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Detects structured noncoding RNAs in comparative genomics data. RNAz is a program for predicting structurally conserved and thermodynamically 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 (ncRNAs) and cis-acting regulatory elements of messenger RNAs (mRNAs). The algorithm allows to calculate thermodynamic stability scores based on a dinucleotide background model.

RNAex / RNA secondary structure prediction enhanced by high-throughput experimental data

Enables non-specialists to easily predict RNA secondary structures using cutting-edge high-throughput structure-probing data. Users can easily access and incorporate these data into the RNAex server to enhance the RNA secondary structure prediction using four different folding methods (MaxExpect, SeqFold, RNAstructure (Fold) and RNAfold). RNAex results allow users to clearly view, identify and understand SNP and post-transcriptional regulation sites on the RNA structures. Overall, the server is an early web-based platform for the emerging new field of high-throughput RNA structure-probing analysis.


Provides convenient access to in vivo RNA structure probing data across thousands of transcripts. FoldAtlas allows a researcher to easily locate, view, and retrieve probing data for a given RNA molecule. FoldAtlas also provides in silico and in vivo secondary structure predictions for comparison, visualised in the browser as circle plots and topology diagrams. Datasets included are from a new high-depth Structure-seq DMS analysis in Arabidopsis thaliana, covering over 11,000 transcripts.

MIMEAnTo / Mutational Interference Mapping Experiment Analysis Tool

Allows the identification of domains and structures in RNA sequences. MIMEAnTo evaluates MIME data, following error-correction, statistical ascertainment and quality assessment. MIMEAnTo has a wizard-like graphical user interface, guiding through the data analysis procedure in three steps: (i) data input and assessment, (ii) sequencing and reverse transcription error correction and (iii) quantification of raw effects and quality filtering.

SEQualyzer / Structure-profiling Experiment Quality analyzer

A visual and interactive application that makes it easy and efficient to gauge data quality, screen for transcripts with high-quality information and identify discordant replicates in structure profiling experiments. SEQualyzer rely on features common to a wide range of protocols and can serve as standards for quality control and analyses. Its outputs permit easy quality evaluation and identification of discordant replicates.

RSF / RNA Structure Framework

Deals with RNA structure probing and post-transcriptional modifications mapping high-throughput data. RNA Framework is a modular toolkit. Its main features are (i) automatic reference transcriptome creation, (ii) automatic reads preprocessing (adapter clipping and trimming) and mapping, (iii) scoring and data normalization and (iv) accurate RNA folding prediction by incorporating structural probing data. It can perform not only RNA Structure analysis, but also analysis of RNA post-transcriptional modifications mapping experiments (such as m1A-seq, m6A-seq, 2OMe-seq, and Pseudo-seq).


A tool for RNA secondary structure prediction with multiple types of experimental probing data. RME can use experimental pairing probabilities to restrain the partition function, and predict the structure with maximum restrained expected accuracy based on a MEA algorithm, MaxExpect. It also provides preprocessing scripts for transforming the SHAPE, PARS and DMS-seq data into pairing probability according a posterior probabilistic model. Moreover, it also contains a utility for optimizing the parameters of RME by RME-Optimize.


A probabilistic method for RNA secondary structure prediction that integrates experimental structure probing data. It can be automatically trained given a set of known structures with probing data. The approach is demonstrated on SHAPE data sets, where we evaluate and selectively model specific correlations. The approach often makes superior use of the probing data signal compared to other methods. We illustrate the use of ProbFold on multiple data types using both simulations and a small set of structures with both SHAPE, DMS, and CMCT data. Technically, the approach combines stochastic context-free grammars (SCFGs) with probabilistic graphical models. This approach allows rapid adaptation and integration of new probing data types.


Provides an integrated computational solution designed specifically for large-scale RNA structure mapping and reconstruction across any transcriptome. StructureFold automates the processing and analysis of raw high-throughput RNA structure profiling data, allowing the seamless incorporation of wet-bench structural information from chemical probes and/or ribonucleases to restrain RNA secondary structure prediction via the RNAstructure and ViennaRNA package algorithms. StructureFold performs reads mapping and alignment, normalization and reactivity derivation, and RNA structure prediction in a single user-friendly web interface or via local installation. StructureFold is freely available as a component of Galaxy.


Processes reads and calculates SHAPE reactivities for SHAPE-Seq experiments on multiple RNAs. The Spats package implements a read mapping and reactivity analysis pipeline for calculating SHAPE-Seq reactivities from an input set of next-generation reads. It accepts raw paired-end sequencing reads in fastq format, and a target sequence file containing the sequences of RNAs present in the experimental pool. Spats then performs read alignment to calculate distributions of read ends in the SHAPE (+) and (-) channel for each nucleotide in each RNA. Spats then estimates nucleotide resolution SHAPE reactivities for each RNA, using a model-driven maximum likelihood procedure based on a model of the reverse transcriptase process used in the SHAPE-Seq experiment.