GPTomics/bio-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 (Smith & Kitzman 2023 Genome Biol 24:294; 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 | ClinGen SVI: apply at supporting weight (not standalone) | | BP4 (supporting benign) | SpliceAI delta <= 0.10 | ClinGen SVI: apply at supporting weight | | 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. ClinGen SVI 2023 recommends applying predictive splice PP3/BP4 at supporting weight only; higher SpliceAI cutoffs (0.5, 0.8) increase precision but are the tool's own tiers (Jaganathan 2019), NOT ClinGen-endorsed evidence-strength upgrades — reaching moderate/strong requires functional/RNA evidence (PS3/BS3), not a higher SpliceAI score alone. 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.

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:

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).

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],
    # ClinGen SVI applies splice PP3/BP4 at supporting weight; 0.5/0.8 are SpliceAI
    # precision tiers (Jaganathan 2019), NOT ACMG evidence-strength upgrades
    labels=['BP4', 'inconclusive', 'PP3_supporting', 'PP3_supporting_prec0.5', 'PP3_supporting_prec0.8']
)

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.

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.

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 predict variant-associated mis-splicing with ~92% sensitivity overall (96% of exon-skipping and 86% of cryptic-activation events) — 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.

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 2 (Lefter et al 2021 Bioinformatics 37:2811-2817).

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:

# 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 PLoS One), SpliceAI retrained on curated GENCODE splice sites

| 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

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 | PP3 (supporting); strong candidate for RNA validation (PS3) | | 2/3 above | Concordant evidence | PP3 (supporting) | | 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.

Branchpoint Variant Detection

All current tools are weak at branchpoint variants because the BPS motif (yUnAy) has low information content. Specific branchpoint tools:

| Tool | Method | Notes | |------|--------|-------| | BPP | Mixture model (BP motif + polypyrimidine tract) | Zhang 2017 Bioinformatics 33:3166 | | LaBranchoR | Bidirectional LSTM | Paggi & Bejerano 2018 RNA 24:1647 | | SVM-BPfinder | SVM on conservation+sequence | Corvelo 2010 PLoS Comput Biol | | BPHunter | Genome-wide branchpoint screen against an aggregated experimental (lariat/RNA-seq) + computational BP database | Zhang 2022 PNAS |

Branchpoint variants are under-recognized in clinical pipelines; SpliceAI captures only some because branchpoint motifs have low information content. Recommendation: when SpliceAI delta is borderline (0.1-0.3) for a variant in the BPS region (-18 to -40 from 3'ss), run BPHunter as supplement.

Splice-Switching ASO Design

Goal: Design antisense oligonucleotides to modulate splicing therapeutically (e.g. SMA ISS-N1, DMD exon skipping).

Approach: Use SpliceAI to predict impact of binding-site occlusion; check accessibility (RNAfold); avoid SR/hnRNP off-target motifs.

# Conceptual workflow - actual design uses ASO synthesis platforms
# 1. Identify target ESE/ESS/ISE/ISS region from MaxEntScan + SpliceAI scan
# 2. Design candidate 18-22 nt ASOs spanning the regulatory element
# 3. For each ASO, simulate splice-site occlusion impact via SpliceAI on the masked sequence
# 4. Filter for RNA accessibility (avoid stable hairpins) using RNAfold
# 5. Whole-transcriptome SpliceAI scan for off-target binding (>=17/20 nt match)
# 6. Avoid TLR9 immunostimulatory CpG motifs

# Chemistry choices:
# - 2'-MOE-PS: nusinersen-like (CNS, intrathecal)
# - PMO: DMD ASOs (systemic IV)
# - GalNAc-conjugated: hepatic targeting

Approved precedents: nusinersen (SMA ISS-N1 occlusion, exon 7 inclusion); risdiplam (small-molecule SMN2 splicing modulator); eteplirsen/golodirsen/casimersen/viltolarsen (DMD exon skipping). Design references: Hua 2008 AJHG; Roberts et al 2023 Nat Rev Drug Discov 22:917 (DMD therapeutic approaches).

