splicing-qc

Name: splicing-qc
Author: GPTomics

1.1kstars185forks

How to install splicing-qc?

gptomics/bioskills/splicing-qc

$npx -y skills add GPTomics/bioSkills --skill splicing-qc

Installs into the current project.

Summary

Assesses RNA-seq data quality specifically for alternative splicing analysis. QC layers include experimental design audit (library prep, read length, depth, replicates), STAR 2-pass cohort-style alignment, junction saturation curves and discovery plateau detection, novel-vs-known

Files · 1

View on GitHub

SKILL.md

1	## Version Compatibility
2
3	Reference examples tested with: RSeQC 5.0+, STAR 2.7.11+, samtools 1.19+, pysam 0.22+, regtools 1.0+, maxentpy 0.0.1+, spliceai 1.3+, matplotlib 3.8+, pandas 2.2+
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	# Splicing-Specific Quality Control
13
14	Splicing analysis is more demanding than DGE on read length, depth, library prep, alignment strategy, and annotation choice. Failures in any of these silently bias PSI estimates and inflate novel-junction false positives. The decision sequence is: experimental design -> library prep -> alignment strategy -> annotation -> diagnostic metrics. Each layer's failure mode is distinct.
15
16	## QC Layer Taxonomy
17
18	\| Layer \| Target \| Tool \| Fails when \|
19	\|-------\|--------\|------\|------------\|
20	\| Experimental design \| Read length, depth, replicates, library type \| Pre-sequencing review \| <PE 75nt; n<3 vs n<3; <30M reads/sample \|
21	\| Library prep \| poly(A) vs rRNA depletion \| Pre-sequencing review \| poly(A) library used for IR analysis \|
22	\| Alignment \| STAR 2-pass cohort-style \| STAR \| 1-pass loses 14% novel junctions; per-sample 2-pass introduces inconsistency \|
23	\| Junction discovery \| Saturation, novelty \| RSeQC `junction_saturation`, `junction_annotation` \| Curve still rising = under-sequenced; novel% >40% suggests biology or artifact \|
24	\| Strand specificity \| Library protocol consistency \| RSeQC `infer_experiment` \| Wrong `--libType` halves usable junctions \|
25	\| Splice site strength \| Cryptic vs canonical \| MaxEntScan, SpliceAI \| Weak splice sites (MaxEnt<5) may indicate cryptic, regulated, or annotation error \|
26	\| Junction overhang \| Read-junction support quality \| pysam CIGAR parsing \| Overhang <8nt = high false-positive rate \|
27	\| Contamination \| rRNA, adapters \| fastq_screen \| >20% rRNA in "depleted" library = failed depletion \|
28	\| Annotation \| GENCODE basic vs comprehensive \| Annotation choice \| Basic for canonical events; comprehensive for DTU \|
29
30	## Decision Tree by Question
31
32	\| Question \| Recommended QC \|
33	\|----------\|-----------------\|
34	\| Will my planned RNA-seq design support AS analysis? \| Pre-sequencing audit: library type, read length, depth, replicates \|
35	\| Is my data suitable for cassette exon analysis? \| Junction saturation + known/novel ratio + read length \|
36	\| Why does my AS analysis call so few events? \| Saturation curve, depth, library type, alignment 2-pass \|
37	\| Why does my AS analysis call so many novel junctions? \| Annotation completeness + novel% + biology check (TDP-43, SF3B1) \|
38	\| Are my SpliceAI predictions calibrated for my tissue? \| MaxEntScan + SpliceAI concordance for known sites \|
39	\| Did STAR 2-pass actually run cohort-style? \| Verify SJ.out.tab merging across samples \|
40	\| Is intron retention detectable in my data? \| Library type (must be rRNA-depleted); strand-specific \|
41	\| Are my microexons detectable? \| Read length >=100; aligner anchor settings; consider VAST-TOOLS \|
42
43	## Experimental Design Audit (Before Sequencing)
44
45	\| Decision \| For splicing analysis \| Rationale \|
46	\|----------\|------------------------\|-----------\|
47	\| Library prep \| rRNA depletion (Ribo-Zero, RiboCop) \| poly(A) selection loses pre-mRNA, nascent transcripts, and detained introns; for IR analysis rRNA depletion is mandatory (convention) \|
48	\| Read length \| PE 100-150 nt (PE 150 strongly preferred) \| Junction-spanning reads need >=8 nt overhang on each exon; shorter single-end reads bias junction detection toward shorter exons (convention) \|
49	\| Pairing \| Paired-end \| Single-end loses fragment-level disambiguation of junctions \|
50	\| Depth \| 50-100M reads/sample \| DGE-grade 30M misses low-PSI events; 100M for low-abundance event discovery \|
51	\| Strandedness \| Stranded library (I

