Detecting non-coding RNA in Genomic Sequences
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1 Detecting non-coding RNA in Genomic Sequences I. Overview of ncrnas II. What s specific about RNA detection? III. Looking for known RNAs IV. Looking for unknown RNAs Daniel Gautheret INSERM ERM 206 & Université de la Méditerranée
2 Non-coding or non-messenger RNA All organisms rrna 5S/5.8S 15S/18S 23S/28S (5-300 copies) RNAse P/MRP (1 copy) trna (20 diff., copies) Eukaryotes and Archaes snornas (H/ACA and C/D: different) Eukaryotes mirnas ( diff.) XIST, H19, IPW (vertebrates) snrnas (U1, U2, U4, U5, U6) Procaryotes rpra, csrb, oxys, tmrna rrna & RNAse P: catalytic!
3 Vertebrate Genomes: > 50% transcribed! Vertebrate gene: 30kb (coding: 1,5kb) Introns, UTRs Intergenic DNA Transcription: T T T? T T Transposable elements Coding regions: 1.5% Satellite DNA trna, rrna
4 How many other ncrnas? Rnomics* Extract total RNA Isolate small RNAs (> mrna size) Tag & Reverse transcribe Clone & Sequence Success 80 ncrnas identified in mouse Lots of new snornas Limitations Access to nuclear RNAs Tissue/time specific expression may be frequent * Huttenhofer et al., 2001
5 Bioinformatics Not sensitive to «rare» expression Highly succesful in identifying protein-coding genes Many complete genomes available Large computational toolbox Statistics Thermodynamics Phylogeny
6 What s Special About ncrna Detection? s s BLAST HMM s No ORF No Markov model / sequence statistics ncrna is defined both by primary and secondary structure «Substitution matrices» for nucleic acids are terrible compared to aminoacids counterparts
7 III. Looking for known ncrnas How can you detect ncrna genes from known families?
8 Custom RNA search programs Based on a variety of algorithms: seek regular expressions or base pairs, weight matrices, SCFGs, etc. trna trnascan (Fichant & Burks 91) trnascan-se (Eddy 94) C/D Box snornas (Lowe & Eddy 99) One type of snorna mirna Not an automated procedure
9 Descriptor-based programs RnaMot / Rnamotif (Gautheret 91, Macke 02) Palingol (Viari 96) Patscan (Overbeek 00) PatSearch (Pesole 01) h1 s1 h1 s2 h2 s3 h2 h1 5:5 1 h2 5:5 NNNNR:YNNNN s1 7:7 NUNNNNN s2 4:40 s3 7:7 UUCNNNN RnaMot descriptor for anticodon+tyc domain of trna
10 Descriptor-based programs PROS CONS Draft descriptors can be quickly sketched and tested Requires a good prior knowledge of secondary structure and sequence constraints No alignment is required, although it is very helpful to have one Requires basic computer skills to translate biological constraints into the descriptor language Biologists decide what features are important or not (see also CONS!) Biologists have the responsibility of correctly weighting each important feature
11 Probabilistic ncrna search programs Stochastic Context Free Grammars (first adaptation of CFG to RNA: Searls 94; SCFG: Eddy & Durbin 94) Training set Production rules describe how to generate any structure in the training set Time cost = O(N 4 ) for sequence of length N Not «practical» for large alignments or genome-wide searches Pseudoknots not allowed
12 ERPIN: Profile-based search Gautheret & Lambert, JMB, 2001, 313, p Training set Helix profile (16xN) A:A G:A C:A U:A A:G G:G C:G U:G... U:U A G C U - Single-strand profile (5xN) Search algorithm combines dynamic programming for single strands and profile search for helices
13 Profile-based search PROS All constraints in the training set are efficiently exploited, resulting in highly specific detections After alignments and secondary structures are created, no further programming is needed Scoring system is defined automatically E-values are provided for each hit CONS Alignment and secondary structure constraints must be accurate Helices of variable length need to be reduced to their shortest consensus Program will not depart from initial alignment in terms of motif size Users still have to decide on search order and masked elements
14 Running a successful ncrna search Example: the Signal Recognition Particle (SRP) RNA 172 sequences available All 3 kingdoms Signature: 50-nt domain IV
15 Organize ncrna information Alignment is a must Should be structure-based ClustalW OK only as a first attempt RNAalifold (Vienna package) can identify covarying basepairs Secondary Structure annotation Will help identify sequence/ structure constraints: helix sizes, conserved bases, etc.
