splicing-quantification

name	splicing-quantification
description	Quantifies alternative splicing as PSI (percent spliced in) from RNA-seq using rMATS-turbo (BAM-based event), SUPPA2 (TPM-based event), MAJIQ V3 (LSV-based Bayesian), leafcutter (annotation-free intron clusters), VAST-TOOLS (cross-species with microexon support), Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage), or IRFinder-S (intron retention coverage-aware). Distinguishes the five canonical event classes (SE, A5SS, A3SS, MXE, RI), special classes (microexons, exitrons, AFE/ALE), intron retention subtypes (canonical RI vs detained introns), and applies effective-length normalization. Use when measuring splice-site usage or isoform inclusion ratios from short-read RNA-seq.
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+, IRFinder-S 2.0+, kallisto 0.50+, Salmon 1.10+, pandas 2.2+, STAR 2.7.11+

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.

Splicing Quantification

Quantify alternative splicing events as PSI (percent spliced in) from RNA-seq. PSI = inclusion read evidence / (inclusion + skipping read evidence), normalized for differential mapping opportunity between isoforms. The choice of quantification unit (event, intron cluster, LSV, transcript) determines which biological questions can be answered and which failure modes apply.

Algorithmic Taxonomy

Family	Unit	Reference tools	Fails when
Event-based	Pre-defined SE/A5SS/A3SS/MXE/RI events from annotation	rMATS-turbo, SUPPA2, VAST-TOOLS	Event isn't in annotation; complex multi-junction events split arbitrarily; AFE/ALE confounded with splicing
LSV-based	Local Splice Variations at single source/target nodes	MAJIQ V3	Memory-constrained environments; cohorts smaller than ~3 reps; non-academic users (license)
Junction-cluster	Annotation-free intron clusters by shared splice sites	leafcutter, leafcutter2	Undersampled clusters lose power; topology biologically uninterpretable for novel events
Splice-graph	Graph nodes (non-overlapping exonic regions)	Whippet, Shiba	Whippet maintenance status uncertain since ~2022; complex multi-exon graphs
Coverage-aware (IR)	Intron body coverage + flanking junctions	IRFinder-S, S-IRFindeR, iREAD	Confounded by overlapping exons, repeats, low mappability regions
Isoform-based	Transcript abundance via EM	Salmon/kallisto + tximport	Salmon EM uncertainty propagates; many similar isoforms (TTN, MAPT) become indistinguishable

The agent's first decision is which family the question requires, not which tool. Switching family is the response to a tool failing within a family — switching tool within a family rarely fixes systematic blind spots.

Event Taxonomy (Beyond Standard SE/A5SS/A3SS/MXE/RI)

Class	Code	Biology	Detection caveat
Skipped exon (cassette)	SE	Default cassette-exon AS	Most common; well-handled by all tools
Alternative 5' splice site	A5SS	Alternative donor; intron 5' end varies	Sign convention tool-specific (see below)
Alternative 3' splice site	A3SS	Alternative acceptor; intron 3' end varies	Sensitive to BPS cancer mutations (SF3B1) — cryptic 3'ss ~10-30nt upstream
Mutually exclusive exons	MXE	Two exons paired, one included	Tool implementations vary; verify which "form 1" is yours
Retained intron	RI	Whole intron retained in mature mRNA	Junction-only quant systematically underdetects; needs IRFinder-S
Microexon	(sub-SE)	3-27 nt; neural-enriched, SRRM4-regulated	Missed by default aligner anchor lengths (>=20-30 nt); needs VAST-TOOLS, MicroExonator, or long-read
Exitron	n/a	Intronic region within an annotated CDS exon	Mis-classified as A5SS/A3SS by most tools; use ExitronFinder, ScanExitron
Alternative first exon (AFE)	AFE	Alternative TSS / promoter use	Promoter-driven, NOT spliceosomal; confirm with FANTOM CAGE before reporting as splicing
Alternative last exon (ALE)	ALE	Alternative cleavage/polyadenylation	APA-driven, NOT spliceosomal; confirm with 3'-end-seq
Detained intron (DI)	(RI subtype)	Nuclear-retained on mature mRNA, regulated by Clk kinases	Distinct from cytoplasmic NMD-targeted RI (Boutz 2015 Genes Dev); requires fractionation to confirm
Recursive splicing	(intron subtype)	Long introns >50kb spliced via internal ratchet points	Sibley 2015 Nature; only detectable with long-read or nascent RNA-seq

Tool-agnostic taxonomy reference: Wang 2008 Nature; Vaquero-Garcia 2016 eLife (LSV); Tapial 2017 Genome Res (VastDB).

