Long non-coding RNA disease association detection software tools | Non-coding RNA data analysis
Increasing evidences have demonstrated that long noncoding RNAs (lncRNAs) play important roles in many human diseases. Therefore, predicting novel lncRNA-disease associations would contribute to dissect the complex mechanisms of disease pathogenesis.
Consists of a long non-coding RNA global function predictor. lnc-GFP is a method in which a bi-colored biological network is constructed using coding–non-coding co-expression data and protein interaction data. The algorithm also predicts the proper functions for many long non-coding RNAs (lncRNAs) dynamically expressed in different stages of oliogodendrocyte and neuronal differentiation in their study.
A sequence-based bioinformatics method to predict the lncRNA-disease associations based on the crosstalk between lncRNAs and miRNAs. Using LncDisease, we predicted the lncRNAs associated with breast cancer and hypertension. LncDisease is a convenient tool for researchers to dissect the relations between lncRNAs and diseases in a large-scale, which could be helpful in identifying potential lncRNAs for disease diagnosis and therapy.
Investigates functions and mechanisms of long non-coding RNAs (lncRNAs) in cancer context. TANRIC is an application organized through three main panels for (i) exploring lncRNAs of interest according to their interactions with other TCGA's data types within and across tumor types; (ii) corroborating a pattern of interest or determining model cell lines for experimental identification; (iii) interrogating expression profiles of user-defined lncRNAs.
Models associated risk-dependence between long intergenic noncoding RNAs (lincRNAs) and genes. CisPi is a quantitative scoring system that was developed to identify enhancer-templated RNAs (eRNAs) or active enhancers that regulate the expression of local genes. It also allows broader applications to contexts in which enhancer hallmarks are not available or show limited sensitivity.
Performs prediction of disease-associated long non-coding RNAs (lncRNAs) using genome-wide tissue expression profiles and disease-associated protein-coding genes (PCGs). DislncRF is based on training of multiple balanced random forest models on generic tissue expression profiles from disease-associated PCGs. The software is also able to automatically identify disease-tissue associations. It was tested using case studies of lncRNAs associated to prostate cancer and inflammatory bowel disease.
Determines lncRNA-disease associations. SIMCLDA is based on an inductive matrix completion (IMC) method. It employs informative feature vectors corresponding to the top singular vectors of the lncRNA and disease feature matrices to work. This tool enables deduction of potential lncRNAs for renal cancer, gastric cancer, and prostate cancer. It assists in minimizing reduce false positive in lncRNA-protein interaction.
Allows identification of lncRNA-disease associations. MFLDA is a matrix factorization based data fusion model that integrates various heterogenous data sources to predict associations between different types of entities, such as RNA-protein interactions, associations between genes and Gene Ontology terms. The software can selectively and differentially fuse heterogenous data sources by assigning large weights to relevant data sources and small (or zero) weights to less relevant (or noisy) data sources.
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.
Offers an approach for various prioritization applications. ProphTools is an open-source modular software available as both a standalone application and via Docker container. The program combines a within-network propagation method and a weighted across-network propagation that allows users to perform either prioritization and performance tests. Besides, it includes functionalities to authorize advanced users to extend its features.
Allows users to foresee novel lncRNA-disease associations. IRLWRLDA integrates various lncRNA similarity measures, disease semantic similarity, and known lncRNA-disease associations. It incorporates lncRNA expression similarity and disease semantic similarity to set the initial probability vector of Random walk with restart (RWR). This tool can be applied to any diseases without known related lncRNAs.
Infers long non-coding RNA (lncRNA) functional similarity (LFS) by modeling the information flow in an integrated network that comprises both lncRNA-related transcriptional and post-transcriptional information. IntNetLncSim integrated network involves lncRNA regulatory network, miRNA-mRNA interaction network, and mRNA-mRNA interaction network. The performance of IntNetLncSim was evaluated by investigating the relationship of LFS with the similarity of lncRNA-related mRNA sets (LmRSets) and miRNA sets (LmiRSets). Furthermore, highly-ranked lncRNA-disease associations confirmed by literature mining demonstrated the performance of IntNetLncSim. A web-accessible system was provided for querying LFS and potential lncRNA-disease relationships.
