Protein-binding region detection software tools | RNA modification data analysis
Motivated by the increased amount of data on protein-RNA interactions and the availability of complete genome sequences of several organisms, many computational methods have been proposed to predict binding sites in protein-RNA interactions. However, most computational methods are limited to finding RNA-binding sites in proteins instead of protein-binding sites in RNAs. Predicting protein-binding sites in RNA is more challenging than predicting RNA-binding sites in proteins. Recent computational methods for finding protein-binding sites in RNAs have several drawbacks for practical use.
Allows the study of RNA-protein interactions. RNAcompete provides an estimate of relative preference for a large number of individual sequences using a single binding reaction. It also permits to design arrays that focus on variants of a specific type of sequence and structure. Finally, this method can be used to reliably identify preferred binding sequences for RNA-binding proteins (RBPs), whether these are in structured or unstructured RNA contexts.
A server for large-scale calculations of protein-RNA interactions. catRAPID omics allows (i) predictions at proteomic and transcriptomic level; (ii) use of protein and RNA sequences without size restriction; (iii) analysis of nucleic acid binding regions in proteins; and (iv) detection of RNA motifs involved in protein recognition.
A web server for accurate prediction and mapping of RBP binding sites. RBPmap has been developed specifically for mapping RBPs in human, mouse and Drosophila melanogaster genomes, though it supports other organisms too. RBPmap enables the users to select motifs from a large database of experimentally defined motifs. In addition, users can provide any motif of interest, given as either a consensus or a PSSM.
Establishes a central, redistributable workbench for scientists and programmers working with RNA-related data. The RNA workbench builds a sustainable community around it. This platform is unique in combining available tools, workflows and training material, as well as providing easy access for experimentalists. It serves as a central hub for programmers, which can easily integrate and deploy their existing or novel tools and workflows.
A tool for genome-wide recommendation of RNA-protein interactions. RNAcommender is a recommender system capable of suggesting RNA targets to unexplored RNA binding proteins, by propagating the available interaction information, taking into account the protein domain composition and the RNA predicted secondary structure. RNAcommender can be a valid tool to assist researchers in identifying potential interacting candidates for the majority of RBPs with uncharacterised binding preferences.
Compares enriched functional categories such as pathways and GO terms. SimiRa allows to find similar regulators for given input sets of microRNAs and RBPs. It was developed to act as a hypothesis-generator for wet lab scientists that run into common limitations of microRNA research: miRNAs have environment-specific functions and act in concert. SimiRa performs an enrichment analysis to find significant functional categories and subsequently compares miRNAs and RBPs.
A tool for the study of the combinatorial nature of post-transcriptional trans-factors. PTRcombiner allows the identification of clusters of trans-factors (RBPs and miRNAs) from interaction maps and it performs the biological characterization of identified clusters giving information about the overlap among the target genes, the GO enrichments and similarity among GO terms.
Predicts mutual binding sites in RNA and protein at the nucleotide level resolutions. PRIdictor is based on the library for support vector machine (SVM) with the radial basis function (RBF) as a kernel. The tool is able to predict (i) RNAbinding residues in a protein sequence with or without RNA sequence data, and (ii) protein-binding nucleotides in an RNA sequence with or without protein sequence data.
A deep learning based framework to fuse heterogeneous data for predicting RNA-protein interaction sites. iDeep can not only learn the hidden feature patterns from individual source of data, but also extracted the shared representation across them. In addition, the convolutional neural network in iDeep can automatically identify binding motifs. To validate this method over other methods, experiments were performed on large-scale CLIP-seq datasets. The comprehensive results indicated the huge advantage of iDeep, which performs much better than the state-of-the-art methods.
Offers a method to estimate allele-specific protein-RNA interaction. BEAPR consists of an algorithm that serves for the allele-specific binding (ASB) detection and prediction of functional genetic variants (GVs) in post-transcriptional gene regulation. Moreover, it employs an empirical Gaussian distribution to model the normalized read counts. The expected variance is estimated using a regression mode.
Predicts protein-binding regions in mRNA. RBPbinding is a support vector machine (SVM) that uses sequence profiles constructed from log-odds scores of mono and di-nucleotides and nucleotide compositions. The software showed a high performance in testing on many human RNA sequences. It was evaluated in several ways, including standard 10-fold cross validation on six datasets with different ratios of positive to negative instances, LOPO cross validation, and independent testing with six datasets of different ratios of positive to negative instances.
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