Secondary structure prediction method based on conditional log-linear models (CLLMs), a flexible class of probabilistic models which generalize upon SCFGs by using discriminative training and feature-rich scoring.
MFE RNA structure prediction algorithm. Includes an implementation of the partition function for computing basepair probabilities and circular RNA folding.
MFE RNA structure prediction based on abstract shapes. Shape abstraction retains adjacency and nesting of structural features, but disregards helix lengths, thus reduces the number of suboptimal solutions without losing significant information. Furthermore, shapes represent classes of structures for which probabilities based on Boltzmann-weighted energies can be computed.
A program to predict lowest free energy structures and base pair probabilities for RNA or DNA sequences. Programs are also available to predict Maximum Expected Accuracy structures and these can include pseudoknots. Structure prediction can be constrained using experimental data, including SHAPE, enzymatic cleavage, and chemical modification accessibility. Graphical user interfaces are available for Windows and for Mac OS-X/Linux. Programs are also available for use with Unix-style text interfaces. Additionally, a C++ class library is available.
The UNAFold software package is an integrated collection of programs that simulate folding, hybridization, and melting pathways for one or two single-stranded nucleic acid sequences.
Crumple is simple, cleanly written software for producing the full set of possible secondary structures for a single sequence, given optional constraints.
A Python library for the probabilistic sampling of RNA structures that are compatible with a given nucleotide sequence and that are RNA-like on a local length scale.
An integrated platform for de novo and homology modeling of RNA 3D structures, where coordinate file input, sequence editing, sequence alignment, structure prediction and analysis features are all accessed from a single intuitive graphical user interface.
The single sequence methods mentioned above have a difficult job detecting a small sample of reasonable secondary structures from a large space of possible structures. A good way to reduce the size of the space is to use evolutionary approaches. Structures that have been conserved by evolution are far more likely to be the functional form. The methods below use this approach.
an expectation maximization algorithm using covariance models for motif description. Uses heuristics for effective motif search, and a Bayesian framework for structure prediction combining folding energy and sequence covariation.
an algorithm that improves the accuracy of structure prediction by combining free energy minimization and comparative sequence analysis to find a low free energy structure common to two sequences without requiring any sequence identity.
A pairwise RNA structural alignment tool based on the comparison of RNA trees. Considers alignments in which the compared trees can be rooted differently (with respect to the standard “external loop” corresponding roots), and/or permuted with respect to branching order.
Fast RNA structural clustering method of local RNA secondary structures. Predicted clusters are refined using LocARNA and CMsearch. Due to the linear time complexity for clustering it is possible to analyse large RNA datasets.
LocaRNA is the successor of PMcomp with an improved time complexity. It is a variant of Sankoff's algorithm for simultaneous folding and alignment, which takes as input pre-computed base pair probability matrices from McCaskill's algorithm as produced by RNAfold -p. Thus the method can also be viewed as way to compare base pair probability matrices.
A sampling approach using Markov chain Monte Carlo in a simulated annealing framework, where both structure and alignment is optimized by making small local changes. The score combines the log-likelihood of the alignment, a covariation term and the basepair probabilities.
This method uses multiple Dynalign calculations to find a low free energy structure common to any number of sequences. It does not require any sequence identity.
A method for joint prediction of alignment and common secondary structures of two RNA sequences using a probabilistic model based on pseudo free energies obtained from precomputed base pairing and alignment probabilities.
Formally integrates both the energy-based and evolution-based approaches in one model to predict the folding of multiple aligned RNA sequences by a maximum expected accuracy scoring. The structural probabilities are calculated by RNAfold and Pfold.
PMcomp is a variant of Sankoff's algorithm for simultaneous folding and alignment, which takes as input pre-computed base pair probability matrices from McCaskill's algorithm as produced by RNAfold -p. Thus the method can also be viewed as way to compare base pair probability matrices. PMmulti is a wrapper program that does progressive multiple alignments by repeatedly calling pmcomp
uses RNAlpfold to compute the secondary structure of the provided sequences. A modified version of T-Coffee is then used to compute the multiple sequence alignment having the best agreement with the sequences and the structures. R-Coffee can be combined with any existing sequence alignment method.
