Automates the task of coding information in the secondary structure of folded RNA sequences. NOBAI is a web server which consists of four separate modules, MARTEN, STOAT, STRINGGEN and OMROKGEN. The software codes topological and thermodynamic information related to the secondary structure of RNA molecules as multi-state phylogenetic characters, builds character matrices directly and provides sequence randomization options.
Implements an algorithm to cluster a set of structured RNAs taking their respective structural conservation into account. For a set of multiple structural alignments of RNA sequences, each containing a paralog sequence included in a structural alignment of its orthologs, RNAscClust computes minimum free-energy structures for each sequence using conserved base pairs as prior information for the folding. The paralogs are then clustered using a graph kernel-based strategy, which identifies common structural features. The clustering accuracy clearly benefits from an increasing degree of compensatory base pair changes in the alignments.
Serves for designing and analyzing structured pools for in vitro selection. RAGPOOLS is an online application assisting in: (1) design of structured RNA pools with target motif distribution; (2) analysis of structural distributions of RNA pools; and (3) research of novel RNAs via combined experimental and theoretical pool design. It is composed of two different tools, RNA Pool Designer and RNA Pool Analyzer.
Offers tools for the visualization of RNA family models, also known as covariance models (CMs) and Hidden Markov Models (HMMs). CMV offers four different visualization tools: (1) HMMV, for visualizing HMMs; (2) HMMCV, for seeing HMM comparisons; (3) CMV, for displaying RNA family models; and (4) CMCV, for studying RNA family comparisons. This tool is available both as a web application and as a standalone software.
Predicts long non-coding RNA (lncRNA) subcellular localization. lncLocator is an ensemble predictor that combines four learning machines using a stacked ensemble strategy. The learning machines are random forest with features extracted by deep neural networks (RFA), support vector machine with features extracted by deep neural networks (SVMA), random forest with raw kmer features (RFR) and support vector machine with raw k-mer features (SVMR), respectively. The software consists of three major steps: (1) feature representation, (2) prediction engine construction, and (3) stacked ensemble.
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