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Structural RNAs:
A. Ribosomal RNA analysis
Silva – rRNA database project (Max Planck Institute for Marine Microbiology, Bremen, Germany ) – provides comprehensive, quality checked and regularly updated datasets of aligned small (16S/18S, SSU) and large subunit (23S/28S, LSU) ribosomal RNA (rRNA) sequences for all three domains of life (Bacteria, Archaea and Eukarya).
RNAmmer 1.2 – predicts 5s/8s, 16s/18s, and 23s/28s ribosomal RNA in full genome sequences (Reference: Lagesen, K et al. 2007. Nucl. Acids Res. 35: 3100-3108)
rRNA prediction (hmm_rRNA) – predicts rRNA by using HMMER 3.0 to identify DNA reads containing rRNA sequences. (Reference: S. Wu et al. 2011. BMC Genomics 12:444).
B. Transfer RNAs (tRNA) – for additional information see the genomic tRNA database GtRNAdb or Transfer RNA database tRNAdb
tRNAs: tRNAscan-SE– (Univerisity of California, Santa Cruz, U.S.A,) is incredibly sensitive & also provides secondary structure diagrams of the tRNA molecules (Reference: Lowe, TM, & Eddy, SR. Nucleic Acids Res. 1997. 25: 955-964). tRNAscan-SE 2.0 can also be accessed here. Alternatively use ARAGORN (Reference: Laslett, D. & Canback. 2004. Nucleic Acids Research 32:11-16).
ARWEN – is a program to detect tRNAs in metazoan mitochondrial DNA sequences (Reference: D. Laslett & B. Canbäck B. 2008. Bioinformatics 24:172-175)
Rfam – The Rfam database is a collection of RNA families, each represented by multiple sequence alignments, consensus secondary structures and covariance models (Reference: Gardner, P.P. et al. 2008. Nucl. Acids Res. 37, Database issue D136-D140)
tmRNAs: ARAGORN, tRNA (and tmRNA) detection – detects transfer-messenger RNAs which function to free ribosome-stalled mRNAs. (Reference: Laslett, D., et al. 2002. Nucleic Acids Res. 30: 3449-3453, 2002) .
C. Micro RNAs (miRNAs) are small, non-coding RNA (~20-22 nucleotides) that negatively regulate gene expression at post-transcriptional level. You might want to start with RNAIWeb or miRGator.
mirTools – users can: (i) filter low-quality reads and 3/5′ adapters from raw sequenced data; (ii) align large-scale short reads to the reference genome and explore their length distribution; (iii) classify small RNA candidates into known categories, such as known miRNAs, non-coding RNA, genomic repeats and coding sequences; (iv) provide detailed annotation information for known miRNAs, such as miRNA/miRNA*, absolute/relative reads count and the most abundant tag; (v) predict novel miRNAs that have not been characterized before; and (vi) identify differentially expressed miRNAs between samples based on two different counting strategies.(Reference: Zhu, E.L. et al. 2010. Nucl. Acids Res. 38 (suppl 2): W392-W397).
MiRPara – is a SVM-based software tool for prediction of most probable microRNA coding regions in genome scale sequences (Reference: Wu Y.et al. 2011. BMC Bioinformatics. 12(1):107).
miRDB provides a web interface for more flexible miRNA target search. You may search targets by providing your own sequence. In addition, target search can also be performed for unconventional sites in the coding region or 5′-UTR. (Reference: X. Wang & I. M. El Naqa (2008) Bioinformatics 24(3):325-332).
miR-BAG predict miRNAs from the genomic sequences as well as from Next Generation Sequencing data. It applies a bootstrap aggregating approach to create an ensemble of three different approaches (naïve Bayes, Best First Decision tree and SVM) to achieve a high accuracy. At present miR-BAG includes 6 different species, 4 for animals (Homo sapiens, Canis familiaris, Mus musculus, Rattus norvegicus) alongwith one nematode (Caenorhabditis elegans) and one insect species (Drosophila melanogaster). miR-BAG was found to perform consistently with accuracy level higher than 90% for several species.(Reference: Jha, A. et al. 2012. PLoS ONE 7(9): e45782.)
Small nucleolar RNAs (snoRNAs) – can be detected with Snoscan for methylation-guide for snoRNAs and snoGPS for pseudouridylation-guide snoRNAs (Reference: P. Schattner et al. 2005. Nucl. Acids Res. 33: W686-W689). Test sequences.
sRNAtoolbox – is an integrated collection of small RNA research tools. Includes: sRNAbench: Expression profiling of small RNAs and prediction of novel microRNAs from deep sequencing data; sRNAde: Differential expression analysis; sRNAblast: Blast analysis of deep sequencing reads against a local nt/nr (NCBI link) database.(Reference: A. Rueda et al. 2015. Nucl. Acids Res. 43 (W1): W467-W473).
