Bio Isoform Switching

Version Compatibility

Reference examples tested with: IsoformSwitchAnalyzeR 2.11+, DRIMSeq 1.34+, DEXSeq 1.52+, satuRn 1.14+, stageR 1.28+, fishpond 2.14+, tximport 1.34+, tximeta 1.24+, Salmon 1.10+

Before using code patterns, verify installed versions match. If versions differ:

• R: packageVersion('') then ?function_name to verify parameters
• CLI: --version then --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Isoform Switching and Differential Transcript Usage

Identify shifts in which transcript a gene predominantly uses between conditions, and predict functional consequences. Statistically distinct from DGE and DTE; biologically distinct because the same gene-level expression can hide a complete isoform switch with major protein-level consequences.

DGE vs DTE vs DTU: Which Question Is Being Asked?

| Question | Statistic | Tool | Example claim | |----------|-----------|------|----------------| | DGE Does the gene total change? | Sum of transcript counts | DESeq2, edgeR, limma-voom | "Gene X is upregulated 2-fold" | | DTE Does this transcript change in absolute abundance? | Per-transcript count | swish (fishpond), DESeq2 on transcripts, sleuth | "Transcript X-201 is upregulated 2-fold" | | DTU Do proportions of transcripts within the gene shift? | Vector of per-transcript proportions | DRIMSeq, DEXSeq, satuRn (+ stageR) | "Gene X switches from isoform 201 (50% -> 10%) to 202 (50% -> 90%)" |

DTU is statistically harder than DGE because:

1. The null is compositional (proportions sum to 1; one transcript up means another down).
2. Multi-stage testing is required: gene-level "any DTU" + transcript-level "which transcript" -> stageR formalizes this.
3. Quantification uncertainty propagates when transcripts are similar (Salmon EM ambiguity).

DTU and event-level differential splicing answer related but distinct questions: rMATS' IncLevelDifference is essentially a 1-D projection of a DTU shift onto a single event coordinate. The pragmatic 2026 default: run both an event-level tool (rMATS or leafcutter) and a DTU pipeline; reconcile.

Tool Selection for DTU

| Tool | Model | When to use | Fails when | |------|-------|-------------|------------| | IsoformSwitchAnalyzeR v2 | Wraps DEXSeq or satuRn + functional consequence annotation | Standard interpretation workflow with NMD/domain output | Manual DTU control needed; very large cohorts (>200) | | DRIMSeq | Dirichlet-multinomial on transcript counts; gene-level DTU | Pre-filter step before DEXSeq/satuRn | Cannot annotate functional consequences alone | | DEXSeq | Negative-binomial GLM on exon-bin or transcript counts | Classic DTU; conservative; 5 replicates; else DEXSeq.** For exactly 5 replicates per condition (boundary), explicitly choose; results may differ.

Decision Tree by Research Question

| Question | Recommended approach | |----------|----------------------| | Functional consequences of switches (domains, NMD, signal peptide) | IsoformSwitchAnalyzeR v2 with full external annotator pipeline | | Pure statistical DTU (gene-level + transcript-level OFDR) | DRIMSeq (filter) -> DEXSeq -> stageR; or -> satuRn -> stageR for n>5 | | DTU with proper quantification uncertainty | Salmon --numGibbsSamples 20 -> tximeta -> swish for DTE; concurrent DTU | | Single-cell DTU | satuRn (DEXSeq doesn't scale to scRNA-seq) | | Long-read DTU (PacBio Iso-Seq, ONT) | IsoformSwitchAnalyzeR v2 long-read input mode (no Salmon EM uncertainty) | | Time-course DTU | DEXSeq with time as factor + interaction; or limma::lmFit on logit-prop matrix | | Cancer / disease — switch hits -> mechanism | Standard pipeline + cross-reference with eCLIP, ClinVar, COSMIC | | Therapeutic ASO target identification | Standard pipeline + sashimi visualization + SpliceAI design |

IsoformSwitchAnalyzeR v2 Workflow

Goal: Identify isoform switches with functional consequences in one integrated workflow.

Approach: Import Salmon, pre-filter, run statistical test (satuRn auto-selected if any condition has >5 replicates, else DEXSeq), annotate switches with external tools (CPC2, Pfam, SignalP, IUPred2A or DeepTMHMM), then summarize consequences.

library(IsoformSwitchAnalyzeR)

salmonQuant 50-55 nt upstream of the last exon-exon junction** (Maquat 2004 *Nat Rev Mol Cell Biol*; Lykke-Andersen & Jensen 2015 *Nat Rev Mol Cell Biol*).

**Mechanism:** Spliceosome deposits the Exon Junction Complex (EJC) ~20-24 nt upstream of every exon-exon junction. During the pioneer round of translation, ribosome reading through removes EJCs upstream of the stop codon. If a stop codon precedes the last EJC by >50 nt, the EJC remains, recruits UPF1 -> SMG1 phosphorylation -> SMG6/SMG7 -> mRNA decay.

**Caveats and exceptions:**
- **Last-exon PTCs escape NMD** — can be dominant-negative or gain-of-function (e.g. MYH7 truncating variants).
- **3'UTR length matters**: very long 3' UTRs (>1 kb past stop) trigger NMD via UPF1 binding even without EJCs (faux-3'UTR rule).
- **Tissue-specific NMD**: SMG6 vs SMG5/7 ratios vary; UPF1 stress conditions modulate.
- **PTC distance must be measured on the spliced transcript**, not the genomic distance.
- **~10-20% of "predicted NMD" transcripts escape NMD per orthogonal RNA-seq** (Lindeboom 2016 *Nat Genet*; ~22% of canonical PTC-bearing transcripts escape in some tissues). Treat NMD prediction as probabilistic, not certain.

