bio-differential-splicing

name	bio-differential-splicing
description	Detects differential alternative splicing between conditions using rMATS-turbo (binomial LRT on junction counts), leafcutter (Dirichlet-multinomial GLM on intron clusters), MAJIQ V3 deltapsi/HET (Bayesian posterior on LSVs), SUPPA2 (empirical-null on TPM-derived PSI), or Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage). Reports FDR-corrected significance and delta PSI effect sizes. Tools differ in statistical model, annotation dependence, calibration regime, and replicate-count requirements. Use when comparing splicing patterns between treatment groups, tissues, or disease states.
tool_type	mixed
primary_tool	rMATS-turbo

Version Compatibility

Reference examples tested with: rMATS-turbo 4.3+, SUPPA2 2.4+, leafcutter 0.2.9+, MAJIQ 3.0+, Shiba 0.5+, STAR 2.7.11+, regtools 1.0+, pandas 2.2+, R 4.4+

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

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters
• CLI: <tool> --version then <tool> --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.

Differential Splicing

Detect splicing changes between conditions. Tool choice is a decision about statistical model, annotation dependence, and calibration regime under the specific experimental design — not a preference. Wrong tool for the design produces uncalibrated FDR or systematic effect-size bias.

Statistical Model Taxonomy

Tool	Model	Test statistic	Min reps per group	Calibration regime	Fails when
rMATS-turbo	Binomial counts with hierarchical PSI variance	LRT on |ΔPSI| > cutoff (default 0.0001)	n>=3	Well-calibrated at n>=3 with adequate junction reads	Junction read imbalance; very low coverage; uncorrected for confounders
leafcutter	Dirichlet-multinomial GLM at cluster level	LRT on group factor	n>=2 (n>=3 preferred)	Strong at n>=3; novel-junction-friendly	Undersampled clusters (DM dispersion unstable); cluster topology arbitrariness
MAJIQ deltapsi	Beta-binomial bootstrap -> posterior over PSI per LSV	P(|ΔPSI| > T) threshold (T=0.2)	n>=3	Replicate-structured n=3 vs n=3	Cohorts where between-sample variability dominates between-group
MAJIQ HET	Same model, heterogeneity-aware	Per-LSV permutation-based test	n>=10	n>=10 vs n>=10 cohort designs	Tightly-controlled small replicate experiments
SUPPA2 (empirical)	Empirical null from between-replicate ΔPSI	ECDF on |ΔPSI| conditioned on TPM	n>=4	n>=4 vs n>=4 with paired-end deep sequencing	n<=3 vs n<=3 (sparse null collapses)
SUPPA2 (classical)	Wilcoxon rank-sum on PSI distributions	Wilcoxon p-value	n>=2	Small samples; non-parametric backup	Cassette events with tight PSI distributions
Shiba (2025)	Beta-binomial with explicit junction-imbalance correction	LRT	n>=2	n=2-3 vs n=2-3	Established benchmarks limited (new tool)
LeafcutterMD	Dirichlet-multinomial outlier mode	Per-sample p-value	n=1 vs cohort >=20	Single-patient vs cohort	Too few controls (<20)
FRASER 2.0	Beta-binomial autoencoder on Intron Jaccard Index	Per-sample p-value with delta cutoff	n=1 vs cohort >=20	n>=20 control cohort, single-patient query	See outlier-splicing-detection for this regime

The first decision is which regime the design falls into: between-group with replicates, heterogeneous cohort, or single-sample-vs-cohort. Within each regime, tool choice is much smaller (1-2 options).

Comprehensive 2023-2026 benchmarks: Olofsson 2023 Biochem Biophys Res Commun; Tran 2025 WIREs RNA; Kubota 2025 NAR. Methodology evolves — verify benchmarks and tool docs before reporting. Default 2026 recommendation: run two complementary tools (rMATS + leafcutter) and require concordance for high-confidence calls.

