splice-variant-prediction

## Version Compatibility Reference examples tested with: SpliceAI 1.3+, Pangolin 1.0+, MMSplice 2.4+, pyensembl 2.3+, pysam 0.22+, pandas 2.2+, gffutils 0.13+, tensorflow 2.15+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - 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. # Splice Variant Prediction Predict whether a DNA variant alters mRNA splicing. **Distinct from "variant pathogenicity" generally**: a variant can be a strong splice disruptor without being pathogenic for the gene's standard mechanism, or pathogenic for reasons orthogonal to splicing. Splice prediction asks specifically: does this variant change splice-site usage? ## Predictor Taxonomy | Family | Architecture | Output | Fails when | |--------|--------------|--------|------------| | Context-aware CNN | 10 kb dilated ResNet | Per-position donor/acceptor probability | Long-range (>5 kb) regulatory effects; tissue-specific events | | Tissue-aware CNN/transformer | Same arch + multi-tissue training | Per-tissue ΔPSI | Tissue not in training set; novel cell types | | Modular per-region CNN | Separate sub-models for 5'ss/3'ss/exon/intron | Calibrated quantitative ΔPSI | Atypical events; complex multi-junction effects | | Foundation transformer | Pretrained on broad genomic context | Splice probability or ΔPSI | New tools; less battle-tested | | Empirical lookup | Public RNA-seq event database | Top-N most likely mis-splicing outcomes | Variant types not represented in training cohorts | | Composite score | Blend of multiple predictors | Single scaled score | When component predictors disagree internally | ## Tool Selection Matrix | Tool | Best for | Output | When to use | Fails when | |------|----------|--------|-------------|------------| | SpliceAI | Clinical screening; canonical splice site disruption | Delta score 0-1 | Default for ACMG variant classification | Tissue-specific events; deep-intronic with default 50nt window | | Pangolin | Tissue-aware predictions | Per-tissue ΔPSI | When disease tissue is known (brain, heart, liver, testis) | Tissue not in 4-tissue training set | | MMSplice | Quantitative ΔPSI | Δlogit_psi | Research where calibrated effect-size matters | Atypical events outside cassette-exon model | | SpliceTransformer | 2024+ benchmark improvements | Tissue-specific ΔPSI | When transformer foundation models outperform CNN on benchmark variant sets | New (2024); limited clinical adoption | | TrASPr | Multi-transformer, 2024-2025 | Tissue-specific PSI/ΔPSI | Strong on tissue-specific test sets | New; verify before clinical use | | SpliceVault | Empirical mis-splicing outcome | Top-N events at the affected splice site | Predicting consequence (skip vs cryptic) of canonical-disrupting variants | Variants not represented in 300K-RNA training | | CADD-Splice | Single composite score | Scaled C-score | Clinical pipelines wanting one number | When knowing which sub-component drove the score is needed | Methodology evolves; verify benchmarks (Strawn 2025 *bioRxiv*; You et al 2024 *Nat Commun*) and ClinGen SVI splicing recommendations before reporting clinical interpretations. Concordance across SpliceAI + Pangolin + MMSplice is gold-standard evidence; discordance flags need RNA validation. ## Decision Tree by Use Case | Use case | Recommended approach | |----------|----------------------| | Clinical variant report (single variant, ACMG classification) | SpliceAI default 50nt + ClinGen SVI 2023 thresholds | | Tissue-specific clinical question (brain disease, cardiomyopathy) | SpliceAI + Pangolin (tissue-matched) | | Unsolved Mendelian case (suspect deep-intronic) | SpliceAI extended window (-D 500-2000) + SpliceVault | | VUS panel screening | SpliceAI + Pangolin + MMSplice concordance scoring | | Predict consequence of canonical-disrupting variant | SpliceVault top-N empirical events | | Branchpoint variant suspected | BPHunter (branchpoint screen) — SpliceAI is weak here | | Splice-switching ASO design (target ESE/ESS occlusion) | SpliceAI on masked sequence + RNAfold accessibility | | Validate predicted splice change in patient | RNA-seq + FRASER2 (see outlier-splicing-detection) | | Pseudoexon prediction in deep intron | SpliceAI extended window + CI-SpliceAI; require RNA validation | ## ClinGen SVI 2023 Framework The ClinGen Sequence Variant Interpretation (SVI) splicing subgroup (Walker 2023 *Am J Hum Genet*) extended the ACMG/AMP 2015 framework with explicit