This algorithm predicts conserved structures in any number of sequences. It uses probabilistic alignment and partition functions to map conserved pairs between sequences, and then iterates the partition functions to improve structure prediction accuracy
The structure based sequence alignment (SBSA) algorithm within RNA123 utilizes a novel suboptimal version of the Needleman-Wunsch global sequence alignment method that fully accounts for secondary structure in the template and query. It also utilizes two separate substitution matrices that are optimized for RNA helices and single stranded regions. The SBSA algorithm provides >90% accurate sequence alignments even for structures as large as bacterial 23S rRNA (~2800 nts).
enumerates the near-optimal abstract shape space, and predicts as the consensus an abstract shape common to all sequences, and for each sequence, the thermodynamically best structure which has this abstract shape.
A probabilistic sampling approach that combines intrasequence base pairing probabilities with intersequence base alignment probabilities. This is used to sample possible stems for each sequence and compare these stems between all pairs of sequences to predict a consensus structure for two sequences. The method is extended to predict the common structure conserved among multiple sequences by using a consistency-based score that incorporates information from all the pairwise structural alignments.
Stem Candidate Aligner for RNA (Scarna) is a fast, convenient tool for structural alignment of a pair of RNA sequences. It aligns two RNA sequences and calculates the similarities of them, based on the estimated common secondary structures. It works even for pseudoknotted secondary structures.
an alignment tool designed to provide multiple alignments of non-coding RNAs following a fast progressive strategy. It combines the thermodynamic base pairing information derived from RNAfold calculations in the form of base pairing probability vectors with the information of the primary sequence.
A tool for predicting non-coding RNA secondary structures including pseudoknots. It takes in input an alignment of RNA sequences and returns the predicted secondary structure(s).It combines criteria of stability, conservation and covariation in order to search for stems and pseudoknots. Users can change different parameters values, set (or not) some known stems (if there are) which are taken into account by the system, choose to get several possible structures or only one, search for pseudoknots or not, etc.
a webserver that makes it possible to simultaneously use a number of state of the art methods for performing multiple alignment and secondary structure prediction for noncoding RNA sequences.
a program for analysis of multiple sequence alignments using phylogenetic grammars, that may be viewed as a flexible generalization of the "Pfold" program.
Computes the full unpseudoknotted partition function of interacting strands in dilute solution. Calculates the concentrations, mfes, and base-pairing probabilities of the ordered complexes below a certain complexity. Also computes the partition function and basepairing of single strands including a class of pseudoknotted structures. Also enables design of ordered complexes.
Predicts bimolecular secondary structures with and without intramolecular structure. Also predicts the hybridization affinity of a short nucleic acid to an RNA target.
calculates the partition function and thermodynamics of RNA-RNA interactions. It considers all possible joint secondary structure of two interacting nucleic acids that do not contain pseudoknots, interaction pseudoknots, or zigzags.
calculates the thermodynamics of RNA-RNA interactions. RNA-RNA binding is decomposed into two stages. (1) First the probability that a sequence interval (e.g. a binding site) remains unpaired is computed. (2) Then the binding energy given that the binding site is unpaired is calculated as the optimum over all possible types of bindings.
MicroRNAs regulate protein coding gene expression by binding to 3' UTRs, there are tools specifically designed for predicting these interactions. For an evaluation of target prediction methods on high-throughput experimental data see (Selbach et al., Nature 2008) [85] and (Alexiou et al., Bioinformatics 2009)[86]
Name
Description
Species Specific
Intra-molecular structure
Comparative
Link
References
Diana-microT
DIANA-microT 3.0 is an algorithm based on several parameters calculated individually for each microRNA and it combines conserved and non-conserved microRNA recognition elements into a final prediction score.