Decision Tree by Experimental Design

Scenario	Recommended tool	Why	Threshold
Standard n=3 vs n=3, GENCODE-annotated	rMATS-turbo + leafcutter (concordance)	Two algorithmic families; concordant hits = high-confidence	FDR<0.05, |ΔPSI|>0.10
n=2 vs n=2 small pilot	Shiba	Junction-imbalance correction matters most at low coverage	FDR<0.10, |ΔPSI|>0.10
n=10+ vs n=10+ heterogeneous (clinical, GTEx-style)	MAJIQ V3 HET	HET designed for between-sample heterogeneity	P(|ΔPSI|>0.2)>0.95
Single rare-disease patient vs panel of n>=20	FRASER 2.0 (see outlier-splicing-detection)	Outlier detection statistical model is fundamentally different	padj<0.05, |delta-jaccard|>=0.1
Time-course / multi-condition design	Custom DEXSeq or limma on PSI matrix	rMATS/leafcutter primarily 2-group	FDR<0.05 on time:group interaction
Paired tumor-normal	rMATS with --paired-stats	Paired test reduces inter-patient variance	FDR<0.05, paired |ΔPSI|>0.10
Cancer with spliceosomal mutation (SF3B1, U2AF1)	leafcutter or MAJIQ denovo	Cryptic events not in annotation	FDR<0.05; check 3'ss shifts in IGV
TDP-43 loss / ALS post-mortem	leafcutter denovo	Cryptic exons not in annotation	FDR<0.05; expect UNC13A, STMN2
Non-model organism without GENCODE-grade annotation	leafcutter	Annotation-free	FDR<0.05, |ΔPSI|>0.10
Long-read available	rMATS-long, FLAIR diffSplice	See long-read-splicing	Tool-specific

rMATS-turbo Differential Analysis

Goal: Detect statistically significant differential splicing between two groups from BAMs.

Approach: Run rMATS-turbo without --statoff, then filter by FDR + ΔPSI + per-replicate coverage.

rmats.py \
    --b1 condition1_bams.txt \
    --b2 condition2_bams.txt \
    --gtf annotation.gtf \
    -t paired \
    --readLength 150 \
    --variable-read-length \
    --libType fr-firststrand \
    --nthread 8 \
    --od rmats_output \
    --tmp rmats_tmp \
    --novelSS \
    --cstat 0.05

--cstat 0.05 tests |ΔPSI| > 0.05; raise to 0.10 for stricter discovery. --novelSS enables novel-junction discovery (recommended with STAR 2-pass). For paired designs, add --paired-stats.

import pandas as pd
import numpy as np

se = pd.read_csv('rmats_output/SE.MATS.JC.txt', sep='\t')

def min_per_rep(s):
    return s.str.split(',').apply(lambda x: min(int(v) for v in x))

se['min_inc'] = min_per_rep(se['IJC_SAMPLE_1']).combine(min_per_rep(se['IJC_SAMPLE_2']), min)
se['min_skip'] = min_per_rep(se['SJC_SAMPLE_1']).combine(min_per_rep(se['SJC_SAMPLE_2']), min)

significant = se[
    (se['FDR'] < 0.05) &
    (se['IncLevelDifference'].abs() > 0.10) &
    ((se['min_inc'] + se['min_skip']) >= 10)
].copy()

significant['score'] = -np.log10(significant['FDR']) * significant['IncLevelDifference'].abs()
top = significant.nlargest(50, 'score')

leafcutter Differential Intron Usage

Goal: Detect differential intron-cluster usage annotation-free, capturing novel junctions and complex multi-junction events.

Approach: Extract junctions with regtools, cluster introns by shared splice sites, run cluster-level Dirichlet-multinomial test.

for bam in *.bam; do
    regtools junctions extract -a 8 -m 50 -s XS "$bam" -o "${bam%.bam}.junc"
done
ls *.junc > juncfiles.txt

python leafcutter_cluster_regtools.py \
    -j juncfiles.txt \
    -o leafcutter \
    -m 50 \
    -l 500000

library(leafcutter)

groups <- data.frame(
    sample = c('s1', 's2', 's3', 's4', 's5', 's6'),
    group = c('control', 'control', 'control', 'treatment', 'treatment', 'treatment')
)
write.table(groups, 'groups.txt', sep = '\t', quote = FALSE, row.names = FALSE, col.names = FALSE)

system('leafcutter_ds.R --num_threads 4 --exon_file gencode_exons.txt.gz \
    leafcutter_perind_numers.counts.gz groups.txt -o ds_results')

cluster_sig  read.table header   sep  
intron_effects  read.table header   sep  

sig_clusters  subsetcluster_sig p.adjust  

LeafCutter2 (Buen Abad Najar 2025 bioRxiv) extends leafcutter with NMD-aware classification of unproductive splicing — useful when AS-NMD coupling is the question.