Tool Selection Matrix

Tool	Best for	Input	Strengths	Fails when
rMATS-turbo	Standard SE/A5SS/A3SS/MXE/RI in annotated organism, n>=3	BAM + GTF	Fast, well-calibrated at n>=3, novel SS support	Junction read imbalance; novel multi-junction events; underdetects RI and microexons
SUPPA2	Quick PSI from existing TPM, pilot analysis	Salmon/kallisto TPM + GTF	No alignment; fastest	Annotation-bound; high FDR (15-30%) at n<=2 vs n<=2
MAJIQ V3	Complex events, heterogeneous cohorts (HET)	BAM + GFF3	Bayesian posterior PSI, complete LSV semantics	High memory (~50+ GB on cohorts); academic license; complex LSV interpretation needs care
leafcutter	Novel junction discovery, sQTL, low-memory environments	BAM (regtools junctions)	Annotation-free; ~400 MB memory; SOTA for unannotated organisms	Sensitive to read depth; cluster topology arbitrary for complex multi-junction events
Shiba (2025)	Low-coverage / few-replicate designs; junction imbalance correction	BAM + GTF	Best calibration in own benchmark at n=2 vs n=2	New (2025); limited community calibration
VAST-TOOLS	Cross-species comparative AS, microexons	FASTQ	VastDB orthology, ExOrthist co-tool	Limited to species in VastDB
Whippet	Laptop-scale exploratory	FASTQ	Fast splice-graph-based PSI	Reduced active development since ~2022; underperforms on complex topologies
IRFinder-S	Intron retention specifically	FASTQ	Coverage + junction integration; CNN-based artifact filtering	IR-only; not for cassette events
S-IRFindeR	Replicate-stable IR ratio	BAM	Stable IR ratio metric	IR-only; less integrated than IRFinder-S

Methodology evolves; verify benchmarks (Olofsson 2023 Brief Bioinform; Kubota 2025 NAR; Tran 2025 WIREs RNA) and tool docs before committing. Default 2026 recommendation: run rMATS-turbo + leafcutter and reconcile; add MAJIQ V3 for complex events / heterogeneous cohorts; switch to Shiba for n=2 vs n=2.

PSI Definition and Effective Length Normalization

For a cassette exon, naive PSI ignores that the inclusion isoform contains more positions where a junction read can map than the skipping isoform. rMATS reports IncFormLen (= 2*(read_length - anchor) for the two flanking junctions, plus exon body bases) and SkipFormLen (= read_length - anchor) and computes:

PSI = (IJC / IncFormLen) / (IJC / IncFormLen + SJC / SkipFormLen)

where IJC = inclusion junction counts, SJC = skipping junction counts. Skipping this normalization biases PSI by ~10-30% depending on read length and exon size. SUPPA2 derives PSI from transcript TPMs, which the upstream Salmon/kallisto already accounts for. leafcutter operates on intron usage proportions within a cluster (a different statistic).

For long-read data, every read carries full isoform identity — effective-length normalization becomes unnecessary because each read counts as one isoform.

Sign Conventions for Alternative Splice Sites

Tool	A5SS interpretation	A3SS interpretation	"Inclusion" direction
rMATS	"long" form = donor downstream of alternative donor	"long" form = acceptor upstream of alternative acceptor	PSI > 0 = more long form
SUPPA2	Same as rMATS (long = inclusion of additional exon body)	Same	PSI > 0 = more long form
VAST-TOOLS	Encoded in event ID (_D1 vs _D2)	Encoded in event ID (_A1 vs _A2)	Document the chosen reference
MAJIQ	Per-junction within LSV; explicit donor/acceptor naming in VOILA	Same	PSI per junction in the LSV

Always record which alternative form ΔPSI > 0 corresponds to in publication-grade reporting. Confusion is the most common reviewer comment for AS papers.

rMATS-turbo Workflow

Goal: Quantify SE/A5SS/A3SS/MXE/RI events from BAMs aligned with STAR 2-pass.

