RNABindR

[…] regions was further normalized to that at region h4., the presence of rna-interacting residues in full length numa sequence (residues 1–2101) was analyzed by the rna web-based servers bindn and rnabindr (,). both servers returned as output the input sequence with each amino acid receiving either a positive or negative symbol, denoting the particular residue as either a rna-interacting […]

[…] including data derived from crosslinking immunoprecipitation (clip)-seq datasets (e.g., aura [], clipz []) and some that have been developed as a predictive tool such as pridictor [], bindn+ [], rnabindr []., clipdb [] is a clip-seq database for rna–protein interactions. the version has been developed to clipdb version 2: postar [], which is a resource of post-transcriptional regulation […]

[…] rnaprob employs a smoothed pssm encoding scheme to predict rna‐binding sites on proteins and shows an accuracy of 0.89. slightly different types of physicochemical properties are combined in rnabindr, that exploits accessible surface area, sequence specific entropy, hydrophobicity, secondary structure propensities, and electrostatics. in the testing phase, rnabindr identifies interface […]

[…] min/max (minimum/maximal gray value), and intden (integrated density, i.e., the product of area and mean gray value)., potential rna-binding sites were searched in the hex 110 sequence using rnabindr, bindn, pprint, svm and rnabindrplus softwares [–]. we selected the most significant motifs, and only the alignments passing a threshold of 80% were considered as significant. the cnls […]

[…] the possibility of biased prediction by the programs. finally, different criteria are measured to show different aspects of the programs. the webservers assessed include bindn[], bindn+[], rnabindr[], rnabindrplus[], dbs-pred[], dbs-pssm[], kyg[], prbr[], pprint[], dnabindprot[], protedna[], displar[], dr_bind1[], aarna[], rbscore[], rbrdetector[], dnabind[], xypan[] and rnaprosite […]

[…] [] introduced a predictor for rna-binding sites using predicted secondary structure and amino acid type, and employingan artificial neural network. subsequently, terribilini et al. [] contributed rnabindr, which is a classical method to train naive bayes (nb) classifiers to predict rna-binding sites. the rb109 dataset is listed in . wang and brown developed the bindn tool, […]