MAJIQ V3 Differential Analysis

Goal: Detect differential LSVs with full posterior distributions over ΔPSI; ideal for complex multi-junction events and heterogeneous cohorts.

Approach: Build splice graph -> compute coverage per group -> run deltapsi (replicate-structured) or heterogen (cohort-style).

majiq build annotation.gff3 -c settings.ini -j 8 -o build_output

majiq deltapsi \
    -grp1 build_output/ctrl1.majiq build_output/ctrl2.majiq build_output/ctrl3.majiq \
    -grp2 build_output/trt1.majiq build_output/trt2.majiq build_output/trt3.majiq \
    -n control treatment \
    -o deltapsi_output \
    --minreads 10 --minpos 3 \
    -j 8

majiq heterogen \
    -grp1 build_output/het_ctrl{1..20}.majiq \
    -grp2 build_output/het_trt{1..20}.majiq \
    -n control treatment \
    -o heterogen_output \
    -j 8

voila view -p 5000 -j 8 build_output/splicegraph.zarr deltapsi_output/control_treatment.deltapsi.voila -o voila_html

MAJIQ V3 (Aicher, Slaff, Jewell, Barash bioRxiv 2024; public release 2025) uses Zarr storage (splicegraph.zarr); V2's SQLite splicegraph is deprecated. MAJIQ reports posterior probability P(|ΔPSI| > 0.2); thresholds are interpreted differently from FDR. Use HET for n>=10 vs n>=10 cohort designs (clinical, GTEx-style); deltapsi for tightly controlled n=3 vs n=3.

SUPPA2 Differential Analysis

Goal: Quick differential splicing from existing transcript quantifications, useful as a sanity check or pilot.

Approach: Generate per-condition PSI files from Salmon TPM, then run diffSplice with empirical or classical p-values.

suppa.py generateEvents -i annotation.gtf -o events -f ioe -e SE SS MX RI

for ev in SE A5 A3 MX RI; do
    suppa.py psiPerEvent -i events_${ev}_strict.ioe -e ctrl_tpm.tsv -o ctrl_${ev}
    suppa.py psiPerEvent -i events_${ev}_strict.ioe -e trt_tpm.tsv -o trt_${ev}

    suppa.py diffSplice \
        -m empirical \
        -gc \
        -i events_${ev}_strict.ioe \
        -p ctrl_${ev}.psi trt_${ev}.psi \
        -e ctrl_tpm.tsv trt_tpm.tsv \
        -o diff_${ev}
done

For n<=3 designs, switch -m classical (Wilcoxon). Empirical null requires sufficient between-replicate observations to construct.

Shiba for Low-Coverage / Few-Replicate Designs

Goal: Detect differential splicing with explicit junction-imbalance correction — addresses a known false-positive source for rMATS-style methods.

Approach: Shiba is a Snakemake-based pipeline configured via YAML. Install via bioconda, write a config file describing groups + BAMs, then run with snakemake.

conda install -c bioconda shiba

# Edit config.yaml with reference GTF, BAM groups, output dir, thresholds
# Then run the Snakemake workflow:
snakemake -s snakeshiba.smk \
    --configfile config.yaml \
    --cores 8 \
    --use-singularity \
    --singularity-args "--bind $HOME:$HOME"

Shiba (Kubota 2025 NAR) reportedly outperforms rMATS at n=2 vs n=2 by correcting differential mappability between inclusion and skipping junctions; community calibration still emerging. See https://sika-zheng-lab.github.io/Shiba/ for the full config.yaml schema.

Per-Tool Failure Modes

rMATS: Confounder-Blind LRT

Trigger: Sequencing batch, RIN, library prep date, or sex correlates with the comparison of interest.

Mechanism: rMATS' default LRT does not natively accept covariates the way DESeq2 does; the --paired-stats flag handles paired designs but not arbitrary covariates.

