single-cell-splicing

Name: single-cell-splicing
Author: GPTomics

1.1kstars185forks

How to install single-cell-splicing?

gptomics/bioskills/single-cell-splicing

$npx -y skills add GPTomics/bioSkills --skill single-cell-splicing

Installs into the current project.

Summary

Analyzes alternative splicing at single-cell resolution. The first decision is library chemistry — 10X 3' is fundamentally limited (RT primes from poly-A, R2 falls in 3' UTR, <0.1 junction read per cell per AS event). Plate-based full-length methods (Smart-seq3, FLASH-seq, VASA-s

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SKILL.md

1	## Version Compatibility
2
3	Reference examples tested with: MARVEL 2.0+, BRIE2 0.2.4+, scQuint 0.1+, SpliZ 0.0.1+, Sierra 1.0+, Psix 0.1+, anndata 0.10+, scanpy 1.10+, pandas 2.2+, scipy 1.13+
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	- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
8	- CLI: `<tool> --version` then `<tool> --help` to confirm flags
9
10	If code throws ImportError, AttributeError, or TypeError, introspect the installed
11	package and adapt the example to match the actual API rather than retrying.
12
13	# Single-Cell Splicing Analysis
14
15	The fundamental decision is chemistry, not tool. Most droplet 3' scRNA-seq cannot support transcriptome-wide splicing inference because reverse transcription primes from the poly(A) tail and most reads land in the 3' UTR — far from CDS-region splicing events. Plate-based full-length methods and single-cell long-read sequencing are the chemistries that give per-cell isoform structure across the gene body.
16
17	## The 10X 3' Problem (Quantified)
18
19	Three compounding mechanisms make 10X Chromium 3' (v3.1, GEM-X, v4) hostile to splicing:
20
21	1. 3' enrichment: median fragment <1 kb from poly(A); >70% of unique reads fall within 3' UTR.
22	2. Short R2 (~91 nt): each read straddles at most one junction; usually none, because R2 lands in 3' UTR.
23	3. PCR concatemers and TSO artifacts: pollute junction detection; UMI collapse is gene-level, not isoform-level.
24
25	Quantitative estimate: Only a small fraction of cassette exons sit close enough to the polyA site to be sampled by 3' chemistry (empirical estimates from APA/3'-end atlases — see Tian & Manley 2017 Nat Rev Mol Cell Biol for the 3' UTR isoform landscape). Effective junction read yield from 10X 3' is <0.1 per cell per AS event — vs the 5-10 needed for stable per-cell PSI. Most splicing analyses on 10X 3' data report artifacts.
26
27	The 5' kit (10X 5' GEX) does not solve this — it shifts capture from 3' UTR to 5' UTR / TSS-proximal regions. Marginal improvement; not a transcriptome-wide solution. Note that V(D)J recovery requires the 10X Chromium Single Cell Immune Profiling kit (with TCR/BCR-specific enrichment), not 5' GEX alone — postdocs designing immune-repertoire experiments must use the dedicated V(D)J kit.
28
29	## Decision: Does the Chemistry Support Splicing Analysis?
30
31	\| Chemistry \| Splicing analysis viable? \| Best alternative if no \|
32	\|-----------\|----------------------------\|--------------------------\|
33	\| 10X 3' (Chromium v3, GEM-X, v4, Flex) \| No (transcriptome-wide); maybe near-3'-end events \| Sierra for APA \|
34	\| 10X 5' GEX \| Limited; near-5'-end events only \| Sierra for alternative TSS; switch to MAS-Iso-seq \|
35	\| Smart-seq2 \| Yes (full transcript) \| MARVEL or BRIE2 \|
36	\| Smart-seq3 / Smart-seq3xpress \| Yes + UMI molecule counting \| MARVEL or BRIE2 \|
37	\| FLASH-seq \| Yes (faster, cheaper Smart-seq3) \| MARVEL or BRIE2 \|
38	\| VASA-seq \| Yes + total RNA (incl. nascent, IR) \| MARVEL with IR analysis \|
39	\| STORM-seq \| Yes + total RNA + ribodepletion \| MARVEL with IR analysis \|
40	\| MAS-Iso-seq + 10X 5' (PacBio Kinnex) \| Yes — full isoforms per cell \| FLAMES, scNanoGPS, IsoQuant, see long-read-splicing \|
41	\| scISOr-Seq2 (PacBio + 10X) \| Yes — full isoforms with cell-typing \| FLAMES, IsoQuant \|
42	\| ONT direct cDNA scRNA \| Yes \| FLAMES \|
43	\| ONT direct RNA scRNA \| Yes + native modifications \| FLAMES \|
44
45	## Tool Selection Matrix
46
47	\| Tool \| Best for \| Input \| Strengths \| Fails when \|
48	\|------\|----------\|-------\|-----------\|------------\|
49	\| MARVEL \| Smart-seq plate-based and (v2+) 10X droplet unified workflow \| Plate or droplet BAMs + Seurat \| SE/A5SS/A3SS/MXE/RI/AFE/ALE; modality classification; native Seurat integration; v2 droplet support \| R-only \|
50	\| BRIE2 \| Plate-based with regulatory feature prior \| Plate BAM + GFF3 events \| Bayesian variational PSI + ELBO_gain test; principled uncertainty; CLI-driven (`brie-count`, `brie-quant`) \| TensorFlow depen

