R-SAP

R-SAP analyzes high-throughput RNA-Seq datasets to characterize transcripts, quantify expression in RPKM (reads per kilobase of exon model per million mapped reads), and detect aberrant splicing and chimeric events.

Key Features:

• Fully Automated Pipeline: Automates processing from raw RNA-Seq input through generation of expression level estimates.
• Multi-Threading Capability: Distributes computational tasks across multiple processors to accelerate analysis and scales near-linearly with additional threads.
• Hierarchical Decision-Making Procedure: Employs a structured decision hierarchy for transcript characterization enabling identification and classification of transcript classes.
• Expression Level Quantitation (RPKM): Produces expression estimates reported as RPKM (reads per kilobase of exon model per million mapped reads) for cross-study comparison.
• Transcript Characterization: Identifies and quantifies transcripts arising from alternative splicing and chimeric events.
• Microarray Concordance: Demonstrates high concordance between RPKM-based expression estimates and traditional microarray measurements.

Scientific Applications:

• Transcript diversity analysis: Quantifies transcript isoform abundance to assess transcriptome complexity.
• Alternative splicing and chimeric transcript detection: Detects and characterizes aberrant splicing events and chimeric formations.
• Gene regulation studies: Enables examination of expression changes relevant to regulatory mechanisms.
• Disease mechanism investigation: Supports analysis of transcriptome alterations associated with disease processes.
• RNA processing anomaly characterization: Assesses functional implications of anomalous RNA processing events.

Methodology:

Integrates data preprocessing, alignment, transcript assembly, and quantitation, applies a hierarchical decision-making procedure for transcript characterization, and leverages parallel multi-threading for computation.