SplicingEngine

Introduction

Alternative splicing generates proteomic diversity from a limited gene set and is a major mechanism of gene regulation. Dysregulation of splicing is implicated in cancer, neurodegeneration, and developmental disorders. We present SplicingEngine, a pure-Python pipeline for comprehensive alternative splicing analysis.

Methods

PSI Quantification

For each splicing event, PSI (Percent Spliced In) is computed as: PSI = inclusion_reads / (inclusion_reads + 2 × skipping_reads)

Five event types are supported: skipped exon (SE), alternative 5' splice site (A5SS), alternative 3' splice site (A3SS), mutually exclusive exons (MXE), and retained intron (RI).

Differential Splicing

Combined statistical test: two-sample t-test on PSI values + Fisher's exact test on read counts. P-values combined by Fisher's method. BH FDR correction applied. Significance: |dPSI| > 0.1, FDR < 0.05.

RBP Motif Enrichment

Position weight matrices for 15 canonical RBPs (SRSF1, PTBP1, HNRNPA1, RBFOX2, etc.) are scanned against flanking sequences of differential vs. non-differential events. Enrichment by Fisher's exact test.

Splicing Regulatory Network

Bipartite graph linking RBPs to regulated splicing events, with edge weights proportional to motif score × enrichment significance.

Results

• 500 splicing events quantified (8 samples, 4 per condition)
• 60 differential events (|dPSI|>0.1, FDR<0.05)
• Top event: ENASE0142 (dPSI=-0.39, SE type)
• Top RBP: SRSF1 (motif GGAGG, enrichment=5.28, p<0.0001)
• Splicing network: 15 RBPs × 60 events

Conclusion

SplicingEngine provides a complete, executable alternative splicing analysis pipeline covering PSI quantification, differential testing, and RBP regulatory analysis.

Code

https://github.com/junior1p/SplicingEngine

pip install numpy scipy pandas matplotlib
python splicing_engine.py