Approach: Group BAMs by condition, run rMATS with --statoff for quantification only, then parse JC.txt files for per-replicate PSI.

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 \
    --statoff

Key flags: --novelSS discovers junctions absent from the GTF (recommended with STAR 2-pass output). --variable-read-length allows mixed read lengths in the cohort. --libType fr-firststrand matches Illumina TruSeq stranded; verify with RSeQC infer_experiment.py. --statoff is for quantification-only runs; omit for differential testing.

import pandas as pd

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

inc_cols = [c for c in se_jc.columns if c.startswith('IncLevel')]
se_jc['mean_PSI'] = se_jc[inc_cols].mean(axis=1)

per_rep_inc = se_jc['IJC_SAMPLE_1'].str.split(',').apply(lambda x: list(map(int, x)))
per_rep_skip = se_jc['SJC_SAMPLE_1'].str.split(',').apply(lambda x: list(map(int, x)))
min_inc = per_rep_inc.apply(min)
min_skip = per_rep_skip.apply(min)

reliable = se_jc[(min_inc + min_skip) >= 20]

JC vs JCEC files

SE.MATS.JC.txt uses only junction-spanning reads. SE.MATS.JCEC.txt adds reads contained within the alternative exon body as inclusion evidence.

• Prefer JC for clean cassette-exon analysis when reads spanning junctions are sufficient.
• Use JCEC when alternative exons are short (<50nt) and junction-spanning reads are scarce.
• Avoid JCEC when intron retention overlaps the alternative exon body — exon-body reads may come from retained introns, not inclusion isoform.

SUPPA2 Workflow

Goal: Compute event PSI from transcript TPM without alignment; useful when Salmon/kallisto TPMs already exist.

Approach: Generate IOE event definitions from GTF, then aggregate TPMs of transcripts including/excluding each event.

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

for ev in SE A5 A3 MX RI; do
    suppa.py psiPerEvent -i events_${ev}_strict.ioe -e transcript_tpm.tsv -o psi_${ev}
done

SUPPA2 is annotation-bound: events absent from the GTF cannot be quantified. Whether an event is detected depends entirely on which transcripts the upstream Salmon/kallisto index contains. Use GENCODE comprehensive over basic when SUPPA2 detection sensitivity matters.

MAJIQ V3 Workflow

Goal: Quantify LSVs with Bayesian posterior PSI distributions; ideal for complex multi-junction events that don't fit canonical event types.

Approach: Build a splice graph from BAMs + GFF3, compute per-junction coverage with bootstrap, then run majiq psi for posterior PSI per LSV.

majiq build annotation.gff3 -c settings.ini -j 8 -o build_output
majiq psi build_output/sample1.majiq build_output/sample2.majiq -j 4 -o psi_output -n condition_psi
voila view -p 5000 -j 8 build_output/splicegraph.zarr psi_output/condition_psi.psi.voila -o voila_output

MAJIQ V3 (Aicher, Slaff, Jewell, Barash bioRxiv 2024; public release 2025) replaced V2's SQLite splicegraph (splicegraph.sql) with Zarr storage (splicegraph.zarr); the .sql is deprecated. V3 is ~3.2x faster than V2 via xarray/zarr/Dask parallelization. LSV output includes posterior mean PSI plus the full posterior distribution; this enables threshold-based testing (e.g. P(|ΔPSI| > 0.2)) rather than frequentist p-values.

leafcutter Junction Quantification

Goal: Detect junctions and intron clusters annotation-free for downstream cluster-level usage.

Approach: Extract junctions per BAM with regtools, write filenames into a list, then cluster introns sharing splice sites.

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

-a 8 = 8nt anchor minimum (raise to 12 for stricter; lower to 6 for microexon-friendly). -m 50 = minimum junction reads per cluster. -l 500000 = max intron length (relevant for long brain-gene introns; raise for genes like DSCAM, ROBO2, ANK3).

Per-Tool Failure Modes

rMATS-turbo: Junction Read Imbalance

Trigger: A cassette exon's flanking exons have unequal read mapping opportunity (very short upstream exon, repeat-overlapping flanks, or low-mappability regions).

Mechanism: rMATS' binomial model treats inclusion vs skipping junctions as having equal mappability. When mappability differs between the two junction types, the PSI estimate is biased.

