FilTar

FilTar integrates RNA-Seq data into miRNA target prediction workflows to produce context-specific miRNA target predictions by reannotating 3'-UTRs and filtering targets by transcript expression to reduce false positives from generic seed-binding methods.

Key Features:

• Context-Specific Target Prediction: Integrates RNA-Seq data to tailor miRNA target predictions to specific cell types or tissues.
• 3'-UTR Reannotation: Performs sample-specific reannotation of 3'-UTRs by extending or truncating default annotations based on RNA-Seq read evidence.
• Expression-Based Filtering: Filters putative miRNA targets by removing interactions where the target transcript is not expressed in the sample according to RNA-Seq-derived expression levels.

Scientific Applications:

• miRNA Transfection Datasets: Applied to miRNA transfection datasets to improve prediction accuracy compared with generic methods.
• Cell Type-Specific Studies: Provides precise miRNA target predictions for cell type- or tissue-specific studies of gene regulation.
• Transcriptome Analysis: Supports transcriptome analyses by integrating RNA-Seq-informed 3'-UTR annotations and expression filtering to refine miRNA–mRNA interaction predictions.

Methodology:

Integrates RNA-Seq into pre-existing miRNA target prediction workflows; reannotates 3'-UTRs using RNA-Seq read evidence (extending or truncating default annotations); filters predicted miRNA–target interactions based on transcript expression levels derived from RNA-Seq.