RNA-SeQC

RNA-SeQC assesses RNA sequencing (RNA-seq) data quality by computing metrics that evaluate sequencing performance and library quality for transcriptome analysis using next-generation sequencing.

Key Features:

• Quality Control Metrics: Computes a suite of metrics including yield, alignment and duplication rates, GC bias, rRNA content, exon/intron/intragenic alignment distribution, coverage continuity across transcripts, 3'/5' end bias, and count of detectable transcripts.
• Multi-Sample Evaluation: Performs simultaneous assessment of multiple samples to enable comparative analysis of library construction protocols, input materials, and experimental parameters.
• Modularity and Integration: Provides modular outputs for integration into bioinformatics pipelines and monitoring of alignable read counts, duplication rates, and rRNA contamination.
• Decision Support for Sample Inclusion: Supplies detailed quality metrics to inform selection or exclusion of samples for downstream analyses.

Scientific Applications:

• Experiment Design: Evaluates sequencing and library preparation quality to inform experimental setup and protocol selection.
• Process Optimization: Identifies issues in sample preparation or sequencing runs through targeted quality metrics.
• Downstream Computational Analysis: Provides quality-filtered inputs and metric-based sample selection to support accurate transcriptome characterization.

Methodology:

Computes the listed RNA-seq quality metrics (yield, alignment and duplication rates, GC bias, rRNA content, region-specific alignment counts, coverage continuity, 3'/5' bias, and detectable transcript counts).