RSeQC

RSeQC performs comprehensive quality control and evaluation of RNA sequencing (RNA-seq) data to assess sequence quality, mapping distribution, coverage uniformity, strand specificity, nucleotide composition bias, PCR and GC bias, and transcript integrity.

Key Features:

• Quality Control Modules: Evaluates sequence quality, nucleotide composition bias, PCR bias, and GC content bias.
• Sequencing Saturation: Assesses whether sequencing depth has reached saturation for transcript discovery and quantification.
• Mapped Reads Distribution: Analyzes how reads are distributed across genome structure.
• Coverage Uniformity: Measures evenness of coverage across transcripts to inform expression quantification.
• Strand Specificity: Validates strand-specific nature of RNA-seq data to distinguish sense and antisense transcripts.
• Input Compatibility: Processes SAM/BAM formats from RNA-seq mappers and BED files for gene models.
• Efficiency and Visualization: Uses R scripts to generate visualizations and to handle large datasets with hundreds of millions of alignments.
• Programming Languages: Implemented in Python and C.
• Transcript Integrity Number (TIN): Computes TIN to measure RNA degradation at transcript and sample levels and supports correction of gene expression counts to mitigate degradation effects.

Scientific Applications:

• Transcriptome profiling: Provides QC metrics required for reliable transcriptome profiling.
• Gene expression analysis: Supplies coverage, saturation, and bias assessments used in expression quantification and differential expression.
• RNA degradation studies: Quantifies degradation effects using TIN and enables correction of expression counts for degraded samples.
• Translational and clinical research: Calibrates gene expression data with TIN to reduce degradation-related artifacts in archived clinical tissues.
• Cancer research, genomics, and personalized medicine: Enhances reliability of RNA-seq–based findings in studies across cancer research, genomics, and personalized medicine.

Methodology:

Parses SAM/BAM and BED inputs; evaluates sequence quality, nucleotide composition, PCR bias, and GC bias; computes sequencing saturation, mapped reads distribution, coverage uniformity, and strand specificity; computes Transcript Integrity Number (TIN) at transcript and sample levels; uses R scripts for visualization; implemented in Python and C.