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single-cell-splicing

1.1kstars185forks

View on GitHub

How to install single-cell-splicing?

gptomics/bioskills/single-cell-splicing

$npx -y skills add GPTomics/bioSkills --skill single-cell-splicing

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	## Version Compatibility
2
3	Reference examples tested with: MARVEL 2.0+, BRIE2 0.2.4+, scQuint 0.1+, SpliZ 0.0.1+, Sierra 1.0+, Psix 0.1+, anndata 0.10+, scanpy 1.10+, pandas 2.2+, scipy 1.13+
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	- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
8	- CLI: `<tool> --version` then `<tool> --help` to confirm flags
9
10	If code throws ImportError, AttributeError, or TypeError, introspect the installed
11	package and adapt the example to match the actual API rather than retrying.
12
13	# Single-Cell Splicing Analysis
14
15	The fundamental decision is chemistry, not tool. Most droplet 3' scRNA-seq cannot support transcriptome-wide splicing inference because reverse transcription primes from the poly(A) tail and most reads land in the 3' UTR — far from CDS-region splicing events. Plate-based full-length methods and single-cell long-read sequencing are the chemistries that give per-cell isoform structure across the gene body.
16
17	## The 10X 3' Problem (Quantified)
18
19	Three compounding mechanisms make 10X Chromium 3' (v3.1, GEM-X, v4) hostile to splicing:
20
21	1. 3' enrichment: median fragment <1 kb from poly(A); >70% of unique reads fall within 3' UTR.
22	2. Short R2 (~91 nt): each read straddles at most one junction; usually none, because R2 lands in 3' UTR.
23	3. PCR concatemers and TSO artifacts: pollute junction detection; UMI collapse is gene-level, not isoform-level.
24
25	Quantitative estimate: Only a small fraction of cassette exons sit close enough to the polyA site to be sampled by 3' chemistry (empirical estimates from APA/3'-end atlases — see Tian & Manley 2017 Nat Rev Mol Cell Biol for the 3' UTR isoform landscape). Effective junction read yield from 10X 3' is <0.1 per cell per AS event — vs the 5-10 needed for stable per-cell PSI. Most splicing analyses on 10X 3' data report artifacts.
26
27	The 5' kit (10X 5' GEX) does not solve this — it shifts capture from 3' UTR to 5' UTR / TSS-proximal regions. Marginal improvement; not a transcriptome-wide solution. Note that V(D)J recovery requires the 10X Chromium Single Cell Immune Profiling kit (with TCR/BCR-specific enrichment), not 5' GEX alone — postdocs designing immune-repertoire experiments must use the dedicated V(D)J kit.
28
29	## Decision: Does the Chemistry Support Splicing Analysis?
30
31	\| Chemistry \| Splicing analysis viable? \| Best alternative if no \|
32	\|-----------\|----------------------------\|--------------------------\|
33	\| 10X 3' (Chromium v3, GEM-X, v4, Flex) \| No (transcriptome-wide); maybe near-3'-end events \| Sierra for APA \|
34	\| 10X 5' GEX \| Limited; near-5'-end events only \| Sierra for alternative TSS; switch to MAS-Iso-seq \|
35	\| Smart-seq2 \| Yes (full transcript) \| MARVEL or BRIE2 \|
36	\| Smart-seq3 / Smart-seq3xpress \| Yes + UMI molecule counting \| MARVEL or BRIE2 \|
37	\| FLASH-seq \| Yes (faster, cheaper Smart-seq3) \| MARVEL or BRIE2 \|
38	\| VASA-seq \| Yes + total RNA (incl. nascent, IR) \| MARVEL with IR analysis \|
39	\| STORM-seq \| Yes + total RNA + ribodepletion \| MARVEL with IR analysis \|
40	\| MAS-Iso-seq + 10X 5' (PacBio Kinnex) \| Yes — full isoforms per cell \| FLAMES, scNanoGPS, IsoQuant, see long-read-splicing \|
41	\| scISOr-Seq2 (PacBio + 10X) \| Yes — full isoforms with cell-typing \| FLAMES, IsoQuant \|
42	\| ONT direct cDNA scRNA \| Yes \| FLAMES \|
43	\| ONT direct RNA scRNA \| Yes + native modifications \| FLAMES \|
44
45	## Tool Selection Matrix
46
47	\| Tool \| Best for \| Input \| Strengths \| Fails when \|
48	\|------\|----------\|-------\|-----------\|------------\|
49	\| MARVEL \| Smart-seq plate-based and (v2+) 10X droplet unified workflow \| Plate or droplet BAMs + Seurat \| SE/A5SS/A3SS/MXE/RI/AFE/ALE; modality classification; native Seurat integration; v2 droplet support \| R-only \|
50	\| BRIE2 \| Plate-based with regulatory feature prior \| Plate BAM + GFF3 events \| Bayesian variational PSI + ELBO_gain test; principled uncertainty; CLI-driven (`brie-count`, `brie-quant`) \| TensorFlow depen

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6 picks

Type