups Retrieve upstream sequences < 10 PU days Footprinter ranking < 10 PU days Mfinder 1 ~ 2 PU months motifs Motif postprocessing 1740 motifs RaveNn 10 PU months Mfinder refinement < 1 PU month Motif postprocessing 1466 motifs

102 enome Scale Search: Why Most riboswitches, e.g., are present in ~5 copies per genome Throughout (most of) clade More examples give better model, hence even more examples, fewer errors More examples give more clues to function

103 enome Scale Search: How Mfinder is directly usable for/with search Folding predictions Smart heuristics andidate alignment M Search Realign

104 Results Process largely complete in bacillus/clostridia gamma proteobacteria cyanobacteria actinobacteria nalysis ongoing

105 Some Preliminary ctino Results Rfam Family Type (metabolite) Rank THI riboswitch (thiamine) 4 ydao-yua riboswitch (unknown) 19 obalamin riboswitch (cobalamin) 21 SRP_bact gene 28 RFN riboswitch (FMN) 39 yybp-ykoy riboswitch (unknown) 48 gcvt riboswitch (glycine) 53 S_box riboswitch (SM) 401 tmrn gene Not found RNaseP gene Not found not cisregulatory

106 More Prelim ctino Results Many others (not in Rfam) are likely real of top 50: known (Rfam, 23S) 10 probable (Tbox, IRE, Lex, parp, pyrr) 7 ribosomal genes 9 potentially interesting 12 unknown or poor 12 One other being bench-verified

107 mrn leader mrn leader switch?

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109 Rank Score # DD Rfam RV MF FP RV MF ID ene Descriptio n IlvB Thiamine pyrophosphate-requiring enzymes RF00230 T-box O3859 Predicted membrane protein RF00059 THI MetH Methionine synthase I specific DN methylase RF00162 S_box O0116 Predicted N6-adenine-specific DN methylase RF00011 RNaseP_bact_b DHBP 3,4-dihydroxy-2-butanone 4-phosphate synthase RF00050 RFN ua MP synthase RF00167 Purine cvp lycine cleavage system protein P RF00504 lycine DUF149 Uncharacterised BR, YbaB family O0718 RF00169 SRP_bact bib obalamin biosynthesis protein obd/bib RF00174 obalamin Lys Diaminopimelate decarboxylase RF00168 Lysine Ter Membrane protein Ter RF00080 yybp-ykoy MgtE Mg/o/Ni transporter MgtE RF00380 ykok lms lucosamine 6-phosphate synthetase RF00234 glms OpuBB B-type proline/glycine betaine transport systems RF00005 trn EmrE Membrane transporters of cations and cationic drug RF00442 ykk-yxkd O0398 Uncharacterized conserved protein RF00023 tmrn Table 1: Motifs that correspond to Rfam families

110 Table 3: High ranking motifs not found in Rfam Rank # DD ene: Description nnotation DHOase IIa: Dihydroorotase PyrR attenuator [22] RplL: Ribosomal protein L7/L1 L10 r-protein leader; see Supp RpsF: Ribosomal protein S6 S6 r-protein leader O1179: Dinucleotide-utilizing enzymes 6S RN [25] RpsJ: Ribosomal protein S10 S10 r-protein leader; see Supp Resolvase: N terminal domain Inf: Translation initiation factor 3 IF-3 r-protein leader; see Supp RpsD: Ribosomal protein S4 and related proteins S4 r-protein leader; see Supp [30] rol: haperonin roel Hrc DN binding site [46] Ribosomal L21p: Ribosomal prokaryotic L21 protein L21 r-protein leader; see Supp ad: admium resistance transporter [47] RplB: Ribosomal protein L2 S10 r-protein leader RN pol Rpb2 1: RN polymerase beta subunit O3830: T domain-containing protein Ribosomal S2: Ribosomal protein S2 S2 r-protein leader Rps: Ribosomal protein S7 S12 r-protein leader O2984: B-type uncharacterized transport system tsr: Firmicutes transcriptional repressor of class III tsr DN binding site [48] Formyl trans N: Formyl transferase PurE: Phosphoribosylcarboxyaminoimidazole O4129: Predicted membrane protein RplO: Ribosomal protein L15 L15 r-protein leader RpmJ: Ribosomal protein L36 IF-1 r-protein leader na B: na protein B-type domain Ribosomal S12: Ribosomal protein S12 S12 r-protein leader Ribosomal L4: Ribosomal protein L4/L1 family L3 r-protein leader O0742: N6-adenine-specific methylase ylbh putative RN motif [4] Pencillinase R: Penicillinase repressor BlaI, MecI DN binding site [49] Ribosomal S9: Ribosomal protein S9/S16 L13 r-protein leader; Fig Ribosomal L19: Ribosomal protein L19 L19 r-protein leader; Fig ap: lyceraldehyde-3-phosphate dehydrogenase/erythrose O4708: Predicted membrane protein O0325: Predicted enzyme with a TIM-barrel fold RpmF: Ribosomal protein L32 L32 r-protein leader LDH: L-lactate dehydrogenases spr: Predicted rrn methylase Fus: Translation elongation factors EF- r-protein leader

