VECTOR

VECTOR elucidates regulatory networks of non-coding RNAs in uveal melanoma (UM) by integrating TCGA-derived expression correlations, predicted or validated RNA–RNA interactions, sense–antisense overlap information, and ChiPseq-enriched transcription factor correlations to identify potential ceRNA axes.

Key Features:

• Expression Correlation Values: Compiles TCGA-derived correlation values for miRNA-mRNA, miRNA-lncRNA, and lncRNA-mRNA pairs alongside predicted or validated RNA–RNA interactions.
• Sense-Antisense Overlapping Data: Includes sequence overlap information between sense and antisense RNAs that can affect gene regulation.
• Transcription Factor Interactions: Incorporates correlation values for TF–miRNA, TF–lncRNA, and TF–mRNA pairs and integrates ChiPseq data to inform transcriptional regulatory relationships.
• Expression Data Across Tissues: Provides expression profiles of miRNAs, lncRNAs, and mRNAs in uveal melanoma and physiological tissues for comparative analyses.

Scientific Applications:

• ceRNA axis identification: Facilitates identification of potential competitive endogenous RNA axes involving miRNAs, lncRNAs, and mRNAs in UM using correlation and interaction data.
• Transcriptional regulation analysis: Enables exploration of TF-mediated regulation of miRNAs, lncRNAs, and mRNAs supported by ChiPseq-enriched correlations.
• Tissue-specific expression comparisons: Supports comparative analyses between uveal melanoma and physiological tissues to reveal tissue-specific regulatory mechanisms.
• Hypothesis generation for experimental design: Highlights candidate regulatory interactions and axes to guide targeted experimental validation in UM pathogenesis studies.

Methodology:

Uses TCGA-derived expression data to compute statistical correlations among miRNAs, lncRNAs, and mRNAs, integrates predicted or validated RNA–RNA interactions and sense–antisense overlap data, and incorporates TF–RNA correlation values enriched with ChiPseq data, together with expression profiling across UM and physiological tissues.