The first link (predictions) provides RNA22 predictions for all protein coding transcripts in human, mouse, roundworm, and fruit fly. It allows you to visualize the predictions within a cDNA map and also find transcripts where multiple miR's of interest target. The second web-site link (custom) first finds putative microRNA binding sites in the sequence of interest, then identifies the targeted microRNA.
Sylamer is a method for finding significantly over or under-represented words in sequences according to a sorted gene list. Typically it is used to find significant enrichment or depletion of microRNA or siRNA seed sequences from microarray expression data.
TAREF stands for TARget REFiner. It predicts microRNA targets on the basis of multiple feature information derived from the flanking regions of the predicted target sites where traditional structure prediction approach may not be successful to assess the openness. It also provides an option to use encoded pattern to refine filtering.
p-TAREF stands for plant TARget REFiner. It identifies plant microRNA targets on the basis of multiple feature information derived from the flanking regions of the predicted target sites where traditional structure prediction approach may not be successful to assess the openness. It also provides an option to use encoded pattern to refine filtering. It first time employed power of machine learning approach with scoring scheme through Support Vector Regression(SVR) while considering structural and alignment aspects of targeting in plants with plant specific models. p-TAREF has been implemented in concurrent architecture in server as well as standalone form, making it one of the very few available target identification tools able to run concurrently on simple desktops while performing huge transcriptome level analysis accurately and fast. Besides this, it also provides an option to experimentally validate the predicted targets, on the spot, using expression data, which has been integrated in its back-end, to draw confidence on prediction along with SVR score.p-TAREF performance benchmarking has been done extensively through different tests and compared with other plant miRNA target identification tools. p-TAREF was found better performing.
Predicts biological targets of miRNAs by searching for the presence of conserved 8mer and 7mer sites that match the seed region of each miRNA. Predictions are ranked using site number, site type, and site context, which includes factors that influence target-site accessibility.
a comparative method for identifying functional RNA structures in multiple-sequence alignments. It is based on a probabilistic model-construction called a phylo-SCFG and exploits the characteristic differences of the substitution process in stem-pairing and unpaired regions to make its predictions.
This is the code from Elena Rivas that accompanies a submitted manuscript "Noncoding RNA gene detection using camparative sequence analysis". QRNA uses comparative genome sequence analysis to detect conserved RNA secondary structures, including both ncRNA genes and cis-regulatory RNA structures.
program for predicting structurally conserved and thermodynamic stable RNA secondary structures in multiple sequence alignments. It can be used in genome wide screens to detect functional RNA structures, as found in noncoding RNAs and cis-acting regulatory elements of mRNAs.
a program for analysis of multiple sequence alignments using phylogenetic grammars, that may be viewed as a flexible generalization of the "Evofold" program.
Given a search query, candidate homologs are identified using BLAST search and then tested for their known miRNA properties, such as secondary structure, energy, alignment and conservation, in order to assess their fidelity.
A SVM-based approach that, in conjunction with a non-stringent filter for consensus secondary structures, is capable of recognizing microRNA precursors in multiple sequence alignments.
RNAmmer uses HMMER to annotate rRNA genes in genome sequences. Profiles were built using alignments from the European ribosomal RNA database[112] and the 5S Ribosomal RNA Database.[113]
Uses a combination of RNA secondary structure prediction and machine learning that is designed to recognize the two major classes of snoRNAs, box C/D and box H/ACA snoRNAs, among ncRNA candidate sequences.
snoSeeker includes two snoRNA-searching programs, CDseeker and ACAseeker, specific to the detection of C/D snoRNAs and H/ACA snoRNAs, respectively. snoSeeker has been used to scan four human–mammal whole-genome alignment (WGA) sequences and identified 54 novel candidates including 26 orphan candidates as well as 266 known snoRNA genes.