Symptom: "Significant" rMATS calls with no concordant change in leafcutter or MAJIQ at the same locus; ΔPSI direction inconsistent with sashimi-plot intuition.

Fix: Run Shiba (Kubota 2025 NAR) which corrects junction-imbalance, or filter rMATS hits requiring concordant detection in leafcutter.

SUPPA2: Sparse Empirical Null at Low Replicate Count

Trigger: n=2 vs n=2 (or n=3 vs n=2) design with --method empirical.

Mechanism: SUPPA2's empirical null is constructed from between-replicate ΔPSI distributions binned by transcript expression. With few replicates, the binned null is sparse and conservative-looking but actually under-calibrated.

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

Fix: Switch to leafcutter or Shiba for n<=3 designs; or use --method classical (Wilcoxon) for very low replicate count; reconcile against orthogonal tool.

MAJIQ V3: Complex LSV Interpretation

Trigger: A gene with 4+ alternative splice sites at one node (e.g. one source, multiple acceptors).

Mechanism: A complete LSV at a single node lists all observed junctions; PSI is per-junction within the LSV, not "PSI of one event."

Symptom: Reporting "PSI of the gene" doesn't make sense; per-junction PSIs sum to 1 across the LSV but no single number represents the gene.

Fix: Use VOILA to visualize the LSV graph and identify which junction(s) shifted; for cassette-style reporting, derive equivalent PSI from sum of inclusion-junctions / total junctions in the LSV.

leafcutter: Cluster Topology Arbitrariness

Trigger: A cluster has 4+ introns sharing splice sites with non-canonical topology (e.g. mixed cassette + alternative donor + IR).

Mechanism: leafcutter clusters introns by shared splice sites; complex topologies don't map onto SE/A5SS/A3SS taxonomy and cluster-level "ΔPSI" hides which intron drove the change.

Symptom: Significant cluster-level p-value but multiple introns showing different effect-size directions.

Fix: Inspect the cluster in leafviz; report per-intron effect sizes (effect_sizes.txt); for canonical event reporting, map to SE/A5SS/A3SS via flanking exon coordinates manually.

Reconciliation: When rMATS and leafcutter 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 read imbalance OR rMATS event hits an annotation that leafcutter clustered differently	Inspect locus in IGV; check Shiba
leafcutter sig, rMATS not sig	Novel junction not in rMATS annotation; rMATS --novelSS may have missed it	Check --novelSS was on; rerun if not
Both sig, opposite ΔPSI direction	Event class mismatch (e.g. rMATS calls SE positive but 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

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

Intron Retention: Canonical vs Detained vs Co-Transcriptional Unspliced

Three biologically distinct states all called "IR" by generic tools:

1. Canonical RI (cytoplasmic, NMD-substrate often): mature polyadenylated mRNA carries the intron; usually PTC-bearing and NMD-targeted, sometimes encoding an alternative protein.
2. Detained intron (DI) (Boutz 2015 Genes Dev): nuclear-localized, mature transcripts retaining a specific intron; a regulated reservoir released into translation upon signaling.
3. Co-transcriptional unspliced: nascent pre-mRNA captured before splicing complete; not a regulated state.

Library prep determines which state(s) are visible:

• Poly(A) selection: enriches (1), depletes (2)/(3)
• rRNA depletion (cytoplasmic): captures (1)
• rRNA depletion (whole cell or nuclear): captures all three

To distinguish DI from canonical RI: subcellular fractionation (nuclear vs cytoplasmic RNA-seq), or NMD inhibitor (cycloheximide, NMDi-14) treatment — canonical RI mRNA increases under NMD inhibition; DI does not.

IRFinder -m FullAuto -r REF/ -d ir_output sample.fastq.gz

IRFinder-S (Lorenzi 2021 Genome Biol) uses CNN-based filtering of true IR vs noise; current SOTA for IR analysis. iREAD and S-IRFindeR (Broseus & Ritchie 2020 bioRxiv) are alternatives.