RNA folding:
LocARNA – Multiple Alignment of RNAs – is a tool for multiple alignment of RNA molecules. LocARNA requires only RNA sequences as input and will simultaneously fold and align the input sequences. LocARNA outputs a multiple alignment together with a consensus structure. For the folding it makes use of a very realistic energy model for RNAs as it is by RNAfold of the Vienna RNA package (or Zuker’s mfold). For the alignment it features RIBOSUM-like similarity scoring and realistic gap cost. (Reference: C. Smith et al. 2010. Nucl. Acids Res. 38: W373-377).
CARNA is a tool for multiple alignment of RNA molecules. CARNA requires only the RNA sequences as input and will compute base pair probability matrices and align the sequences based on their full ensembles of structures. Alternatively, you can also provide base pair probability matrices (dot plots in .ps format) or fixed structures (as annotation in the FASTA alignment) for your sequences. If you provide fixed structures, only those structures and not the entire ensemble of possible structures is aligned. In contrast to LocARNA, CARNA does not pick the most likely consensus structure, but computes the alignment that fits best to all likely structures simultaneously. Hence, CARNA is particularly useful when aligning RNAs like riboswitches, which have more than one stable structure. (Reference: A. Dragos et al. 2012. Nucleic Acids Reseach 40: W49-W53).
For RNA folding use MFold – N.B. The data can be presented in a number of graphic formats. This is my “go to” site if I’m interested in a secondardy structure for a fragment of RNA or DNA (Reference: M. Zuker. 2003. Nucleic Acids Res. 31: 3406-3415).Vienna RNA secondary structure prediction (University of Vienna, Austria). I have found this site useful for drawing tRNAs in cloverleaf format.
CONTRAfold is a novel secondary structure prediction method based on conditional log-linear models, a flexible class of probabilistic models which generalize upon stochastic context-free grammars by using discriminative training and feature-rich scoring. By incorporating most of the features found in typical thermodynamic models, CONTRAfold achieves the highest single sequence prediction accuracies to date, outperforming currently available probabilistic and physics-based techniques. It provides MARNA-like output couples with hairpin structures (Reference: Do, C.B. et al. 2006. Bioinformatics 22: e90-e98).
RNAiFold 2.0 – is a web server and software to design custom and Rfam-based RNA molecules – providing a user-friendly pipeline to design synthetic constructs having the functionality of given Rfam families. In addition, the new software supports amino acid constraints, even for proteins translated in different reading frames from overlapping coding sequences; moreover, structure compatibility/incompatibility constraints have been expanded. With these features, RNAiFold 2.0 allows the user to design single RNA molecules as well as hybridization complexes of two RNA molecules. (Reference: J.A. Garcia-Martin et al. 2015. Nucl. Acids Res. 43 (W1): W513-W521).
Web-Beagle: a web server for the pairwise global or local alignment of RNA secondary structures. (Reference: E. Mattei et al. 2015. Nucl. Acids Res. 43 (W1): W493-W497).
Rclick – this web server that is capable of superimposing RNA 3D structures by using clique matching and 3D least-squares fitting. Rclick has been benchmarked and compared with other popular servers and methods for RNA structural alignments. In most cases, Rclick alignments were better in terms of structure overlap. It also recognizes conformational changes between structures. (References: Nguyen MN, & Verma C. 2015. Bioinformatics 31:966-968).
Pseudoknots:
pKiss – is the successor of pknotsRG, the first pseudoknot class is the canonical simple recursive pseudoknot from pknotsRG. The new class are canonical simple recursive kissing hairpins. (Reference: Janssen, S. & Giegerich, R. Bioinformatics, 2015; 31(3):423-5).
vsfold5 – RNA Pseudoknot Prediction Server
Viral IRES Prediction System (VIPS) (Reference: Hong, JJ et al. PLoS One. 2013; 8(11): e79288).
KineFold Web Server – RNA/DNA folding predictions including pseudoknots and entangled helices (Reference: A. Xayaphoummine et al. Nucleic Acid Res. 33: 605-610 (2005).
IPknot: IP-based prediction of RNA pseudoKNOTs – rovides services for predicting RNA secondary structures including a wide class of pseudoknots. IPknot can also predict the consensus secondary structure when a multiple alignment of RNA sequences is given. (Reference: K. Sato et al. Bioinformatics, 27: i85-i93, 2011.
HPknotter: A Heuristic Approach for Detecting RNA H-type Pseudoknots – offers a variety of tools including pknotsRG, PNOTS and NUPACK (Reference: C.-H. Huang et al. 2005. Bioinformatics 21: 3501-3508).
HotKnots – Predict RNA secondary structures with pseudoknots prediction (Reference: Ren, J. et al. 2005. RNA 11: 1494-1504).