Symptom: Many "significant" hits driven by batch rather than biological condition; PCA on PSI matrix shows samples clustering by batch rather than group.

Fix: Either (a) include batch as a stratification (run rMATS within each batch), (b) regress PSI matrix against batch in R, then test residuals, or (c) switch to leafcutter which accepts a confounders argument in differential_splicing.

leafcutter: Cluster Mis-Topology

Trigger: A cluster spans a complex topology (cassette + alternative donor in same cluster).

Mechanism: Cluster-level p-value reports "something in this cluster differs" but doesn't indicate which intron drove the change; downstream analysis needs per-intron effect sizes.

Symptom: Significant cluster, multiple introns with different ΔPSI directions, ambiguous biological interpretation.

Fix: Inspect cluster in leafviz; report per-intron effect sizes from ds_results_effect_sizes.txt; map cluster topology to canonical SE/A5SS/A3SS via flanking exon coordinates manually.

MAJIQ HET: Power vs Type-1 Tradeoff

Trigger: HET module on n=5-10 cohorts (between regimes).

Mechanism: HET assumes between-sample variability dominates; for moderate-replicate designs (n=5-10), HET is conservative and deltapsi is more powerful.

Symptom: HET reports few hits in n=5-10 designs; deltapsi on the same data reports many.

Fix: Use deltapsi for n=3-5; reserve HET for n>=10 with explicit cohort heterogeneity.

SUPPA2 Empirical: Sparse Null at Low Replicate

Trigger: n<=3 vs n<=3 with --method empirical.

Mechanism: Empirical null is ECDF of |ΔPSI| from between-replicate comparisons within each group, binned by transcript expression. Few replicates -> few null observations -> wide confidence on null distribution.

Symptom: Inflated FDR (15-30%); "significant" hits don't replicate or validate.

Fix: Use -m classical (Wilcoxon) for n<=3 vs n<=3; or switch tool entirely (leafcutter, Shiba).

Reconciliation: When Tools Disagree

The two most common short-read tools answer slightly different questions: rMATS classifies on annotated event templates; leafcutter classifies on observed cluster usage. Disagreement is informative.

Pattern	Likely cause	Action
rMATS sig, leafcutter not sig	rMATS junction imbalance OR rMATS event hits annotation that leafcutter clustered differently	Inspect locus in IGV; check Shiba on the same locus
leafcutter sig, rMATS not sig	Novel junction not in rMATS annotation; rMATS --novelSS may have missed it	Verify --novelSS was on; rerun if not
Both sig, opposite ΔPSI direction	Event class mismatch (rMATS calls SE positive, leafcutter sees A5SS shift in same cluster)	Manually map cluster topology to event class
Both sig, same direction	High-confidence call	Report; cross-validate with sashimi-plot
All tools null but biology suggests change	Underpowered design or wrong regime	Increase replicates; check whether outlier-splicing-detection regime applies

Operational rule: for high-confidence reporting, require concordant detection in two tools from different algorithmic families (event-based + cluster-based, or LSV + isoform-based). Document both calls and any explainable disagreements.

rMATS Output Columns Reference

Column	Meaning
IJC_SAMPLE_1 / SJC_SAMPLE_1	Comma-delimited inclusion / skipping junction counts per replicate, group 1
IJC_SAMPLE_2 / SJC_SAMPLE_2	Same for group 2
IncFormLen / SkipFormLen	Effective lengths normalizing PSI for differential mapping opportunity
upstreamES/EE, downstreamES/EE	Flanking exon coordinates (genomic order; strand-agnostic in column meaning)
exonStart_0base / exonEnd	Cassette exon coordinates (0-based half-open)
PValue	LRT p-value of |ΔPSI| > cutoff
FDR	BH-adjusted PValue within event class
IncLevel1, IncLevel2	Comma-delimited per-replicate PSI values
IncLevelDifference	mean(IncLevel1) - mean(IncLevel2); sign matches --b1 - --b2 order

Replicate Count and Power

Design	Recommended tools	Expected power for ΔPSI=0.2
n=2 vs n=2	leafcutter or Shiba; avoid SUPPA2	Marginal; many real effects missed
n=3 vs n=3	rMATS-turbo + leafcutter	Adequate at moderate coverage; standard
n=5 vs n=5	rMATS or leafcutter, MAJIQ deltapsi	Good; recommended for publication
n=10+ vs n=10+ heterogeneous	MAJIQ-HET	Designed for this scale
Single patient vs n=20+ controls	leafcutterMD or FRASER2	Outlier regime; see outlier-splicing-detection

For an effect-size of |ΔPSI|=0.10 (typical biological signal), power generally requires n>=4 and >=20 junction reads per replicate. Below this, expect to miss most real changes.