Per-Tool Failure Modes

SpliceAI: 50nt Window Limitation

Trigger: Variant deep in an intron (>50 nt from canonical splice site).

Mechanism: Default precomputed scores use ±50 nt window; the model is trained on this context but pre-stored scores limit lookups.

Symptom: Known pathogenic deep-intronic variant scores low (<0.2); no pseudoexon detected.

Fix: Re-run with -D 500 or -D 2000; or try CI-SpliceAI (SpliceAI retrained on curated GENCODE splice sites) as a second predictor.

SpliceAI: Tissue Agnosticism

Trigger: Variant in a tissue-specific gene (NEFM in neurons, MAPT brain, DMD muscle isoforms).

Mechanism: SpliceAI is trained on aggregate GENCODE annotation; tissue-specific events with weak constitutive use score low.

Symptom: Tissue-specific pathogenic variant has low SpliceAI delta; functional impact still observed in target tissue.

Fix: Use Pangolin for tissue-aware prediction; or SpliceTransformer; require RNA validation in disease-relevant tissue.

Pangolin: Out-of-Training Tissue

Trigger: Disease tissue not represented in Pangolin's 4-species, 4-tissue (Cardoso-Moreira 2019 developmental) training set.

Mechanism: Pangolin extrapolates poorly to tissues outside training distribution.

Symptom: Pangolin score uncalibrated for queried tissue; doesn't agree with patient RNA-seq from that tissue.

Fix: Fall back to SpliceAI for tissues not in Pangolin training; or run patient RNA-seq directly.

MMSplice: Atypical Events

Trigger: Variant affecting a non-cassette event (MXE, complex multi-junction, AFE/ALE).

Mechanism: MMSplice modular model is trained primarily on cassette exon events.

Symptom: MMSplice ΔPSI doesn't match other predictors or empirical data for non-cassette events.

Fix: Use SpliceAI for non-cassette events; restrict MMSplice to cassette exon contexts.

CADD-Splice: Loss of Component Information

Trigger: Wanting to know which sub-component drove a high CADD-Splice score.

Mechanism: CADD-Splice combines SpliceAI + MMSplice + CADD into a single C-score; sub-component contributions are abstracted.

Symptom: "High CADD-Splice score but unclear why."

Fix: Run SpliceAI and MMSplice separately to see which contributed.

Branchpoint Variants: Low Information Motif

Trigger: Variant in the BPS region (-18 to -40 from 3'ss).

Mechanism: BPS motif (yUnAy) has low information content; CNNs struggle to learn the consensus.

Symptom: Confirmed BPS variant scores SpliceAI delta <0.2 despite functional disruption.

Fix: Use BPHunter (Zhang 2022 PNAS) for genome-wide branchpoint screening; require RNA validation.

Population Database Lookup

| Database | Use for | |----------|---------| | gnomAD v4 | Allele frequency; SpliceAI annotations integrated | | ClinVar | Existing classifications; SpliceAI integrated since 2020 | | SpliceVarDB | Curated splice variants with experimental RNA validation | | dbNSFP4 | Pre-computed splice scores aggregated | | Recount3 | Tissue-specific PSI lookups from public RNA-seq | | GTEx sQTL v8 | Tissue-specific splicing QTLs across 49 tissues | | MaveDB | Splice MAVE results (e.g. BRCA1 saturation; Findlay 2018 Nature) |

Always check ClinVar first for existing classifications; cross-reference with gnomAD for population frequency before committing to PP3/PP4.

Common Errors

| Error | Cause | Solution | |-------|-------|----------| | spliceai: tensorflow not found | TensorFlow not installed | pip install tensorflow>=2.0 separately | | spliceai: chrom not in reference | VCF chrom name mismatch (chr1 vs 1) | bcftools annotate --rename-chrs chr_map.txt | | pangolin: no annotations found for variant | gffutils db doesn't contain queried gene | Rebuild gffutils db with comprehensive GENCODE GFF3 | | mmsplice: variant outside any cassette event | MMSplice model assumes cassette context | Use SpliceAI for non-cassette events | | SpliceVault: variant not found | Variant outside common splice sites in 300K-RNA database | Use SpliceAI for prediction (no empirical baseline available) | | VariantValidator: invalid HGVS | Wrong reference transcript or build | Specify NM_. version explicitly |

