Generates paired gRNAs (pgRNAs). pgRNADesign is a method dedicated to the creation of pgRNAs focusing on a given promoter or on the avoidance of specific exons, promoters within coding genes or user-defined regions. Besides, this application can be employed for barcode assigning. It was integrated into a CRISPR/ Cas9 strategy with the aim of assisting researchers in determining functional long non-coding RNAs in cancer cells.
Determines, prioritizes, and annotates lncRNA functions. lncFunTK includes ChIP-seq, CLIP-seq, and RNA-seq data to proceed. It quantitatively measures the functional importance of a given lncRNA within a gene regulatory network constructed through integrating various types of sequencing data using functional information score (FIS). This tool is useful to investigate lncRNA functional mechanisms.
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.
Predicts long non-coding RNAs (lncRNA) functions from coding and non-coding co-expression networks. lncFunction is a web server that provides six options on the query page. The software allows users to choose Clade, Species and Studies, and to download the running parameters, expression values from selected study and every outcome in the three categories of function prediction.
Provides an approach dedicated to the forecasting, ranking and annotation of functional long non-coding RNAs (lncRNAs). LncFunNet supplies an algorithm that exploits throughput sequencing data and the gene network which is derived from it. It intends to furnish a computational framework highlighting and assessing the functional importance of newly discovered long non-coding RNAs (lncRNAs) in various biological systems.
A computational method and program to predict lncRNA DNA-binding motifs and binding sites. LongTarget is used to analyse multiple antisense lncRNAs, including those that control well-known imprinting clusters.
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).
Permits users to characterize long noncoding RNAs (lncRNAs). LncADeep is a lncRNA identification and functional annotation tool. It can identify lncRNAs and infer the functions of lncRNAs. This method integrates sequence intrinsic features and homology features into a deep belief network (DBN) of deep learning algorithm. It also integrates KEGG and Reactome pathway enrichment analysis.
Predicts the subcellular locations on long non-coding RNAs (lncRNAs). iLoc-IncRNA minimizes the feature dimension for avoiding the over-fitting problem, excludes the redundant information, diminish computational complexity and enhance accuracy and generalization ability of the model. It is based on a five-step rule that consists of: (1) benchmark dataset construction, (2) sample formulation, (3) operation engine, (4) cross validation, and (5) establishment of the web-server for iLoc-lncRNA.
Identifies long noncoding RNAs (lncRNAs). lncRNAnet is a deep learning-based and recurrent neural network (RNN)-based method that classifies lncRNAs from protein-coding transcripts. The software uses convolutional neural networks (CNNs) to detect the open reading frames (ORFs) as the coding transcript candidates and stacked recurrent neural networks. It was tested with two datasets containing over 7,000 transcripts.
Offers an approach dedicated to moonlighting long non-coding RNAs (mlncRNAs) investigation. MoonFinder proposes a statistical method based on a six-step process comprising: (i) protein interaction network refinement; (ii) protein module determination; (iii) functional annotation of modules; (iv) RNA-module interactions setting; (v) generation similarity matric of modules and, lastly, (vi) moonlighting detection.
Allows users to predict long non-coding RNA (lncRNAs) using hierarchical multi-label classification strategy based on multiple neural networks. NeuraNetL2GO uses multiple neural networks to annotate probable function of lncRNAs at large scale. This tool is composed of multiple neural networks to foresee probable functions for all the lncRNAs characterized in the lncRNA co-expression network.
Identifies lncRNA related miRNA sponge regulatory network. LncmiRSRN is a causality-based method that integrates expression data, clinical information and miRNA-target interactions. This application was developed to study how the expression levels of the released mRNAs activate. It complements the ceRNA hypothesis and permits users to study the functions and regulatory mechanism of lncRNAs in human cancers.
Facilitates long non-coding RNAs (LncRNAs) identification and analysis. LncFinder can achieve feature extraction and selection, classifier construction and performance evaluation. This tool is able to extract different alignment-free features such as GC content, k-mer frequencies, hexamer score, Fickett testcode, multi-scale secondary structural features and others. It is available as a desktop version and a web application.
Detects and analyzes long non-coding RNAs from RNA-seq, both known and predicted ones. UClncR is a quantifcation pipeline that works with both stranded and un-stranded RNA-seq protocols and is particularly useful for a project with many samples so that they can be analyzed together swifly. It provides four major modules: de novo transcript assembly, lncRNA candidate prediction, known and lncRNA candidate quantifcation, and reporting.
Identifies pathways synergistically regulated by the interested long noncoding RNA (lncRNA) sets based on an lncRNA-mRNA (messenger RNA) interaction network. LncRNAs2Pathways is a software which draw a heatmap for the genes of a certain pathway based on the expression profile user specified. The rows of heatmap are genes ranked by their weights and the columns of heatmap are samples ordered the same as the expression profile.
An online portal for systematically annotating newly identified human lncRNAs. AnnoLnc offers a full spectrum of annotations covering genomic location, RNA secondary structure, expression, transcriptional regulation, miRNA interaction, protein interaction, genetic association and evolution, as well as an abstraction-based text summary and various intuitive figures to help biologists quickly grasp the essentials. In addition to an intuitive and mobile-friendly Web interactive design, AnnoLnc supports batch analysis and provides JSON-based Web Service APIs for programmatic analysis.
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.
Identifies long noncoding RNA from raw RNA-seq reads in a reference genome-independent manner. The pipeline eliminates protein-coding transcripts and short non-coding RNA sequences and extracts lncRNAs from assembled transcriptomes. The application can be used for the first featuring of lncRNAs in a novel experiment, and was applied for proposing a novel annotation of the 12Xv2 V. vinifera PN40024 reference genome sequence.
Classifies protein coding and long non-coding RNA (lncRNA) transcripts using support vector machine (SVM). lncRScan-SVM is a python package for lncRNA prediction that aims at classifying PCTs and LNCTs. The gold-standard datasets for lncRScan-SVM model training, lncRNA prediction and method comparison were constructed according to the GENCODE gene annotations of human and mouse respectively. LncRScan-SVM is an efficient tool for predicting the lncRNAs, and it is quite useful for current lncRNA study.