Microexon Detection

Microexons (3-27 nt, neural-enriched, SRRM4-regulated; Irimia 2014 Cell) are missed by default short-read aligners requiring 20-30 nt anchors. Options:

Approach	Tool	Notes
Curated database lookup	VAST-TOOLS + VastDB	Cross-species, microexon-aware (Tapial 2017 Genome Res)
De novo discovery	MicroExonator (Parada 2021 Genome Biol)	Snakemake pipeline
Tune the upstream aligner	STAR --alignSJoverhangMin 6 --alignSJDBoverhangMin 1 --outFilterMismatchNoverReadLmax 0.04	rMATS itself cannot recover microexons that STAR didn't pass through; lower DB-junction overhang to 1 (trusts annotated microexon coords) and combine with strict mismatch filter. Typical AS pipelines use STAR 8/3 which is too strict for microexons
Long-read sequencing	PacBio Iso-Seq, ONT	Solves the problem entirely; reads span microexons fully

For brain / neural tissue or autism-spectrum studies, microexon analysis must be explicit — default short-read pipelines underdetect them by ~70%.

Quality Thresholds

Metric	Threshold	Source / Rationale
Junction reads per replicate	>=10-20 (per-replicate minimum)	Empirical PSI variance becomes <0.05 above this; below, PSI becomes a coin flip
PSI dynamic range	mean PSI 0.05-0.95	Outside is near-constitutive; rMATS, SUPPA2 default filters drop these
Missing values	<50% of samples	Higher missingness indicates low expression — re-test with subset
Read length	>=75nt PE preferred; >=100nt for microexons	50nt SE biases toward shorter exons (Wang 2008 Nature)
Library	rRNA depletion for IR analysis; poly(A) acceptable for cassette	Sims 2014 Genome Res; poly(A) loses pre-mRNA
STAR 2-pass	Cohort-style preferred over per-sample basic	Veeneman 2016 Bioinformatics: >=94% novel junction recovery
MAJIQ minreads / minpos	--minreads 10 --minpos 3	Default; lower for low-coverage
leafcutter -m	50 reads per cluster	Higher for rare events; lower for sQTL discovery
Anchor length	>=8 nt for short-read	Below this, false-positive junctions dominate (CIGAR-N noise)

Decision Tree by Scenario

Scenario	Recommended tool(s)	Why
Standard cassette analysis, n>=3, GENCODE-annotated	rMATS-turbo + leafcutter (concordance)	Default workflow; complementary algorithmic families
Non-model organism, no GENCODE-grade annotation	leafcutter + de novo discovery	Annotation-free
Heterogeneous cohort, n>=10 vs n>=10 (clinical, GTEx-style)	MAJIQ V3 with HET module	HET designed for between-sample variability dominance
Low coverage / few replicates (n=2 vs n=2)	Shiba	Junction-imbalance correction; SOTA at low coverage in 2025 benchmarks
Cross-species comparative (vertebrate panel)	VAST-TOOLS + VastDB	Orthology-aware events; ExOrthist co-tool
TPM-only available (no BAMs)	SUPPA2	Annotation-bound but fast
Microexon focus (neural / ASD)	VAST-TOOLS or MicroExonator	Default tools systematically miss microexons
Intron retention focus	IRFinder-S (rRNA-depleted library)	Coverage-aware; CNN artifact filter
Detained introns specifically	IRFinder-S + nuclear/cytoplasmic fractionation	Required to separate DI from cytoplasmic RI
Long reads available	rMATS-long, FLAIR, IsoQuant	Full-isoform resolution; see long-read-splicing
Single-cell (full-length plate)	MARVEL, BRIE2	See single-cell-splicing
Single-cell (10X 3')	Likely don't attempt; consider Sierra for APA	10X 3' chemistry insufficient for AS

Common Errors

Error	Cause	Solution
error: GTF gene_id parsing (rMATS)	rMATS expects GENCODE-style gene_id; some Ensembl GTFs use different attribute order	gffread input.gff3 -T -o standardized.gtf
KeyError: 'IJC_SAMPLE_1' (rMATS parsing)	Output column missing; sometimes occurs when --statoff combined with novel events on older versions	Update rMATS-turbo to >=4.3.x; re-run
MAJIQ: too few reads at junction	Default --minreads 10 --minpos 3 filters out the locus	Lower thresholds for low-coverage data; document filtering
leafcutter: dispersion estimation failed	Cluster has all-zero counts in one group	Pre-filter clusters with --min-samps-feature-prop 3
SUPPA2: empirical p computed on N=4 nulls	Insufficient replicates for empirical mode	Switch --method classical (Wilcoxon) for very low replicate count
regtools: invalid CIGAR	Non-BAM-spec read in input	Filter with samtools view -h -F 0x100 -F 0x800 (drop secondary/supplementary)
STAR: too many SJs (in pass 2)	Cohort SJ.out.tab too large	Filter to junctions seen in >=3 samples or with >=3 unique reads before merging

Output Interpretation

PSI ranges 0 to 1: 1 = always included, 0 = always skipped, 0.5 = balanced. Sign of IncLevelDifference matches --b1 minus --b2 group order — always document which is which in publications.