Common Pitfalls

• Using SpliceAI score alone for clinical reporting — must combine with concordant predictors and ideally RNA validation; ClinGen SVI requires this for non-canonical positions.
• 50nt window for deep intronic variants — pseudoexon-creating variants 100-2000 nt deep are systematically missed.
• Tissue-agnostic prediction for tissue-specific genes — use Pangolin or SpliceTransformer when tissue context matters (NEFM, MAPT, DMD isoforms).
• Branchpoint variants — all current predictors are weak here. Use BPHunter for branchpoint screening.
• Forgetting NMD direction — confirmed splice disruption needs NMD-status check. Last-exon PTCs escape NMD and can be dominant-negative or gain-of-function.
• In-silico-only PVS1 application — PVS1 for non-canonical positions requires functional or strong computational evidence; SpliceAI alone is supporting (PP3), not very strong.
• Trusting LLMs for variant interpretation — use as orchestrators on top of SpliceAI/VariantValidator/ClinVar; all clinical-grade calls require human expert sign-off.
• Skipping HGVS validation — invalid HGVS leads to silent reference-transcript mismatches; always run VariantValidator first.

Quality Thresholds

| Metric | Recommendation | Source | |--------|----------------|--------| | Default SpliceAI window | -D 50 (clinical screening) | Jaganathan 2019 | | Deep-intronic SpliceAI window | -D 500-2000 (unsolved Mendelian) | Convention (verify current literature) | | ACMG PP3 (supporting) | SpliceAI delta >= 0.2 | Walker 2023 AJHG (apply at supporting weight) | | ACMG BP4 (supporting) | SpliceAI delta <= 0.1 | Walker 2023 AJHG | | SpliceAI higher-precision cutoffs | 0.5 / 0.8 raise precision, NOT ACMG strength | Jaganathan 2019 (not ClinGen graded tiers) | | Off-target ASO match | <=16/20 nt to any non-target transcript | Design convention | | Concordance for high-confidence | 2/3 predictors above PP3 threshold | Pragmatic |

Related Skills

• splicing-qc - MaxEntScan + library QC for confirming predicted impact
• splicing-quantification - Empirical PSI from RNA-seq to validate predictions
• outlier-splicing-detection - FRASER2/DROP for RNA-seq confirmation in clinical samples
• variant-calling/clinical-interpretation - Broader ACMG/AMP variant interpretation framework
• variant-calling/variant-annotation - VEP plugin integration for SpliceAI

References

• Jaganathan et al 2019 Cell - SpliceAI
• Zeng & Li 2022 Genome Biol - Pangolin
• Cheng et al 2019 Genome Biol - MMSplice
• Cheng et al 2021 Genome Biol - MTSplice (tissue MMSplice)
• You et al 2024 Nat Commun 15:9129 - SpliceTransformer
• Strauch et al 2022 PLoS One 17:e0269159 - CI-SpliceAI extended window
• Smith & Kitzman 2023 Genome Biol 24:294 - SpliceAI/Pangolin MPSA benchmark
• Rentzsch et al 2021 Genome Med - CADD-Splice
• Dawes et al 2023 Nat Genet - SpliceVault
• Walker et al 2023 Am J Hum Genet - ClinGen SVI splicing recommendations
• Riepe et al 2021 Hum Mutat 42:799 - SpliceAI in clinical pipelines (Riepe TV et al)
• Abou Tayoun et al 2018 Hum Mutat - PVS1 decision tree
• Richards et al 2015 Genet Med - ACMG/AMP framework
• den Dunnen et al 2016 Hum Mutat - HGVS standard
• Zhang et al 2022 PNAS (PMID 36306325) - BPHunter for branchpoints
• Hua et al 2008 AJHG - ISS-N1 / nusinersen mechanism
• Roberts et al 2023 Nat Rev Drug Discov 22:917-934 - DMD therapeutic approaches (exon-skipping ASOs)
• Findlay et al 2018 Nature - BRCA1 saturation genome editing (MAVE)