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Structural motif discovery software tools | RNA data analysis

Motif discovery is the problem of finding recurring patterns in biological data. Patterns can be sequential, mainly when discovered in DNA sequences. They can also be structural (e.g. when discovering RNA motifs). Finding common structural patterns helps to gain a better understanding of the mechanism of action (e.g. post-transcriptional regulation). Unlike DNA motifs, which are sequentially conserved, RNA motifs exhibit conservation in structure, which may be common even if the sequences are different.

Source text:
(Badr et al., 2013) Classification and assessment tools for structural motif discovery algorithms. BMC Bioinformatics.

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TEISER / Tool for Eliciting Informative Structural Elements in RNA
Allows users to identify the structural motifs that are informative of whole-genome measurements across all the transcripts. TEISER employs mutual information to measure the regulatory consequences of the presence or absence of each of roughly 100 million different seed context-free grammars (CFGs). This tool is able to capture the dependency between the stability of each mRNA and the presence or absence of a given structural motif. It uses these measurements to choose and further refine the most informative motifs.
FOLDALIGN
An algorithm for local or global simultaneous folding and aligning two or more RNA sequences and is based on the Sankoffs algorithm. Foldalign can make pairwise local or global alignments and structure predictions. Foldalign makes a multiple global alignment and structure prediction. We substantially improve execution time while maintaining all previous functionalities, including carrying out local structural alignments of sequences with low similarity. Furthermore, the improvements allow for comparing longer RNAs and increasing the sequence length. For example, lengths in the range 2,000-6,000 nucleotides improve execution up to a factor of five.
ALIDOT / ALIgned DOT-plots
Offers a way for identifying conserved secondary structure elements. ALIDOT is a standalone software that couples a secondary structure prediction based on thermodynamic criteria and sequence comparison. The application displays information on all predicted base pairs and generates a dot plot of the predicted conserved base pairs. Besides, additional scripts allow to obtain alternative representations, such as the aligned mountain plots. The software is a part of the Vienna package.
RNAProfile
Uses a heuristic to extract a set of candidate regions from each sequence. The second step involves grouping regions to find similar motifs. RNAProfile takes as input a set of unaligned RNA sequences expected to share a common motif, and outputs the regions that are most conserved throughout the sequences, according to a similarity measure that takes into account both the sequence of the regions and the secondary structure they can form according to base-pairing and thermodynamic rules.
CMfinder
A tool to predict RNA motifs. It is an expectation maximization algorithm using covariance models for motif description, carefully crafted heuristics for effective motif search, and a novel Bayesian framework for structure prediction combining folding energy and sequence covariation. CMfinder performs well on unaligned sequences with long extraneous flanking regions, and in cases when the motif is only present in a subset of sequences. CMfinder also integrates directly with genome-scale homology search, and can be used for automatic refinement and expansion of RNA families.
GraphClust
Compars and clusters RNAs according to sequence and structure. GraphClust scales to datasets of hundreds of thousands of sequences. The quality of the retrieved clusters has been benchmarked against known ncRNA datasets and is comparable to state-of-the-art sequence–structure methods although achieving speedups of several orders of magnitude. The tool predicted local structural elements specific to lincRNAs likely functionally associating involved transcripts to vital processes of the human nervous system. In total, we predicted 349 local structural RNA elements.
comRNA
A method for predicting common RNA secondary structure motifs in a set of functionally or evolutionarily related RNA sequences. comRNA is based on comparison of stems (palindromic helices) between sequences and is implemented by applying graph-theoretical approaches. It first finds all possible stable stems in each sequence and compares stems pairwise between sequences by some defined features to find stems conserved across any two sequences. Then by applying a maximum clique finding algorithm, it finds all significant stems conserved across at least k sequences. Finally, it assembles in topological order all possible compatible conserved stems shared by at least k sequences and reports a number of the best assembled stem sets as the best candidate common structure motifs.
Structurexplor
Furnishes a web-based tool platform to enable the exploration process for RNA secondary structures. Structurexplor generates clusters and structure hierarchies and provides information about the most representative and unusual structural shapes of RNA by the identification of representative and unusual structures of clusters. Thanks to an interactive visualization, users can explore structural features of secondary structures from both linear and circular RNA, and to take pseudoknots and G-quadruplexes into account.
TSDD / Transcript Structure and Domain Display
A publicly available, web-based program that provides publication quality images of transcript structures and domains. TSDD relies on GFF/GFF3 files to draw the transcript structures and domains. GFF/GFF3 files were used as a data source to produce the transcript structures since almost all annotated genomes use this file format. This tool is capable of providing researchers with publication quality images that can be customized and downloaded. TSDD is also highly customizable to meet the needs of users.
Mc-Annotate
Extracts and manipulates structural information, to simplify further structural analyses and searches, and to objectively represent structural knowledge. Mc-Annotate allows one to classify the nucleotide conformations and base-base interactions, and to detect marginal regions that could indicate interactions with other molecules, or new sites that are responsible for structure and/or function. Mc-Annotate also made possible the creation of databases of nucleotide conformations and base-base interactions, extracted from all available DNA and RNA 3-D structures, which were indexed using the symbolic information.
NoFold
Generates and automatically extracts cohesive clusters, which can be used to find structure motifs in RNA sequences. NoFold compares the studied files against the 1973 Rfam covariance models (CMs), normalized and mapped to the RNA Empirical Structure Space (RESS), and clustered by average-linkage hierarchical clustering using Spearman distance as the distance measure. It can be performed with unrelated sequences within the data set or extraneous flanking sequence on the structural sequences.
PIDA / Pattern Island Detection Algorithm
Determines patterns within sequences containing “islands” or subsequences of perfect sequence conservation separated by “water” or intervening regions of arbitrarily long unmatched sequence. PIDA compares two sequences of any alphabet and finds patterns which contain islands of matching sequence separated by arbitrary amounts of unmatched sequence, or water. It simplifies attacking both nucleic acid and amino acid motif finding problems.
Brickworx
Builds crystal structure models of nucleic acid molecules using recurrent motifs including double-stranded helices. In a first step, Brickworx searches for electron-density peaks that may correspond to phosphate groups; it may also take into account phosphate-group positions provided by the user. Subsequently, comparing the three-dimensional patterns of the P atoms with a database of nucleic acid fragments, it finds the matching positions of the double-stranded helical motifs (A-RNA or B-DNA) in the unit cell. If the target structure is RNA, the helical fragments are further extended with recurrent RNA motifs from a fragment library that contains single-stranded segments. Finally, the matched motifs are merged and refined in real space to find the most likely conformations, including a fit of the sequence to the electron-density map.
SCARNA_LM / SCARNA Local Multiple
A pairwise local alignment model and local multiple alignment method for the detection of conserved ncRNAs from unaligned sequences. SCARNA_LM uses a local multiple alignment construction procedure inspired by ProDA, which is a local multiple aligner program for protein sequences with repeated and shuffled elements. Benchmark experiments demonstrate the high ability of the implemented software, SCARNA_LM, for local multiple alignment for the detection of ncRNAs.
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