"Easy RNA Profile IdentificatioN" is an RNA motif search program reads a sequence alignement and secondary structure, and automatically infers a statistical "secondary structure profile" (SSP). An original Dynamic Programming algorithm then matches this SSP onto any target database, finding solutions and their associated scores.
"INFERence of RNA ALignment" is for searching DNA sequence databases for RNA structure and sequence similarities. It is an implementation of a special case of profile stochastic context-free grammars called covariance models (CMs).
Ultra fast software for searching for RNA structural motifs employing an innovative index-based bidirectional matching algorithm combined with a new fast fragment chaining strategy.
Colorstock, a command-line script using ANSI terminal color; SScolor, a Perl script that generates static HTML pages; and Raton, an AJAX web application generating dynamic HTML. Each tool can be used to color RNA alignments by secondary structure and to visually highlight compensatory mutations in stems.
An RNA folding game that challenges players to come up with sequences that fold into a target RNA structure. The best sequences for a given puzzle are synthesized and their structures are probed through chemical mapping. The sequences are then scored by the data's agreement to the target structure and feedback is provided to the players.
Although NUPACK can be used to get useful statistics and properties of an RNA's structure as mentioned above, it's main goal is design of new sequences that fold into a desired structure.
RNAstructure has a viewer for structures in ct files. It can also compare predicted structures using the circleplot program. Structures can be output as postscript files.
Use RNAView to automatically identify and classify the types of base pairs that are formed in nucleic acid structures. Use RnamlView to arrange RNA structures.
A tool for the automated drawing, visualization and annotation of the secondary structure of RNA, initially designed as a companion software for web servers and databases
^Rivas E, Eddy SR (1999). "A dynamic programming algorithm for RNA structure prediction including pseudoknots". J. Mol. Biol.285 (5): 2053–68. doi:10.1006/jmbi.1998.2436. PMID9925784.
^ abcdefI.L. Hofacker, W. Fontana, P.F. Stadler, S. Bonhoeffer, M. Tacker, P. Schuster (1994). "Fast Folding and Perbandingan -- RNA Secondary Structures.". Monatshefte f. Chemie125 (2): 167–188. doi:10.1007/BF00818163.
^McCaskill JS (1990). "The equilibrium partition function and base pair binding probabilities for RNA secondary structure". Biopolymers29 (6-7): 1105–19. doi:10.1002/bip.360290621. PMID1695107.
^Hofacker IL, Stadler PF (2006). "Memory efficient folding algorithms for circular RNA secondary structures". Bioinformatics22 (10): 1172–6. doi:10.1093/bioinformatics/btl023. PMID16452114.
^Bompfünewerer AF, Backofen R, Bernhart SH, et al. (2008). "Variations on RNA folding and alignment: lessons from Benasque". J Math Biol56 (1-2): 129–144. doi:10.1007/s00285-007-0107-5. PMID17611759.
^Mathews DH, Turner DH (2002). "Dynalign: an algorithm for finding the secondary structure common to two RNA sequences". J. Mol. Biol.317 (2): 191–203. doi:10.1006/jmbi.2001.5351. PMID11902836.
^Mathews DH (2005). "Predicting a set of minimal free energy RNA secondary structures common to two sequences". Bioinformatics21 (10): 2246–53. doi:10.1093/bioinformatics/bti349. PMID15731207.
^Torarinsson E, Havgaard JH, Gorodkin J (2007). "Multiple structural alignment and clustering of RNA sequences". Bioinformatics23 (8): 926–32. doi:10.1093/bioinformatics/btm049. PMID17324941.
^Milo Nimrod, Zakov Shay, Katzenelson Erez, Bachmat Eitan, Dinitz Yefim, Ziv-Ukelson Michal (2012). "RNA Tree Comparisons via Unrooted Unordered Alignments". Algorithms in Bioinformatics7534: 135-148. doi:10.1007/978-3-642-33122-0_11.