111 Rfam Membership Overlap Structure # Sn Sp nt Sn Sp bp Sn Sp RF00174 obalamin RF00504 lycine RF00234 glms RF00168 Lysine RF00167 Purine RF00050 RFN RF00011 RNaseP_bact_b RF00162 S_box RF00169 SRP_bact RF00230 T-box RF00059 THI RF00442 ykk-yxkd RF00380 ykok RF00080 yybp-ykoy mean median Tbl 2: Prediction accuracy compared to prokaryotic subset of Rfam full alignments. Membership: # of seqs in overlap between our predictions and Rfam s, the sensitivity (Sn) and specificity (Sp) of our membership predictions. Overlap: the avg len of overlap between our predictions and Rfam s (nt), the fractional lengths of the overlapped region in Rfam s predictions (Sn) and in ours (Sp). Structure: the avg # of correctly predicted canonical base pairs (in overlapped regions) in the secondary structure (bp), and sensitivity and specificity of our predictions. 1 fter 2nd RaveNn scan, membership Sn of lycine and obalamin increased to 76% and 98% resp., lycine Sp unchanged, but obalamin Sp dropped to 84%.

112 Identification of 22 candidate structured RNs in bacteria using the Mfinder comparative genomics pipeline to appear, NR Zasha Weinberg 1,*, Jeffrey E. Barrick 2,3, Zizhen Yao 4, dam Roth 2, Jane N. Kim 1, Jeremy ore 1, Joy Xin Wang 1, Elaine R. Lee 1, Kirsten F. Block 1, Narasimhan Sudarsan 1, Shane Neph 5, Martin Tompa 4,5, Walter L. Ruzzo 4,5 and Ronald R. Breaker 1,2,3,*

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114 Motif RN? is? Switch? Phylum/class M,V ov. # Non cis EMM Y Y y Widespread V /309 Moco Y Y Y Widespread M,V /81 SH Y Y Y Proteobacteria M,V /41 SM-IV Y Y Y ctinobacteria V /54 O4708 Y Y y Firmicutes M,V /23 suc Y Y y -proteobacteria /40 23S-methyl Y Y n Firmicutes /37 hemb Y?? -proteobacteria V /50 (anti-hemb) (n) (n) (37) (31/37) MEB? Y n -proteobacteria /646 mini-ykk Y Y? Widespread V /205 purd y Y? -proteobacteria M /20 6 y? n ctinobacteria /27 alphatransposases? N N -proteobacteria /99 excisionase?? n ctinobacteria /27 TP y?? yanobacteria /23 cyano-30s Y Y n yanobacteria /23 lacto-1?? n Firmicutes /95 lacto-2 y N n Firmicutes /355 TD-1 y? n Spirochaetes M,V /29 TD-2 y N n Spirochaetes V /36 coccus-1? N N Firmicutes /189 gamma-150? N N -proteobacteria /27

115 RN Structures in Diverged enomic Regions of Vertebrates Torarinsson E.1,2, Yao, Z.3, RuzzoW.L.3,4and orodkin J.1

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117 Software Infernal - (Eddy et al.) most of Eddy & Durbin RaveNn - (Weinberg) fast filtering Mfinder - (Yao) Motif discovery (local alignment)

118 Open Problems - Better M s Optional- and variable-length stems Riboswitches & other regulatory RNs often switch between conformations; better search & alignment exploiting both alternatives? ugmented M handling pseudoknots probably too slow for scan, but plausibly could be used for alignment Better use of prior knowledge? (NR tetraloops, single-stranded s )

119 Open Problems - Better algorithms & scoring incorporating phylogeny in model construction & scoring e.g. mutual information ignores it improve scoring by shuffling other ideas for scan filtering comparing & clustering RN structures search/alignment/inference with splicing

120 Open Problems - pplications & Biology clustering intergenic sequences, esp prokaryotic systematic look at eukaryotic UTRs how to cluster? how to score? swiss-cheese phylogenies evidence for selection (no dn/ds)

121 Summary ncrn is a hot topic For family homology modeling: Ms Training & search like HMM (but slower) Dramatic acceleration possible utomated model construction New computational methods lead to new discoveries