IsoformSwitchAnalyzeR's `analyzeSwitchConsequences` with `'NMD_status'` evaluates this from the predicted ORF + transcript model.

## AS-NMD as a Regulatory Layer

A large class of conserved alternative splicing events is **deliberately PTC-introducing** to titrate functional protein levels:

- **All major SR proteins** (SRSF1-12) autoregulate via poison exons (Lareau 2007 *Nature*; Ni 2007 *Genes Dev*)
- **All major hnRNPs** likewise
- **~70% of ribosomal protein genes** use AS-NMD (Mauger 2016 *Neuron*; Pirnie 2017 *RNA*)
- **SCN1A** poison exon -> Stoke STK-001 ASO in Phase 1/2 for Dravet syndrome (Han 2020 *Sci Transl Med*)

**Functional implication:** an *increase* in PSI of a poison exon *decreases* functional protein. Sign-of-effect in DTU output is opposite from intuition for these genes. Always check whether the alternative form is PTC-bearing before interpreting direction.

**Disease examples:**
- TDP-43 cryptic exons (UNC13A, STMN2) introduce PTCs -> NMD on disease-relevant transcript (Brown 2022 *Nature*)
- Last-exon variants in MYH7, CARDIA: escape NMD -> dominant-negative protein

## Manual DTU Pipeline (DRIMSeq + DEXSeq + stageR)

The canonical reference is the *F1000Research* "Swimming downstream" workflow (Love, Soneson, Patro 2018; Bioconductor `rnaseqDTU`).

```r
library(tximeta); library(DRIMSeq); library(DEXSeq); library(stageR)

se 50 samples or single-cell DTU.

**Mechanism:** DEXSeq fits a NB GLM per exon-bin per gene; computational cost scales linearly with samples × bins.

**Symptom:** `estimateDispersions` takes hours; `testForDEU` exhausts memory.

**Fix:** Switch to satuRn (designed for scale, including scRNA-seq); run with parallelization (`BPPARAM = MulticoreParam(8)`).

### DRIMSeq: Filtering Sensitivity

**Trigger:** Default `dmFilter` parameters too strict for low-expression cohort.

**Mechanism:** `min_samps_feature_expr = 3, min_feature_expr = 10` drops transcripts seen in  0.1 | Minimum biological effect |
| Consequence q-value |  inflated transcript-level FDR; gene-level multiple-testing burden ignored.
- **Forgetting NMD direction** -> sign-of-effect on protein opposite to sign-of-effect on transcript when alternative form is a PTC-bearer. Always check.
- **Treating short-read-derived isoform calls as ground truth** -> Salmon EM is uncertain; use Gibbs samples + swish if quantification uncertainty matters.
- **Comparing across annotations** -> GENCODE basic vs comprehensive, RefSeq, Ensembl all have different transcript catalogs; switches "appear" or "disappear" with annotation choice. Document version.
- **Not running long-read where possible** -> Iso-Seq / ONT removes ambiguity for genes with many similar isoforms (TTN, MAPT, NEFM, DSCAM).
- **Choosing satuRn or DEXSeq blindly at the n=5 boundary** -> IsoformSwitchAnalyzeR v2 auto-selects based on >5 vs  reviewers will demand it; do it upfront.
- **Forgetting stageR also corrects gene-level p when starting from DRIMSeq** -> DRIMSeq's `gene_p` should be passed as `pScreen`, not raw transcript p-values.

## Related Skills

- differential-splicing - Event-level (rMATS, leafcutter, MAJIQ) complementary to DTU
- splicing-quantification - PSI is a 1D projection of DTU shifts
- splicing-qc - Verify upstream library, depth, alignment before DTU
- sashimi-plots - Required visualization for switch validation and reporting
- splice-variant-prediction - Connects SpliceAI variant predictions to specific isoforms
- long-read-splicing - Full-isoform DTU bypasses transcript-quant uncertainty; preferred for many-isoform genes
- pathway-analysis/go-enrichment - Pathway enrichment of switching genes
- rna-quantification/alignment-free-quant - Salmon with `--numGibbsSamples` is upstream

## References

- Vitting-Seerup 2025 *bioRxiv* - IsoformSwitchAnalyzeR v2
- Vitting-Seerup & Sandelin 2017 *Bioinformatics* - IsoformSwitchAnalyzeR original
- Anders et al 2012 *Genome Res* - DEXSeq
- Nowicka & Robinson 2016 *F1000Research* - DRIMSeq
- Gilis et al 2022 *F1000Research* - satuRn
- Zhu et al 2019 *NAR* - swish / fishpond
- Van den Berge et al 2017 *Genome Biol* - stageR
- Love, Soneson, Patro 2018 *F1000Research* - Swimming downstream DTU workflow
- Maquat 2004 *Nat Rev Mol Cell Biol* - NMD review
- Lykke-Andersen & Jensen 2015 *Nat Rev Mol Cell Biol* - NMD update
- Lindeboom et al 2016 *Nat Genet* - NMD escape rates from RNA-seq
- Lareau et al 2007 *Nature* - SR protein AS-NMD autoregulation
- Mauger et al 2016 *Neuron* - ribosomal protein AS-NMD
- Brown et al 2022 *Nature* - UNC13A cryptic exon (TDP-43)
- Han et al 2020 *Sci Transl Med* - SCN1A poison exon ASO

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [GPTomics](https://github.com/GPTomics)
- **Source:** [GPTomics/bioSkills](https://github.com/GPTomics/bioSkills)
- **License:** MIT

Install and usage instructions live in the source repository linked above.