^Heyne S, Costa F, Rose D, Backofen R (2012). "GraphClust: alignment-free structural clustering of local RNA secondary structures". Bioinformatics28 (12): i224-i232. doi:10.1093/bioinformatics/bts224. PMID22689765.
^Lindgreen S, Gardner PP, Krogh A (2006). "Measuring covariation in RNA alignments: physical realism improves information measures". Bioinformatics22 (24): 2988–95. doi:10.1093/bioinformatics/btl514. PMID17038338.
^Lindgreen S, Gardner PP, Krogh A (2007). "MASTR: multiple alignment and structure prediction of non-coding RNAs using simulated annealing". Bioinformatics23 (24): 3304–11. doi:10.1093/bioinformatics/btm525. PMID18006551.
^Xu Z, Mathews DH (2011). "Multilign: an algorithm to predict secondary structures conserved in multiple RNA sequences". Bioinformatics27 (5): 626–632. doi:10.1093/bioinformatics/btq726. PMID21193521.
^Kiryu H, Tabei Y, Kin T, Asai K (2007). "Murlet: a practical multiple alignment tool for structural RNA sequences". Bioinformatics23 (13): 1588–98. doi:10.1093/bioinformatics/btm146. PMID17459961.
^Wei D, Alpert LV, Lawrence CE (2011). "RNAG: a new Gibbs sampler for predicting RNA secondary structure for unaligned sequence". Bioinformatics27 (18): 2486–2493. doi:10.1093/bioinformatics/btr421. PMID21788211.
^Seetin MG, Mathews DH (2012). "TurboKnot: rapid prediction of conserved RNA secondary structures including pseudoknots". Bioinformatics28 (6): 792–798. doi:10.1093/bioinformatics/bts044. PMID22285566.
^Hofacker IL, Fekete M, Stadler PF (2002). "Secondary structure prediction for aligned RNA sequences". J. Mol. Biol.319 (5): 1059–66. doi:10.1016/S0022-2836(02)00308-X. PMID12079347.
^Reeder J, Giegerich R (2005). "Consensus shapes: an alternative to the Sankoff algorithm for RNA consensus structure prediction". Bioinformatics21 (17): 3516–23. doi:10.1093/bioinformatics/bti577. PMID16020472.
^Höchsmann M, Töller T, Giegerich R, Kurtz S (2003). "Local similarity in RNA secondary structures". Proc IEEE Comput Soc Bioinform Conf2: 159–68. PMID16452790.
^Höchsmann M, Voss B, Giegerich R (2004). "Pure multiple RNA secondary structure alignments: a progressive profile approach". IEEE/ACM Trans Comput Biol Bioinform1 (1): 53–62. doi:10.1109/TCBB.2004.11. PMID17048408.
^Hamada M, Tsuda K, Kudo T, Kin T, Asai K (2006). "Mining frequent stem patterns from unaligned RNA sequences". Bioinformatics22 (20): 2480–7. doi:10.1093/bioinformatics/btl431. PMID16908501.
^Xu X, Ji Y, Stormo GD (2007). "RNA Sampler: a new sampling based algorithm for common RNA secondary structure prediction and structural alignment". Bioinformatics23 (15): 1883–91. doi:10.1093/bioinformatics/btm272. PMID17537756.
^Tabei Y, Tsuda K, Kin T, Asai K (2006). "SCARNA: fast and accurate structural alignment of RNA sequences by matching fixed-length stem fragments". Bioinformatics22 (14): 1723–9. doi:10.1093/bioinformatics/btl177. PMID16690634.
^Dalli D, Wilm A, Mainz I, Steger G (2006). "STRAL: progressive alignment of non-coding RNA using base pairing probability vectors in quadratic time". Bioinformatics22 (13): 1593–9. doi:10.1093/bioinformatics/btl142. PMID16613908.
