Identifies, predicts and characterizes noncoding RNAs (ncRNAs). incRNA is a machine learning framework which integrates sequence, structure, and expression data. The software was used, with data from the modENCODE consortium, to separate known C. elegans ncRNAs from coding sequences and other genomic elements and find more than 7000 novel ncRNA candidates, among which more than 1000 were located in the intergenic regions of C. elegans genome.
Offers a method for biologists who are interested in regulatory elements in the noncoding regions of microbial genomes. IntergenicS is a user-friendly tool designed for the purpose of sequence extraction and to get guanine-cytosine content. This method extracts intergenic regions of bacterial genomes in silico. It also allows the user to specify intergenic regions of particular size range.
Aims to be the long non-coding RNA portal encompassing expression profile, interacting (binding) protein, integrated sequence curation, evolutionary scores, and coding potential. Data sets were collected from TCGA, GEO, ENCODE, and modENCODE (Organism: Human, Mouse, Fly, Worm, and Yeast).
Annotates human and mouse long non-coding RNA (lncRNA). ncFANs, on the basis of the re-annotated Affymetrix microarray data, provides two alternative strategies for lncRNA functional annotation: one utilizing three aspects of a coding-non-coding gene co-expression (CNC) network, the other identifying condition-related differentially expressed lncRNAs. ncFANs introduces a highly efficient way of re-using the abundant pre-existing microarray data. It includes re-annotated CDF files for human and mouse Affymetrix microarrays.
Allows identification and annotations of human long non-coding RNAs. Co-LncRNA is a web application allowing users to study gene ontology (GO) annotations and KEGG pathways. It uses genome-scale bio-molecular interactions and expression correlation to better understand the potential function of long non-coding RNAs (lncRNAs). This tool offers possibility for users to upload their own lncRNA and protein-coding gene expression profiles to investigate the lncRNA combinatorial effects.
A web app for predicting the interaction between long noncoding RNAs and proteins. By coding RNA and protein sequences into vectors, a matrix multiplication is used to give score to each RNA-protein pair. This score can be the measurement of interactions between the RNA-protein pair. Comparing to existing approaches, this method shortens the time for training matrix. It also theoretically guarantees the results to be the best solution. The method has shown good ability to discriminate interacting/non-interacting RNA-protein pairs and to predict the RNA-protein interaction within a given complex.
An alignment-free program which accurately annotates lncRNAs based on a Random Forest model trained with general features such as multi k-mer frequencies and relaxed open reading frames. Benchmarking versus five state-of-art tools shows that FEELnc achieves similar or better classification performance on GENCODE and NONCODE datasets. FEELnc also provides several specific modules that enable to fine-tune classification accuracy, to formalize the annotation of lncRNA classes and to annotate lncRNAs even in the absence of training set of noncoding RNAs.