People who liked this also viewed

6 picks

Type

splicing-qc

1.1kstars185forks

View on GitHub

How to install splicing-qc?

gptomics/bioskills/splicing-qc

$npx -y skills add GPTomics/bioSkills --skill splicing-qc

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	## Version Compatibility
2
3	Reference examples tested with: RSeQC 5.0+, STAR 2.7.11+, samtools 1.19+, pysam 0.22+, regtools 1.0+, maxentpy 0.0.1+, spliceai 1.3+, matplotlib 3.8+, pandas 2.2+
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	# Splicing-Specific Quality Control
13
14	Splicing analysis is more demanding than DGE on read length, depth, library prep, alignment strategy, and annotation choice. Failures in any of these silently bias PSI estimates and inflate novel-junction false positives. The decision sequence is: experimental design -> library prep -> alignment strategy -> annotation -> diagnostic metrics. Each layer's failure mode is distinct.
15
16	## QC Layer Taxonomy
17
18	\| Layer \| Target \| Tool \| Fails when \|
19	\|-------\|--------\|------\|------------\|
20	\| Experimental design \| Read length, depth, replicates, library type \| Pre-sequencing review \| <PE 75nt; n<3 vs n<3; <30M reads/sample \|
21	\| Library prep \| poly(A) vs rRNA depletion \| Pre-sequencing review \| poly(A) library used for IR analysis \|
22	\| Alignment \| STAR 2-pass cohort-style \| STAR \| 1-pass loses 14% novel junctions; per-sample 2-pass introduces inconsistency \|
23	\| Junction discovery \| Saturation, novelty \| RSeQC `junction_saturation`, `junction_annotation` \| Curve still rising = under-sequenced; novel% >40% suggests biology or artifact \|
24	\| Strand specificity \| Library protocol consistency \| RSeQC `infer_experiment` \| Wrong `--libType` halves usable junctions \|
25	\| Splice site strength \| Cryptic vs canonical \| MaxEntScan, SpliceAI \| Weak splice sites (MaxEnt<5) may indicate cryptic, regulated, or annotation error \|
26	\| Junction overhang \| Read-junction support quality \| pysam CIGAR parsing \| Overhang <8nt = high false-positive rate \|
27	\| Contamination \| rRNA, adapters \| fastq_screen \| >20% rRNA in "depleted" library = failed depletion \|
28	\| Annotation \| GENCODE basic vs comprehensive \| Annotation choice \| Basic for canonical events; comprehensive for DTU \|
29
30	## Decision Tree by Question
31
32	\| Question \| Recommended QC \|
33	\|----------\|-----------------\|
34	\| Will my planned RNA-seq design support AS analysis? \| Pre-sequencing audit: library type, read length, depth, replicates \|
35	\| Is my data suitable for cassette exon analysis? \| Junction saturation + known/novel ratio + read length \|
36	\| Why does my AS analysis call so few events? \| Saturation curve, depth, library type, alignment 2-pass \|
37	\| Why does my AS analysis call so many novel junctions? \| Annotation completeness + novel% + biology check (TDP-43, SF3B1) \|
38	\| Are my SpliceAI predictions calibrated for my tissue? \| MaxEntScan + SpliceAI concordance for known sites \|
39	\| Did STAR 2-pass actually run cohort-style? \| Verify SJ.out.tab merging across samples \|
40	\| Is intron retention detectable in my data? \| Library type (must be rRNA-depleted); strand-specific \|
41	\| Are my microexons detectable? \| Read length >=100; aligner anchor settings; consider VAST-TOOLS \|
42
43	## Experimental Design Audit (Before Sequencing)
44
45	\| Decision \| For splicing analysis \| Rationale \|
46	\|----------\|------------------------\|-----------\|
47	\| Library prep \| rRNA depletion (Ribo-Zero, RiboCop) \| poly(A) selection loses pre-mRNA, nascent transcripts, and detained introns; for IR analysis rRNA depletion is mandatory (convention) \|
48	\| Read length \| PE 100-150 nt (PE 150 strongly preferred) \| Junction-spanning reads need >=8 nt overhang on each exon; shorter single-end reads bias junction detection toward shorter exons (convention) \|
49	\| Pairing \| Paired-end \| Single-end loses fragment-level disambiguation of junctions \|
50	\| Depth \| 50-100M reads/sample \| DGE-grade 30M misses low-PSI events; 100M for low-abundance event discovery \|
51	\| Strandedness \| Stranded library (I

People who liked this also viewed

6 picks

Type