^Gerlach W, Giegerich R (2006). "GUUGle: a utility for fast exact matching under RNA complementary rules including G-U base pairing.". Bioinformatics22 (6): 762–764. doi:10.1093/bioinformatics/btk041. PMID16403789.
^Alexiou P, Maragkakis M, Papadopoulos GL, Reczko M, Hatzigeorgiou AG (2009). "Lost in translation: an assessment and perspective for computational microRNA target identification.". Bioinformatics25 (23): 3049–55. doi:10.1093/bioinformatics/btp565. PMID19789267.
^Krek A, Grün D, Poy MN, Wolf R, Rosenberg L, Epstein EJ, MacMenamin P, da Piedade I, Gunsalus KC, Stoffel M, Rajewsky N (2005). "Combinatorial microRNA target predictions.". Nat Genet37 (5): 495–500. doi:10.1038/ng1536. PMID15806104.
^Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E (2007). "The role of site accessibility in microRNA target recognition.". Nat Genet39 (10): 1278–84. doi:10.1038/ng2135. PMID17893677.
^Miranda KC, Huynh T, Tay Y, Ang YS, Tam WL, Thomson AM, Lim B, Rigoutsos I (2006). "A pattern-based method for the identification of MicroRNA binding sites and their corresponding heteroduplexes.". Cell126 (6): 1203–17. doi:10.1016/j.cell.2006.07.031. PMID16990141.
^Bartonicek N, Enright AJ (2010). "SylArray: A web-server for automated detection of miRNA effects from expression data.". Bioinformatics26 (22): 2900–1. doi:10.1093/bioinformatics/btq545. PMID20871108.
^R. Heikham and R. Shankar (2010). "Flanking region sequence information to refine microRNA target predictions.". Journal of Biosciences35 (1): 105–18. doi:10.1007/s12038-010-0013-7. PMID20413915.
^Lewis BP, Burge CB, Bartel DP (2005). "Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets.". Cell120 (1): 15–20. doi:10.1016/j.cell.2004.12.035. PMID15652477.
^Washietl S, Hofacker IL (2004). "Consensus folding of aligned sequences as a new measure for the detection of functional RNAs by comparative genomics". J. Mol. Biol.342 (1): 19–30. doi:10.1016/j.jmb.2004.07.018. PMID15313604.
^Rivas E, Klein RJ, Jones TA, Eddy SR (2001). "Computational identification of noncoding RNAs in E. coli by comparative genomics". Curr. Biol.11 (17): 1369–73. doi:10.1016/S0960-9822(01)00401-8. PMID11553332.
^I.L. Hofacker, W. Fontana, P.F. Stadler, S. Bonhoeffer, M. Tacker, P. Schuster (1994). "Fast Folding and Perbandingan -- RNA Secondary Structures.". Monatshefte f. Chemie125 (2): 167–188. doi:10.1007/BF00818163.
^Byun Y, Han K (2009). "PseudoViewer3: generating planar drawings of large-scale RNA structures with pseudoknots.". Bioinformatics25 (11): 1435–7. doi:10.1093/bioinformatics/btp252. PMID19369500.
^Martinez HM, Maizel JV, Shapiro BA (2008). "RNA2D3D: a program for generating, viewing, and comparing 3-dimensional models of RNA.". J Biomol Struct Dyn25 (6): 669–83. PMID18399701.
Tags: List of RNA structure prediction software, Informatika Komputer, 2243, Daftar/Tabel RNA structure prediction software This list of RNA structure prediction software is a compilation of software tools and web portals used for RNA structure prediction, Contents Single sequence secondary structure prediction 2 Single sequence tertiary structure prediction 3 Comparative methods 4 Inter molecular interactions: RNA RNA 5 Inter molecular interactions: MicroRNA:UTR 6 ncRN, List of RNA structure prediction software, Bahasa Indonesia, Contoh Instruksi, Tutorial, Referensi, Buku, Petunjuk m.kelas karyawan ftumj, prestasi.web.id