lncRNAtor

lncRNAtor provides integrated annotations and analyses to investigate functional properties of long non-coding RNAs (lncRNAs) across six model organisms (human, mouse, zebrafish, fruit fly, worm, and yeast).

Key Features:

• Extensive Data Compilation: Compiles lncRNA data from human, mouse, zebrafish, fruit fly, worm, and yeast using sources including ENSEMBL, HGNC, MGI, and lncRNAdb.
• Functional Annotation and Analysis: Assesses coding potential and phylogenetic conservation and generates expression profiles using gene expression data from 208 RNA-Seq studies (4995 samples) sourced from GEO, ENCODE, modENCODE, and TCGA.
• Coexpression Analysis: Identifies coexpressed mRNAs from RNA-Seq data and produces gene lists for downstream enrichment analyses such as Gene Ontology and KEGG pathways.
• Protein-LncRNA Interactions: Compiles protein–lncRNA interactions by analyzing CLIP-seq and PAR-CLIP sequencing data to identify lncRNA binding partners.
• Evolutionary Conservation Analysis: Examines correlated expression patterns between human and six other organisms to identify evolutionarily conserved lncRNAs.
• Sequence Curation: Curates lncRNA sequences from integrated databases for use in annotation, conservation, and interaction analyses.

Scientific Applications:

• Functional Insights: Enables inference of lncRNA roles through integrated annotations, sequence analyses, expression profiling, coexpression, and interaction data.
• Cross-Species Comparisons: Supports comparative analyses across multiple model organisms to identify conserved functions and evolutionary trajectories of lncRNAs.
• Disease Research: Facilitates investigation of lncRNA involvement in disease by providing expression profiles across diverse disease contexts and datasets.

Methodology:

Integrates sequence curation, gene expression profiling from 208 RNA-Seq studies (4995 samples), coding potential assessment, phylogenetic conservation analysis, coexpression analysis, protein interaction mapping from CLIP-seq and PAR-CLIP, and enrichment analyses (Gene Ontology, KEGG).