Significance and Effect-Size Thresholds

Stringency	|ΔPSI|	FDR	Use case
Lenient	> 0.05	< 0.10	Discovery, exploratory, hypothesis generation
Standard	> 0.10	< 0.05	Publication; default reporting threshold
Stringent	> 0.20	< 0.01	Validation cohort, follow-up targets

For MAJIQ: posterior probability P(|ΔPSI| > 0.2) >= 0.95 is roughly equivalent to standard stringency. Always document tool, threshold, and rationale.

Biologically meaningful ΔPSI varies by context:

• A poison exon shift of |ΔPSI|=0.10 can halve functional protein (huge biology, modest number).
• A stoichiometric isoform shift of |ΔPSI|=0.10 may be physiologically silent.
• Therapeutic ASO target: SMA nusinersen aims for ΔPSI~+0.30 in SMN2 exon 7.

Confounder Handling

rMATS does not natively accept arbitrary covariates. Workarounds:

1. Stratification: run rMATS within each batch separately and meta-analyze.
2. PSI residuals (logit-transformed): PSI is bounded [0,1]; raw linear regression near the boundaries is biased. Logit-transform first, regress on confounders, then test residuals.
3. Switch to leafcutter (R function accepts confounders matrix; CLI accepts confounders as additional columns in the groups file).

import numpy as np
import statsmodels.formula.api as smf

# logit-transform PSI before residualization (PSI is bounded [0,1])
eps = 1e-3
psi['logit_psi'] = np.log((psi['psi'].clip(eps, 1 - eps)) / (1 - psi['psi'].clip(eps, 1 - eps)))
psi['psi_resid'] = smf.ols('logit_psi ~ batch + RIN', data=psi).fit().resid
# then test psi_resid by group via Wilcoxon

leafcutter accepts confounders two ways:

• R function: differential_splicing(counts, x, confounders=numeric_matrix) accepts a numeric covariate matrix
• CLI script: leafcutter_ds.R reads confounders from additional columns in the groups file (3rd, 4th, ... columns), NOT from a --confounders flag

MAJIQ does not accept arbitrary confounders; use stratification or switch tool.

Always check confounding before reporting: PCA on PSI matrix; if PC1 separates by batch rather than group, the comparison is confounded.

Multi-Group / Multi-Factor Designs

Design	Approach
3 groups (e.g. drug A, drug B, control)	Pairwise rMATS or leafcutter; OR limma/DESeq2 on logit-PSI matrix
Time-course (e.g. 0h, 6h, 24h)	DEXSeq on event counts with time as factor; or limma::lmFit on PSI matrix
2x2 factorial (genotype × treatment)	DEXSeq with interaction term; rMATS pairwise on interaction subsets
Continuous covariate (dose, age)	limma::lmFit on logit-PSI ~ covariate

For complex designs, custom regression on the PSI matrix is more flexible than rMATS/leafcutter pairwise.

Common Errors

Error	Cause	Solution
rMATS: numpy.AxisError	rMATS version mismatch with numpy >=2.0	Pin numpy<2.0 or update rMATS-turbo to >=4.3
leafcutter: zero variance in cluster	Cluster has all-zero counts in a group	Pre-filter with --min_samples_per_intron 5 --min_samples_per_group 3
MAJIQ: out of memory	Default settings on >50-sample cohort	Use --mem-profile flag; chunk samples; consider HET for large cohorts
SUPPA2: no events with sufficient coverage	Salmon/kallisto TPM filter too strict upstream	Lower upstream TPM threshold; verify event annotations
voila: missing splicegraph.zarr (V3) or splicegraph.sql (V2; deprecated)	Forgot to keep build output directory	Re-run majiq build; output must persist for VOILA
regtools: too many open files	Many BAMs in one batch	ulimit -n 4096 or batch in groups

Result Prioritization

Goal: Rank events by combined statistical and biological significance for follow-up.