NMD direction matters: an increase in PSI of a poison exon (PTC-introducing) decreases functional protein due to NMD. Always check whether the alternative form is PTC-bearing using ORF-aware annotation (IsoformSwitchAnalyzeR consequences, or manual stop-codon distance check vs last exon-exon junction).

Disease signatures:

• SF3B1 mutations (MDS, CLL, uveal melanoma): cryptic 3'ss ~10-30 nt upstream of canonical (Darman 2015 Cell Rep). Look for clustered A3SS hits.
• U2AF1 mutations (lung adeno, MDS): altered preferences at 3'ss -3 position; cassette-exon shifts.
• TDP-43 loss (ALS/FTD): de novo cryptic exons in UNC13A, STMN2, ATG4B (Brown 2022 Nature; Klim 2019 Nat Neurosci) — annotation-free tools required (leafcutter denovo).

Common Pitfalls

• Treating AFE/ALE as splicing — these are typically promoter-driven (AFE) or APA-driven (ALE), not spliceosomal. Confirm with FANTOM CAGE or 3'-end-seq.
• Confusing detained introns with NMD-targeted RI — both call as "IR" but have opposite biological fates.
• Using poly(A) libraries for IR analysis — biases toward mature transcripts, depletes pre-mRNA.
• Single-end short reads — junction-spanning reads need >=8nt overhang on both sides; biases toward shorter exons.
• Quoting "PSI of the gene" from MAJIQ LSV output — only per-junction PSI within an LSV is meaningful.
• Skipping STAR 2-pass — loses ~14% of novel junctions; matters for any non-canonical organism or condition.
• Trusting rMATS calls without --novelSS when STAR 2-pass found new junctions — rMATS will only quantify pre-annotated events.

Related Skills

• differential-splicing - Compare PSI between conditions; use the same upstream alignment but switch to with-stat tools
• splicing-qc - Run BEFORE quantification to verify library, depth, strandedness, alignment quality
• isoform-switching - DTU framework with NMD/ORF/domain consequences; complementary to event-level PSI
• sashimi-plots - Visualize specific events for QC and reporting; concordance check across tools
• splice-variant-prediction - SpliceAI/Pangolin for variant impact predictions to test against PSI changes
• long-read-splicing - Full-isoform PSI without anchor-length limits; preferred for microexons and complex isoforms
• read-alignment/star-alignment - STAR 2-pass cohort-style alignment is required upstream
• rna-quantification/alignment-free-quant - Salmon/kallisto TPM is required for SUPPA2

References

• Wang et al 2008 Nature - AS event taxonomy
• Vaquero-Garcia et al 2016 eLife - MAJIQ LSV framework
• Trincado et al 2018 Genome Biol - SUPPA2
• Li et al 2018 Nat Genet - leafcutter
• Tapial et al 2017 Genome Res - VAST-TOOLS / VastDB
• Wang et al 2024 Nat Protoc - rMATS-turbo
• Aicher, Slaff, Jewell, Barash 2024 bioRxiv - MAJIQ V3
• Kubota et al 2025 NAR - Shiba
• Lorenzi et al 2021 Genome Biol - IRFinder-S
• Boutz et al 2015 Genes Dev - detained introns
• Irimia et al 2014 Cell - SRRM4 microexons
• Darman et al 2015 Cell Rep - SF3B1 cryptic 3'ss
• Olofsson et al 2023 Brief Bioinform - benchmark
• Tran et al 2025 WIREs RNA - methodology review
• Brown et al 2022 Nature - UNC13A cryptic exon (TDP-43)
• Klim et al 2019 Nat Neurosci - STMN2 cryptic splicing
• Veeneman et al 2016 Bioinformatics - STAR 2-pass benchmark