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star-te-preprocessing

Configures STAR alignment and featureCounts to retain transposable-element signal in bulk RNA-seq counts.

1|Updated Nov 20, 2025
Search Tags:#alignment#nf-core#rna-seq#star#featurecounts#transposable elements#multimappers
One-click install
npx skills add https://github.com/tony-zhelonkin/scio --skill star-te-preprocessing-tony-zhelonkin
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: star-te-preprocessing
Source: https://github.com/tony-zhelonkin/scio/tree/main/skills/star-te-preprocessing
Command: npx skills add https://github.com/tony-zhelonkin/scio --skill star-te-preprocessing-tony-zhelonkin

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Standard RNA-seq alignment discards multi-mapping reads, which systematically erases transposable element (TE) signal because TEs are repetitive. This Skill provides the canonical STAR "Random-One" recipe and featureCounts counting contract that preserve TE signal while producing integer, subfamily-level count matrices ready for differential expression. ## Core Features & Use Cases - Canonical STAR recipe: A verbatim --extra_star_align_args string (Random-One: --outSAMmultNmax 1, --outFilterMultimapNmax 100, --outMultimapperOrder Random, seed 777) with per-flag TE rationale, validated on nf-core/rnaseq 3.20.0. - Preprocessing contract: A 12-point checklist covering grouped subfamily SAF (Subfamily:Family:Class labels, ~1,243 meta-features for mm39), exon subtraction via bedtools, integer -M counting without --fraction, and per-library strandedness verification. - Use Case: You are aligning mouse bulk RNA-seq and need TE differential expression later. Pass the canonical STAR string to nf-core, run the two-pass featureCounts driver, and verify the integer TE matrix against the QC gate (TE proportion 3.8–6.0% across replicates). ## Quick Start Ask the AI to generate the TE-compatible STAR alignment arguments and featureCounts counting contract for a bulk RNA-seq run that must preserve transposable element signal.

Frequently Asked Questions about star-te-preprocessing

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I align RNA-seq reads to keep transposable element signal?▼

Use the STAR Random-One recipe: retain multimappers with --outFilterMultimapNmax 100, emit exactly one alignment per read with --outSAMmultNmax 1, randomize among equal hits with --outMultimapperOrder Random, and fix --runRNGseed 777 for reproducibility. Pass the string verbatim as nf-core --extra_star_align_args.

How do I count multi-mapping reads with featureCounts for TE analysis?▼

Run featureCounts with -M to include multimappers and do NOT add --fraction, so counts stay integer under the Random-One strategy. Use a grouped subfamily SAF with Subfamily:Family:Class GeneIDs and paired-end flags -p --countReadPairs -B -C.

Should I use Random-One or fractional counting for TE quantification?▼

Both are equally accurate per Teissandier 2019. Random-One (this recipe) gives integer counts compatible with DESeq2 and smaller BAMs; fractional Strategy B emits all alignments and uses -M --fraction, producing non-integer counts that need limma-voom or rounding.

Why are my TE counts fractional or my gene counts near zero?▼

Fractional TE counts mean --fraction was added; remove it for the integer Random-One recipe. Near-zero gene counts usually mean the featureCounts -s strandedness flag mismatches the library; set it from MultiQC inferred strandedness per library, never assume 1 or 2.

When should I not use STAR Random-One for TE analysis?▼

Do not use it for locus-level or copy-resolved TE quantification, since Random-One assignments are stochastic per locus; use SQuIRE or Telescope (EM-based) instead. It also does not cover generic nf-core run mechanics or downstream DE analysis.

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