splice-variant-prediction

Name: splice-variant-prediction
Author: GPTomics

1.1kstars185forks

How to install splice-variant-prediction?

gptomics/bioskills/splice-variant-prediction

$npx -y skills add GPTomics/bioSkills --skill splice-variant-prediction

Installs into the current project.

Summary

Predicts whether a DNA variant alters mRNA splicing using sequence-based deep-learning tools — SpliceAI (10kb context dilated CNN, clinical default), Pangolin (multi-tissue), MMSplice (modular per-region CNN with calibrated ΔPSI), SpliceTransformer/TrASPr (tissue-aware transforme

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SKILL.md

1	## Version Compatibility
2
3	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+
4
5	Before using code patterns, verify installed versions match. If versions differ:
6	- Python: `pip show <package>` then `help(module.function)` to check signatures
7	- CLI: `<tool> --version` then `<tool> --help` to confirm flags
8
9	If code throws ImportError, AttributeError, or TypeError, introspect the installed
10	package and adapt the example to match the actual API rather than retrying.
11
12	# Splice Variant Prediction
13
14	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?
15
16	## Predictor Taxonomy
17
18	\| Family \| Architecture \| Output \| Fails when \|
19	\|--------\|--------------\|--------\|------------\|
20	\| Context-aware CNN \| 10 kb dilated ResNet \| Per-position donor/acceptor probability \| Long-range (>5 kb) regulatory effects; tissue-specific events \|
21	\| Tissue-aware CNN/transformer \| Same arch + multi-tissue training \| Per-tissue ΔPSI \| Tissue not in training set; novel cell types \|
22	\| Modular per-region CNN \| Separate sub-models for 5'ss/3'ss/exon/intron \| Calibrated quantitative ΔPSI \| Atypical events; complex multi-junction effects \|
23	\| Foundation transformer \| Pretrained on broad genomic context \| Splice probability or ΔPSI \| New tools; less battle-tested \|
24	\| Empirical lookup \| Public RNA-seq event database \| Top-N most likely mis-splicing outcomes \| Variant types not represented in training cohorts \|
25	\| Composite score \| Blend of multiple predictors \| Single scaled score \| When component predictors disagree internally \|
26
27	## Tool Selection Matrix
28
29	\| Tool \| Best for \| Output \| When to use \| Fails when \|
30	\|------\|----------\|--------\|-------------\|------------\|
31	\| 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 \|
32	\| Pangolin \| Tissue-aware predictions \| Per-tissue ΔPSI \| When disease tissue is known (brain, heart, liver, testis) \| Tissue not in 4-tissue training set \|
33	\| MMSplice \| Quantitative ΔPSI \| Δlogit_psi \| Research where calibrated effect-size matters \| Atypical events outside cassette-exon model \|
34	\| SpliceTransformer \| 2024+ benchmark improvements \| Tissue-specific ΔPSI \| When transformer foundation models outperform CNN on benchmark variant sets \| New (2024); limited clinical adoption \|
35	\| TrASPr \| Multi-transformer, 2024-2025 \| Tissue-specific PSI/ΔPSI \| Strong on tissue-specific test sets \| New; verify before clinical use \|
36	\| 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 \|
37	\| CADD-Splice \| Single composite score \| Scaled C-score \| Clinical pipelines wanting one number \| When knowing which sub-component drove the score is needed \|
38
39	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.
40
41	## Decision Tree by Use Case
42
43	\| Use case \| Recommended approach \|
44	\|----------\|----------------------\|
45	\| Clinical variant report (single variant, ACMG classification) \| SpliceAI default 50nt + ClinGen SVI 2023 thresholds \|
46	\| Tissue-specific clinical question (brain disease, cardiomyopathy) \| SpliceAI + Pangolin (tissue-matched) \|
47	\| Unsolved Mendelian case (suspect deep-intronic) \| SpliceAI extended window (-D 500-2000) + SpliceVault \|
48	\| VUS panel screening \| SpliceAI + Pangolin + MMSpl

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splice-variant-prediction

1.1kstars185forks

View on GitHub

How to install splice-variant-prediction?

gptomics/bioskills/splice-variant-prediction

$npx -y skills add GPTomics/bioSkills --skill splice-variant-prediction

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	## Version Compatibility
2
3	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+
4
5	Before using code patterns, verify installed versions match. If versions differ:
6	- Python: `pip show <package>` then `help(module.function)` to check signatures
7	- CLI: `<tool> --version` then `<tool> --help` to confirm flags
8
9	If code throws ImportError, AttributeError, or TypeError, introspect the installed
10	package and adapt the example to match the actual API rather than retrying.
11
12	# Splice Variant Prediction
13
14	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?
15
16	## Predictor Taxonomy
17
18	\| Family \| Architecture \| Output \| Fails when \|
19	\|--------\|--------------\|--------\|------------\|
20	\| Context-aware CNN \| 10 kb dilated ResNet \| Per-position donor/acceptor probability \| Long-range (>5 kb) regulatory effects; tissue-specific events \|
21	\| Tissue-aware CNN/transformer \| Same arch + multi-tissue training \| Per-tissue ΔPSI \| Tissue not in training set; novel cell types \|
22	\| Modular per-region CNN \| Separate sub-models for 5'ss/3'ss/exon/intron \| Calibrated quantitative ΔPSI \| Atypical events; complex multi-junction effects \|
23	\| Foundation transformer \| Pretrained on broad genomic context \| Splice probability or ΔPSI \| New tools; less battle-tested \|
24	\| Empirical lookup \| Public RNA-seq event database \| Top-N most likely mis-splicing outcomes \| Variant types not represented in training cohorts \|
25	\| Composite score \| Blend of multiple predictors \| Single scaled score \| When component predictors disagree internally \|
26
27	## Tool Selection Matrix
28
29	\| Tool \| Best for \| Output \| When to use \| Fails when \|
30	\|------\|----------\|--------\|-------------\|------------\|
31	\| 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 \|
32	\| Pangolin \| Tissue-aware predictions \| Per-tissue ΔPSI \| When disease tissue is known (brain, heart, liver, testis) \| Tissue not in 4-tissue training set \|
33	\| MMSplice \| Quantitative ΔPSI \| Δlogit_psi \| Research where calibrated effect-size matters \| Atypical events outside cassette-exon model \|
34	\| SpliceTransformer \| 2024+ benchmark improvements \| Tissue-specific ΔPSI \| When transformer foundation models outperform CNN on benchmark variant sets \| New (2024); limited clinical adoption \|
35	\| TrASPr \| Multi-transformer, 2024-2025 \| Tissue-specific PSI/ΔPSI \| Strong on tissue-specific test sets \| New; verify before clinical use \|
36	\| 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 \|
37	\| CADD-Splice \| Single composite score \| Scaled C-score \| Clinical pipelines wanting one number \| When knowing which sub-component drove the score is needed \|
38
39	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.
40
41	## Decision Tree by Use Case
42
43	\| Use case \| Recommended approach \|
44	\|----------\|----------------------\|
45	\| Clinical variant report (single variant, ACMG classification) \| SpliceAI default 50nt + ClinGen SVI 2023 thresholds \|
46	\| Tissue-specific clinical question (brain disease, cardiomyopathy) \| SpliceAI + Pangolin (tissue-matched) \|
47	\| Unsolved Mendelian case (suspect deep-intronic) \| SpliceAI extended window (-D 500-2000) + SpliceVault \|
48	\| VUS panel screening \| SpliceAI + Pangolin + MMSpl

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