Modeling and Searching for Non-Coding RNA

Transcription

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

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

3 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

4 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

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

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

7 ncrn Example: IRE Iron Response Element: a short conserved stemloop, bound by iron response proteins (IRPs). Found in TRs 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' TR. When iron concentration is low, IRPs bind the ferritin mrn IRE. repressing translation. Binding of multiple IREs in the 3' and 5' TRs 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.

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

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

10 E.g.: the lycine Riboswitch lycine - simplest amino acid ses - make proteins, make energy Not enough OR too much - wasteful Hypothetical answer: g prot g g prot g gcvt protein gcvt (glycine cleavage) g gcvt promoter

11 The lycine Riboswitch ctual answer (in many bacteria): Look Ma, no protein g g gcvt protein g gcvt mrn gcvt (glycine cleavage)

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

13 entral Dogma = entral hicken & Egg? DN RN Protein gene Protein DN (chromosome) cell RN (messenger) Was there once an RN World?

14 Outline ncrn: what/why? What does computation bring? How to model and search for ncrn? Faster search Better model inference

15 Iron Response Element IRE (partial seed alignment): Hom.sap. Hom.sap.. Hom.sap.. Hom.sap.... Hom.sap. Hom.sap.... Hom.sap... Hom.sap.... Hom.sap.... av.por. Mus.mus.... Mus.mus. Mus.mus. Rat.nor.... Rat.nor. SS_cons <<<<<...<<<<<...>>>>>.>>>>>

16 6S mimics an open promoter E.coli Barrick et al. RN 2005 Trotochaud et al. NSMB 2005 Willkomm et al. NR 2005

17 Dengue virus genome: ORF 3 element Known distribution (96% sequence identity) Dengue virus With our techniques (70% sequence identity) Dengue virus West Nile virus Yellow Fever Omsk Hemorrhagic fever Japanese encephalitis Tick-borne encephalitis Kunjin virus Langat virus Louping ill Murray Valley virus Powassan virus

18 polyadenylation inhibition element RN 1 small nuclear ribonucleoprotein RN element 3 TR Known distribution (90% sequence identity) Human, mouse, rabbit With our techniques (75% sequence identity) Human, mouse, rabbit Zebrafish, Tetraodon, Fugu Frog

19 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)

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

21 The lycine Riboswitch ctual answer (in many bacteria): Look Ma, no protein g g g g gcvt mrn gcvt protein gcvt (glycine cleavage)

22 (Mandal, Lee, Barrick, Weinberg, Emilsson, Ruzzo, Breaker, Science 2004) 5 nd More examples means better alignment nderstand phylogenetic distribution Find riboswitch in front of new gene gcvt ORF 3

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24 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 BT: slow, inaccurate, demanding

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

26 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

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

28 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

29 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 >>>>>>>

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

31 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

32 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

33 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)

34 Example: searching for trns

35 lignment Quality

36 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

37 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)

38 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

39 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 #)

40 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 ] bifurcation

41 Model Training

42 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 sequence conservation but perfect pairing MI = expected score gain from using a pair state Finding optimal MI, (i.e. optimal pairing of columns) is NP-hard(?) Finding optimal MI without pseudoknots can be done by dynamic programming

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44 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 BT, need enough data---enough sequences at right phylogenetic distance

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

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

48 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,

49 Rfam Input (hand-tuned): MS SS_cons Score Thresh T Window Len W Output: M scan results IRE (partial seed alignment): Hom.sap. Hom.sap.. Hom.sap.. Hom.sap.... Hom.sap. Hom.sap.... Hom.sap... Hom.sap.... Hom.sap.... av.por. Mus.mus.... Mus.mus. Mus.mus. Rat.nor.... Rat.nor. SS_cons <<<<<...<<<<<...>>>>>.>>>>>

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

51 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

52 Oversimplified M (for pedagogical purposes only)

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

54 Key Issue: 25 scores 10 M HMM Need: log Viterbi scores M HMM

55 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}

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

57 Rigorous Filtering ny scores satisfying the linear inequalities give rigorous filtering P L + R P L + R P L + R P L + R 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)

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

59 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 nder 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

60 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

61 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 a numerical convex optimization algorithm "E(L 1,L 2,...) "L i

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

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

64 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 snrn S-box snrn snrn snrn

65 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

66 Full M Profile HMM Sub-M filters Sub-M sub-m Sub-profile-HMM

67 Store-pair filters Full M Store pair Profile HMM:

68 T Filter hains Rigorous filter Rigorous filter Rigorous filter M ncrns

69 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

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

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

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

73 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)

74 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

75 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

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

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

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

79 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)

80 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.

81 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?

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

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

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

85 Early Semi-automated Example Started with 16 genes orthologous to fol in B. subtilis Found 10 sharing good structural motif Searched all bacterial genomes for this motif Found 234 hits Realigned these to refine structural motif Found 367 hits 257 match RFM s T-box (Based on hand-curated alignment of 67 knowns)

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87 Initial orthologs Found by scan hloroflexus aurantiacus hloroflexi eobacter metallireducens eobacter sulphurreducens δ -Proteobacteria Symbiobacterium thermophilum

88 n approach for cis-regulatory RN discovery in bacteria 1. hoose a bacterial genome 2. For each gene, collect close orthologs 3. Find most promising genes, based on sequence motifs conserved among orthologs 4. From those, find most promising genes, incorporating structure in the motifs 5. From those, genome-wide searches for more instances 6. Expert analyses (Breaker Lab, Yale)

89 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

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

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

92 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

93 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

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

95 Summary ncrn is a hot topic For family homology modeling: Ms Training & search like HMM (but slower) Dramatic acceleration possible utomated model construction Hopefully leading to new discoveries