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splicing-qc

separate

Assesses RNA-seq data quality specifically for alternative splicing analysis. QC layers include experimental design audit (library prep, read length, depth, replicates), STAR 2-pass cohort-style alignment, junction saturation curves and discovery plateau detection, novel-vs-known junction ratio diagnostics, junction-overhang distribution, splice-site strength scoring (MaxEntScan intrinsic + SpliceAI context-aware), strandedness verification, GENCODE basic vs comprehensive choice, and rRNA contamination screening. Splicing analysis is more demanding than DGE on read length, depth, library prep, alignment strategy, and annotation choice — failures silently bias PSI estimates and inflate novel-junction false positives. Use when evaluating data suitability for splicing analysis, troubleshooting low event detection, or designing sequencing experiments where AS is a primary endpoint.

When to use

• evaluating data suitability for splicing analysis
• troubleshooting low event detection
• designing sequencing experiments where AS is a primary endpoint
• experimental design audit
• STAR 2-pass cohort-style alignment
• junction saturation curves and discovery plateau detection
• novel-vs-known junction ratio diagnostics
• splice-site strength scoring

When not to use

• DGE analysis

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