Approach: Composite score combining FDR and effect size, then enrich for biology (RBP binding, NMD sensitivity, conservation, disease relevance).

import pandas as pd
import numpy as np

sig['score'] = -np.log10(sig['FDR']) * sig['IncLevelDifference'].abs()
sig['exon_length'] = sig['exonEnd'] - sig['exonStart_0base']
sig['nmd_likely'] = (sig['exon_length'] % 3 != 0)
top_events = sig.nlargest(50, 'score')

Cross-reference top hits with:

• eCLIP/ENCODE RBP target databases (POSTAR3, oRNAment, RBP2GO) -> candidate trans-regulators
• Disease-specific signatures: SF3B1 cryptic 3'ss for MDS/CLL/UM; TDP-43 cryptic exons (UNC13A, STMN2) for ALS/FTD
• Conservation: VastDB cross-species PSI for evolutionary support
• Splice-site predictions: SpliceAI scores for the involved sites (see splice-variant-prediction)

Common Pitfalls

• Junction read imbalance (cassette exon flanks have unequal mapping opportunity) inflates rMATS false positives; Shiba explicitly corrects this.
• Comparing tool outputs naively — MAJIQ posteriors and rMATS FDR are different scales; use threshold equivalents (P>0.95 ~ FDR<0.05 in many regimes) but confirm with simulation when reporting.
• Forgetting NMD direction — increased PSI of a poison exon decreases protein. Always check whether the alternative form is PTC-introducing using ORF-aware annotation.
• Cryptic splicing in TDP-43 loss / SF3B1-mutant samples — annotation-bound tools (rMATS, SUPPA2) miss these; need leafcutter or MAJIQ with denovo mode.
• Forgetting strand — wrong --libType halves usable junctions. Confirm with RSeQC infer_experiment.py.
• Reporting one tool's call as ground truth — discordance between rMATS and leafcutter is informative, not a problem to hide.
• Skipping confounder check — always run PCA on PSI matrix before final reporting.
• Using empirical SUPPA2 at n<=3 — calibration collapses; use classical mode or different tool.

Related Skills

• splicing-quantification - PSI estimation per event; foundational
• splicing-qc - Run BEFORE differential to verify library, depth, strandedness; avoid downstream surprises
• isoform-switching - DTU framework with NMD/ORF/domain consequences; complementary to event-level
• sashimi-plots - Visualize differential events for QC and reporting
• outlier-splicing-detection - Single-sample-vs-cohort regime (FRASER2/DROP); use when not 2-group
• splice-variant-prediction - SpliceAI / Pangolin for variant-driven mechanistic explanation of differential events
• long-read-splicing - Differential analysis from full-length isoforms; use when short-read insufficient
• read-alignment/star-alignment - STAR 2-pass cohort-style required upstream

References

• Shen et al 2014 PNAS - rMATS original
• Wang et al 2024 Nat Protoc - rMATS-turbo
• Li et al 2018 Nat Genet - leafcutter (Dirichlet-multinomial GLM)
• Buen Abad Najar et al 2025 bioRxiv - LeafCutter2 (NMD-aware unproductive splicing)
• Vaquero-Garcia et al 2016 eLife - MAJIQ LSV framework
• Vaquero-Garcia et al 2023 Nat Commun - MAJIQ-HET heterogeneity module
• Aicher, Slaff, Jewell, Barash 2024 bioRxiv - MAJIQ V3
• Trincado et al 2018 Genome Biol - SUPPA2
• Kubota et al 2025 NAR - Shiba (junction-imbalance correction)
• Olofsson et al 2023 Biochem Biophys Res Commun 653:31-37 - benchmark across tools
• Tran et al 2025 WIREs RNA - methodology review
• Brown et al 2022 Nature - UNC13A cryptic exon (TDP-43 / ALS)
• Klim et al 2019 Nat Neurosci - STMN2 cryptic splicing (ALS)
• Darman et al 2015 Cell Rep - SF3B1 cryptic 3'ss