16 Want to publish your finds? Prepare Control Procedures Sensitivity: SN = Specificity : SP = TP TP+FN TP TP+FP Total «true» objects Total predictions TP and FN: easy to obtain, using training set (leave-one out) FP: harder! How do you know a hit is false? Hint: express SP as: FP / Mb in a random sequence Make it large enough and of same composition (mono & dint) as search database (e.g. with the shuffle program)
17 Using the ERPIN program >structure >AQU.AEO. AGGGUGAACU-CCCCCAGGCCCGAA--AGGGAGCAAGGGUAAGC-CCG >THE.THE. GGCGUGAACC-GGGUCAGGUCCGGA--AGGAAGCAGCCCUAAGC-GCC erpin srp.epn sequence.fasta -8,8 -nomask erpin srp.epn sequence.fasta 2,2 -nomask erpin srp.epn sequence.fasta -2,2 umask 5 9 -nomask
18 ERPIN results Score: based on profile values E-value: How many hits expected at this score or higher? No need for random sequence tests!
19 The ERPIN Server All searches parameterized to scan a bacterial genome in less than 5 minutes
20
21 IV. De novo ncrna finding How can we detect ncrna genes when no prior sequence/structure data is available?
22 Exciting times for comparative genomics 5-6% of mamalian genome under selection vs 1.5% coding 3 times as much as in nematodes! «Intergenic regions might hold the key to the complexity of mammals»
23 Functional assignment of conserved regions Coding exons Regulatory non coding exons and introns Promoters ncrna Ancestral repeats Others (matrix attachment, etc.) Fraction of conserved sequences in.. (AR=ancestral repeats) Margulies et al, 2003 Detect this!
24 Thermodynamic Profiling (Le et al. 88) 100 bases 100 bases 100 bases 100 bases 100 bases Profile : Z-score = window free energy - mean (energy of rnd seq.) Var(energy of rnd seq.) New software by Hofacker et al: (RNALfold)
25 The problem with thermodynamics OK for strong local structures (some success in viral genomes) However: true ncrna (trna, rrna) do not display higher folding energy than random sequences of same composition (di-nt: Rivas & Eddy 2000)
26 G+C content G+C content alone is a better ncrna predictor than free energy In high A+T background (thermophilic archaebacteria), ncrna stand out clearly. Combining (G+C)% and CpG% provides the best discriminant (Schattner 02). Does not work in genomes with «normal» G+C contents, except as a complement to other methods (thermodynamics, etc.) See software RNAGenie (Carter, Dubchak & Holbrook, 2001). - Combines energy and G+C contents
27 Comparative Genomics + experiments Bacteria: microarray + Northern in different growth conditions From Wassarman et al. 01 Seq: Escherichia, Salmonella, Klebsiella Wassarman et. al. 01: 60 ncrna predicted, 23 confirmed Argaman et al. 01: 24 predicted, 14 confirmed
28 Q-RNA (Rivas & Eddy 2001) Analysis of Blast alignment (SCFG based) Model for protein coding gene Synonymous mutations Model for ncrna (also include loop probabilities obtained from training set of real ncrna) Compensatory mutations
29 Q-RNA results Limited range for similarity (65%-85%) Too dissimilar: incorrect Blast alignments Too similar: no covariation Problem: Human/mouse/rat ncrnas not in this range! E.coli vs Salmonella typhy: analysis of ~5000 Blast hits 115 true ncrnas 33 with Blastn alignments in the 65%-85% range 33 detected as ncrna 440 other candidates (half of them known elements: terminators, palindromic repetitive elements, etc.)
30 Comparative Genomics & mirna Criteria: Loose conservation human/mouse/fugu Fall outside of protein coding gene Predictedtoformstem-loop hits Score based on resemblance to 21mer mirna 107 potential new mirnas Lim et al. Science 2003
31 The right species for ncrna detection? Human/mouse ncrna: ~98-100% id 18S fugu/xenopus/human: 95% id! Still too close Obvious interest for older animals Human/mouse Asp trnas
32 Multiple species is the key Multiple alignments will enable covariation detection Covariation + GC-content + energy will provide enough evidence for ncrna status
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