Detects disease- long non-coding RNAs (lncRNA) associations. DisLncPri is a disease associated lncRNA prioritization method that integrates both competing endogenous RNA (ceRNA) theory and functional genomics data. This algorithm can assist in improving the understanding of lncRNAs regulation at the transcriptional level and results in novel biomarker discovery and therapeutic development of disease.
Identifies disease-related long non-coding RNAs (lncRNAs). LncNetP is a systematical lncRNA prioritization approach. This method (i) detects significant lncRNA-lncRNA interactions according to microRNAs (miRNAs) with competing endogenous RNA (ceRNA) relations, (ii) constructs cancer-specific lncRNA networks associated with different disease phenotypes, and (iii) prioritizes candidate disease lncRNA by integrating disease phenotype associations.
Predicts lncRNA-disease associations by birandom walks on a directed bi-relational network. BRWLDA is a computational model built up with two subnetworks (i) lncRNA-lncRNA functional similarity network derived from various biological data and; (ii) disease-disease similarity network derived from the ontological structure between diseases. This method can boost the identification of lncRNA-disease associations.
Predicts potential long non-coding RNA (lncRNA)-disease associations by integrating known microRNA (miRNA)-disease associations and lncRNA-miRNA interactions. HGLDA obtained a reliable area under ROC curve (AUC) of 0.7621 in the leave-one-out cross validation (LOOCV), based on known experimentally verified lncRNA-disease associations from the LncRNADisease database. It was also applied to predict: breast cancer, lung cancer, and colorectal cancer-related lncRNAs.
Predicts potential lncRNA-disease associations. KATZLDA was developed for predicting potential disease-related Long non-coding RNA (lncRNAs) by measuring the importance of candidate nodes relative to given seed nodes, and identifying nodes similar to seed nodes. The software integrates known lncRNA-disease associations, lncRNA expression profiles, lncRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for diseases and lncRNAs. It has been used to predict potential colon cancer-related lncRNAs.
Aims to address the unique opportunity of comprehensively discovering cancer driver lncRNAs within and across tumour types using mutation data generated by projects such as TCGA (The Cancer Genome Atlas) and ICGC (International Cancer Genome Consortium). ExInAtor is specifically designed to identify cancer driver lncRNAs from tumour genome cohorts. The ExInAtor workflow can be divided into the following steps: exon and background definition, mutations mapping, sub-sampling of background region, gene filtering by mutation counts and statistical analysis.
Predicts potential long non-coding RNAs (lncRNA)-disease associations on a large scale. GrwLDA is a global network random walk for potential human lncRNA-disease association prediction. This method integrates disease semantic similarities, lncRNA functional similarities, and known lncRNA-disease associations to discover the potential associations. It does not require negative samples. This algorithm can be applied to predict isolated disease (i.e., disease without any known related lncRNA), related lncRNAs, and novel lncRNA- associated diseases (i.e., lncRNA without any known associated disease).
Assists users in selection of disease candidate Long non-coding RNAs (lncRNAs). LncPriCNet is an R package, based on a computational method, that gathers data about genes, lncRNAs, phenotypes and their associations. Then, the application prioritizes the possible candidate lncRNAs by taking into account the global functional interactions of the multi-level composite network. The software was tested on a breast cancer RNA-Seq data set.
Identifies potential long non-coding RNAs (lncRNA)-disease associations. TPGLDA is a computational method that integrates experimentally verified gene-disease associations and lncRNA-disease associations. The lncRNA-disease-gene tripartite graph allows delineation of the heterogeneity of coding-non-coding genes-disease associations. The software can be applied to the isolated nodes by integrating lncRNA similarities and disease similarities.