RNA-seq 데이터에서 대체 스플라이싱 및 폴리아데닐화 식별

Executive Industry Relevance

Alternative splicing and polyadenylation analysis enables biopharma R&D teams to resolve isoform-specific regulatory mechanisms that are obscured at the gene-expression level. This mechanistic de-risking supports target validation by clarifying functional consequences of genetic perturbations and improving predictive confidence in lead identification. Integrating exon- and event-based RNA-seq workflows enhances assay readiness for downstream screening and translational biomarker development.

Strategic Applications in Biopharma R&D

Early Discovery & Target Validation

• Scientific Value: Interrogate therapeutic hypotheses by identifying differential exon usage and polyadenylation site shifts that reflect isoform-specific gene regulation.
• Operational Value: Apply exon-based methods to study alternative polyadenylation, enabling unified analysis of splicing and 3' end processing from a single RNA-seq dataset.
• Predictive Value: Detect cassette exon variations and distal-to-proximal polyadenylation shifts that inform functional target de-risking and portfolio triage.

Screening & Assay Development

• Scientific Value: Generate quantitative exon-level and splicing-event readouts (e.g., percent spliced in, inclusion levels) suitable for high-resolution compound screening.
• Operational Value: Normalize and filter count data using edgeR and limma workflows to ensure reproducibility across replicates and conditions.
• Assay Readiness: Produce visualization-ready outputs such as volcano plots, sashimi plots, and event plots for cross-functional review of splicing and polyadenylation dynamics.

Translational & Preclinical Research

• Scientific Value: Link differential splicing and polyadenylation events to disease-relevant genes (e.g., Wnk1, FOSL1) observed in knockout models, supporting mechanistic continuity.
• Operational Value: Use rMATS and maser packages to filter significant splicing events by false discovery rate and percent spliced-in change, enabling robust biomarker candidate selection.
• Translational Value: Visualize isoform-specific expression changes via sashimi plots to support preclinical validation of target engagement and mechanism of action.

Pipeline & Workflow Integration

This workflow fits within the discovery-to-preclinical continuum by providing isoform-resolution data that informs target validation, assay development, and mechanistic follow-up in lead optimization.

• Discovery Biology: Test hypotheses about isoform regulation by comparing exon usage and polyadenylation site distribution across genetic or pharmacological conditions.
• Screening: Generate standardized, quantitative exon and splicing event metrics that support reliable compound effect assessment in phenotypic or target-based assays.
• Analytics: Derive empirical statistics, p-values, and effect sizes from linear modeling (voom, lmfit, eBayes) and event-based tools (rMATS, diffSplice) to compare conditions objectively.
• Translational Research: Connect splicing and polyadenylation changes to gene-level phenotypes in preclinical models, supporting risk-adjusted advancement decisions.
• Enterprise Reuse: Deploy the R Markdown-based protocol as a reusable bioinformatics capability across projects studying genetic models, drug treatments, or disease tissues.

Operational & Enterprise Impact

• Scientific Value: Reduce mechanistic ambiguity by resolving isoform-specific regulatory layers missed in conventional gene-level differential expression analysis.
• Operational Value: Ensure reproducibility through standardized normalization (CPM, calcNormFactors), filtering thresholds, and validated Bioconductor packages (limma, edgeR, rMATS, maser).
• Strategic Value: Improve go/no-go decisions by providing early evidence of isoform-level target modulation, reducing late-stage biological attrition risk.
• Portfolio Impact: Enable risk-adjusted prioritization of targets based on concordant splicing and polyadenylation evidence across orthogonal methods (exon-based and event-based).

Implementation Considerations

• Requires bioinformatics expertise in RNA-seq analysis, linear modeling, and splicing event interpretation using R/Bioconductor.
• Dependent on aligned BAM files, gene annotation (GTF), and count matrices generated from standard RNA-seq preprocessing.
• Necessitates cross-team standardization of filtering criteria (e.g., CPM >1 in ≥3 samples) and statistical thresholds (FDR <0.1, ΔPSI ≥10%) for reproducible results.
• Involves adaptation of parameters (e.g., overlap settings, transpecific information) when applying count metrics to alternative splicing and polyadenylation pipelines.
• Includes practical limitations such as the need for sufficient sequencing depth to detect low-abundance isoforms and the importance of validating computational predictions with orthogonal methods (e.g., PCR, long-read sequencing).

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