RNAstructure – Predict a Secondary Structure Web Server – combines many separate prediction and analysis algorithms: calculating a partition function, predicting a maximum free energy (MFE) structure, finding structures with maximum expected accuracy, and pseudoknot prediction. This server takes a sequence, either RNA or DNA, and creates a highly probable, probability annotated group of secondary structures, starting with the lowest free energy structure and including others with varied probabilities of correctness.
K2N: a service to get from knotted to nested RNA structures. This site provides access to a variety of methods for pseudoknot removal. (Reference: S. Smit et al. RNA (2008) 14(3):410-416).
Promoters, terminators and other regulatory elements:
Virtual Footprint – offers two types of analyses (a) Regulon Analysis – analysis of a whole prokaryotic genome with one regulator pattern and (b) Promoter analysis – Analysis of a promoter region with several regulator patterns (Reference: R. Münch et al. 2005. Bioinformatics 2005 21: 4187-4189).
WebGeSTer – Genome Scanner for Terminators – my favourite terminator search program is finally web enabled. Please note that if you want to analyze data from a *.gbk file you need to use their conversion program “GenBank2GeSTer” first. A complete description of each terminator including a diagram is produced by this program. This site linked to an extensive database of transcriptional terminators in bacterial genome (WebGeSTer DB) (Reference: Mitra A. et al. 2011. Nucl. Acids Res. 39(Database issue):D129-35).
ARNold – finds rho-independent terminators in nucleic acid sequences using two complementary programs, Erpin and RNAmotif. The program colors the terminator stem and loop (References: Gautheret D, Lambert A. 2001. J Mol Biol. 313:1003–11 & Macke T. et al. 2001. Nucleic Acids Res. 29:4724–4735 ).
FindTerm (Softberry Inc.) – is one of only two tools on the internet for mapping rho-independent terminators. You might consider using the advanced feature options and minimally increase the default energy threshold to -12.0.
RibEx: Riboswitch Explorer – scans <40kb DNA for potential genes (which are linked to BLASTP) and several hundred regulatory elements, including riboswitches. If you click on the “search for attenuators” it finds terminators and antiterminators. (Reference: C. Abreu-Goodger & E. Merino. 2005. Nucl. Acids Res. 33: W690-W692).
RegRNA 2.0 is an integrated web server for identifying functional RNA motifs in an input RNA sequence. These include Splicing sites (donor site; acceptor site); Splicing regulatory motifs(ESE; ESS; ISE; ISS elements); Polyadenylation sites; Transcriptional motifs (rho-independent terminator; TRANSFAC); Translational motifs (ribosome binding sites); UTR motifs (UTRsite patterns); mRNA degradation elements (AU-rich elements); RNA editing sites (C-to-U editing sites); Riboswitches (RiboSW); RNA cis-regulatory elements (Rfam; ERPIN); Similar functional RNA sequences (fRNAdb); RNA-RNA interaction regions (miRNA; ncRNA). (Reference: Chang TH et al. 2013. BMC bioinformatics 14 Suppl 2:S4).
RegRNA – A Regulatory RNA Motifs and Elements Finder – RegRNA is an integrated web server for identifying the homologs of regulatory RNA motifs and elements against an input mRNA sequence. Both sequence homologs and structural homologs of regulatory RNA motifs can be recognized. The regulatory RNA motifs supported in RegRNA are categorized into several classes: (i) motifs in mRNA 5′-untranslated region (5′-UTR) and 3′-UTR; (ii) motifs involved in mRNA splicing; (iii) motifs involved in transcriptional regulation; (iv) riboswitches; (v) splicing donor/acceptor sites; (vi) inverted repeats; and (vii) miRNA target sites.(Reference: Huang HY et al. 2006. Nucleic Acids Res. 34(Web Server issue):W429-34).
siRNA:
ARTS (Alignment of RNA Tertiary Structures) – aligns two nucleic acid structures (RNAs or DNAs) in pdb format and detecting apriori unknown common substructures. The identified common substructures can be either large global folds or small local tertiary motifs with at least two successive base pairs. (Reference: O. Dror et al. 2005. Bioinformatics 21 (Suppl 2):ii47-ii53)
CopraRNA is a tool for sRNA target prediction. It computes whole genome predictions by combination of distinct whole genome IntaRNA predictions (Reference: P.R. Wright et al. 2014. Nucl. Acids Res. 42 (W1), W119-W123).
OligoWalk – calculates thermodynamic features of sense-antisense hybidization. It predicts the free energy changes of oligonucleotides binding to a target RNA. It can be used to design efficient siRNA targeting a given mRNA sequence. (Reference: Lu, Z.J. & Mathews, D.H. 2008. Nucleic Acids Res.36:640-647).
RNAiFold 3.0 – Design synthetic functional RNA molecules in three simple steps. It also offeres RNA-CPdesign which uses Constraint Programming (CP) to determine one or more RNA sequences that fold into the given target structure. CP performs a complete exploration of the search space, and, thus can also prove that no sequence folds into the target structure exists. (正文完