splice-prediction rules. | Evidence code | Threshold | Notes | |----------------|-----------|-------| | **PP3** (supporting pathogenic) | SpliceAI delta >= 0.20 | Computational evidence supporting pathogenicity | | **PP3 moderate** | SpliceAI delta >= 0.50 | Or concordance across multiple predictors | | **PP3 strong** | SpliceAI delta >= 0.80 | Typically requires concordance + canonical site | | **BP4** (supporting benign) | SpliceAI delta <= 0.10 | Computational evidence against pathogenicity | | **PVS1** (very strong null) | Canonical +/-1, +/-2 site disruption with predicted LoF + NMD | Requires gene where LoF is established mechanism (Abou Tayoun 2018 *Hum Mutat* PVS1 decision tree) | | **PS3 / BS3** (functional) | RNA evidence (RT-PCR, RNA-seq, minigene) | Supersedes computational evidence | **Operational rules:** Computational evidence (PP3/BP4) is *supporting*, not standalone. Splicing variants benefit from concordance across SpliceAI + Pangolin + MMSplice. RNA validation supersedes prediction. Always log SpliceAI version, distance window, and reference transcript. SpliceAI alone is **not sufficient** for PVS1; canonical site disruption requires gene-level LoF context. ## SpliceAI Workflow **Goal:** Annotate VCF variants with per-variant delta scores for splice-site change. **Approach:** Run `spliceai` CLI with reference genome and annotation; parse INFO field for delta scores. **SpliceAI is human-only** (`-A grch37` or `-A grch38`); the model was trained on GENCODE human and does not directly transfer to mouse, fly, or other species. For mouse, retrained variants exist (e.g. mouseSpliceAI); for other species, use Pangolin (4 species: human, mouse, rat, rhesus macaque) or accept that prediction will be unreliable. ```bash spliceai \ -I input.vcf \ -O output.vcf \ -R GRCh38.primary_assembly.genome.fa \ -A grch38 \ -D 50 \ -M 0 ``` `-D 50` = distance window in nt around variant (default 50). For deep-intronic variants suspected of creating pseudoexons, raise to **500-2000**: ```bash spliceai -I input.vcf -O output_extended.vcf -R genome.fa -A grch38 -D 500 -M 1 ``` `-M 0` (default) returns raw scores; `-M 1` masks splice gains at annotated sites and losses at unannotated sites (cleaner for clinical use). Output INFO format: `SpliceAI=ALLELE|SYMBOL|DS_AG|DS_AL|DS_DG|DS_DL|DP_AG|DP_AL|DP_DG|DP_DL`. Delta score = max(DS_AG, DS_AL, DS_DG, DS_DL). ```python import pandas as pd import re def parse_spliceai_vcf(vcf_path): rows = [] with open(vcf_path) as f: for line in f: if line.startswith('#'): continue fields = line.strip().split('\t') info = fields[7] m = re.search(r'SpliceAI=([^;]+)', info) if not m: continue for ann in m.group(1).split(','): parts = ann.split('|') allele, symbol = parts[0], parts[1] ds = [float(p) if p != '.' else 0 for p in parts[2:6]] dp = parts[6:10] rows.append({ 'chrom': fields[0], 'pos': int(fields[1]), 'ref': fields[3], 'alt': allele, 'gene': symbol, 'DS_AG': ds[0], 'DS_AL': ds[1], 'DS_DG': ds[2], 'DS_DL': ds[3], 'delta_max': max(ds), }) return pd.DataFrame(rows) df = parse_spliceai_vcf('output.vcf') df['acmg_evidence'] = pd.cut( df['delta_max'], bins=[-0.01, 0.10, 0.20, 0.50, 0.80, 1.01], labels=['BP4', 'inconclusive', 'PP3_supporting', 'PP3_moderate', 'PP3_strong'] ) ``` DS labels: AG = acceptor gain, AL = acceptor loss, DG = donor gain, DL = donor loss. ## Pangolin for Tissue-Specific Prediction **Goal:** Get tissue-specific splice impact predictions when disease tissue is known. **Approach:** Run Pangolin CLI with VCF + reference + gffutils annotation database. ```bash python -c "import gffutils; gffutils.create_db('gencode.v45.annotation.gff3', 'gencode.db', force=True)" pangolin \ input.vcf \ GRCh38.primary_assembly.genome.fa \ gencode.db \ pangolin_output \ -d 500 \ -m True \ -s 0.2 ``` `-m True` masks splice gains at annotated sites and losses at unannotated sites (recommended for clinical use). `-s 0.2` outputs all sites with predicted change >= cutoff. Pangolin output is a VCF with per-tissue predictions across the **4 tissues used at training: brain, heart, liver, testis** (Zeng & Li 2022 *Genome Biol*). The model outputs per-species per-tissue predictions but extrapolates poorly to tissues outside this set. Use the tissue closest to disease-relevant context. **For tissues not in the 4-tissue training set, fall back to SpliceAI** — Pangolin extrapolates poorly to unseen tissues. ## SpliceVault for Empirical Mis-Splicing Outcomes **Goal:** Predict the *type* of mis-splicing (exon skipping vs cryptic site activation) given a canonical-disrupting variant. **Approach:** Query SpliceVault's database of empirical mis-splicing events from public RNA-seq. ```python import requests # Web API: https://kidsneuro.shinyapps.io/splicevault/ # Or use the R/Python package at github.com/kidsneuro-lab/SpliceVault # Example: NM_000546.6:c.673-2A>G (TP53) # Returns top-N most likely mis-splicing events: exon skipping, cryptic 3'ss usage, etc. ``` SpliceVault (Dawes 2023 *Nat Genet*) showed that the **Top-4 events** at any splice site explain >95% of empirical mis-splicing — a striking regularity that makes consequence prediction tractable. Use SpliceVault when the question is not "will splicing change?" but "what specific aberrant splicing will occur?". ## MMSplice for Calibrated ΔPSI **Goal:** Predict quantitative ΔPSI (not just probability of disruption) for cassette exons. **Approach:** Score variant impact on each splicing region (5'ss, 3'ss, exon, intron-3'/5') and combine. ```python from mmsplice.vcf_dataloader import SplicingVCFDataloader from mmsplice import MMSplice, predict_save dl = SplicingVCFDataloader( gtf='gencode.v45.basic.gtf', fasta_file='GRCh38.fa', vcf_file='input.vcf' ) model = MMSplice() predict_save(model, dl, 'mmsplice_predictions.csv', pathogenicity=True) ``` MMSplice (Cheng 2019 *Genome Biol*) reports Δlogit_psi per variant. Useful when calibrated effect sizes matter (research) more than probability of disruption (clinical screening). Companion **MTSplice** (Cheng 2021 *Genome Biol*) adds tissue-specific Δψ predictions. ## HGVS Splicing Nomenclature Following den Dunnen 2016 *Hum Mutat*: | Notation | Meaning | |----------|---------| | `c.123+1G>A` | +1 of intron downstream of exon ending at cDNA position 123 (canonical 5'ss G) | | `c.123+5G>A` | +5 position of donor (consensus region) | | `c.124-1G>A` | -1 of acceptor (canonical AG) | | `c.124-3T>G` | -3 of acceptor (Py-tract / BPS region) | | `c.124-50A>G` | Deep-intronic; may activate cryptic site | | `r.123_456del` | RNA-level deletion (predicted exon skipping) | | `r.spl?` | Unknown splice consequence | | `r.0?` | No detectable RNA | | `p.0?` | Unknown protein consequence | | `p.(=)` | No predicted protein change (silent) | Validation tools: VariantValidator (Freeman 2018 *Hum Mutat*), Mutalyzer 3 (Lefter 2021 *Hum Mutat*). ## Extended-Window Scoring for Deep-Intronic Variants SpliceAI's default precomputed scores use a **50-nt window**, missing variants that create pseudoexons in deep intronic regions. For unsolved Mendelian cases: ```bash # Recompute with extended window spliceai -I input.vcf -O output_2kb.vcf -R genome.fa -A grch38 -D 2000 # Or use CI-SpliceAI (Strauch 2022 Bioinformatics) optimized for distal effects ``` | Window | Tradeoff | |--------|----------| | -D 50 (default) | Fast; captures canonical-site disruption; misses deep-intronic | | -D 500 | Captures most pseudoexon-creating deep-intronic variants | | -D 2000 | Maximum sensitivity; some false positives at large distances | Pseudoexon creation in deep introns explains a substantial fraction of unsolved Mendelian disease alleles in current cohorts (estimates 5-15% across studies; specific quantitative range will vary by cohort and panel — verify against current literature). Disease examples: CFTR 3849+10kbC>T, USH2A c.7595-2144A>G, CEP290 c.2991+1655A>G (LCA10). ## Concordance Across Predictors ```python import pandas as pd merged = (spliceai_df .merge(pangolin_df, on=['chrom', 'pos', 'alt'], suffixes=('_sai', '_pang')) .merge(mmsplice_df, on=['chrom', 'pos', 'alt']) ) merged['concordance'] = ( (merged['delta_max_sai'] >= 0.2).astype(int) + (merged['pangolin_score'].abs() >= 0.2).astype(int) + (merged['delta_logit_psi'].abs() >= 1.0).astype(int) ) merged['interpretation'] = merged['concordance'].map({ 0: 'concordant_benign', 1: 'discordant_low_evidence', 2: 'concordant_evidence', 3: 'high_concordance_pathogenic' }) ``` | Concordance | Interpretation | Action | |-------------|----------------|--------| | 3/3 above threshold | High confidence | Report PP3 strong | | 2/3 above | Concordant evidence | Report PP3 moderate | | 1/3 above | Discordant | Report inconclusive; flag for RNA validation | | 0/3 above | Concordant benign | BP4 supporting | Discordance is the most informative pattern — variants where one model sees impact and others don't are high priority for RNA validation.