bio-workflows-smrna-pipeline

name	bio-workflows-smrna-pipeline
description	Orchestrates the end-to-end small RNA-seq pipeline from FASTQ to differential miRNAs and expression-filtered targets, chaining kit-aware cutadapt trimming (adapter on every read, UMI/4N handling), miRge3 known+isomiR quantification or miRDeep2 novel discovery, compositionally-aware DESeq2, and miRanda target prediction. Use when committing the library-kit adapter/UMI handling once, choosing the NORMALIZER (which drives which miRNAs are called DE more than the DE model does), deciding known quantification vs novel discovery, handling biofluid/plasma libraries that lack a trustworthy endogenous normalizer, routing tRF/piRNA reads to their own profiling, or feeding RAW (not RPM) counts with size-factor inspection into DE. Hands mechanism to the small-rna-seq component skills; not a re-teach of any single step.
workflow	true
depends_on	["small-rna-seq/smrna-preprocessing","small-rna-seq/mirge3-analysis","small-rna-seq/mirdeep2-analysis","small-rna-seq/differential-mirna","small-rna-seq/target-prediction","small-rna-seq/trf-pirna-profiling"]
qc_checkpoints	[{"after_trim":"Read-length peak 21-23 nt (30+ smear = degradation/tRNA/rRNA; 26-32 peak = piRNA)"},{"after_quant":"RNA-class composition checked (miRNA vs tRF/rRF/piRNA); abundant non-miRNA = different story"},{"before_de":"RAW counts confirmed (not RPM); size factors inspected for compositional distortion"},{"after_de":"baseMean reported with every call (significant FC on a ~5-count miRNA is noise)"}]
origin	openai4s
category	bioskills/workflows
metadata	{"tool_type":"mixed","primary_tool":"miRge3.0","third_party":{"name":"GPTomics/bioSkills","repository":"https://github.com/GPTomics/bioSkills","commit":"d91ed3d563019e649dc854c56ccd62551359488a","license":"MIT"}}

Version Compatibility

Reference examples tested with: cutadapt 4.4+, miRge3.0 0.1.4+, miRDeep2 2.0.1.3+, DESeq2 1.42+, apeglm 1.24+, miRanda 3.3a, umi_tools 1.1+, miRTrace 1.0+

Before using code patterns, verify installed versions match. If versions differ:

• R: packageVersion('<pkg>') then ?function_name to verify parameters
• 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.

Note: the correct 3' adapter sequence and the UMI/4N scheme are KIT-specific (TruSeq vs NEXTflex vs QIAseq) — confirm against the kit before trimming. The normalizer that best fits compositional miRNA data drifts with the method literature; treat the table below as a decision aid to validate, not a fixed rule.

Small RNA-seq Pipeline

"Analyze my small RNA-seq data from FASTQ to differential miRNAs" -> Chain kit-aware trimming, known-miRNA quantification (or novel discovery), a compositionally-aware DE test, and expression-filtered target prediction.

• CLI + R: cutadapt -> (miRge3.0 | miRDeep2) -> DESeq2 (inspect size factors) -> miRanda (filter by anti-correlated mRNA)

This is a workflow skill: it owns the chaining decisions and hand-offs, not the internals of any one step. Every step below cross-references the component skill that teaches its mechanism.

The governing principle

A small RNA-seq result turns on two things the mRNA pipeline never faces — the adapter is on every read, and the counts are compositional — so the trustworthy result is decided at these seams.

1. The library kit fixes adapter + UMI/4N handling, committed once at trimming and un-reversible. The insert (~22 nt) is far shorter than the read, so the 3' adapter is on EVERY real read and --discard-untrimmed correctly drops no-insert junk (the inverse of genomic DNA). NEXTflex 4N random bases are stripped AFTER adapter removal; QIAseq UMIs are extracted and deduped. NEVER position-dedup a non-UMI small-RNA library — abundant miRNAs legitimately share start positions, so position-dedup destroys real signal.
2. The normalizer is the dominant analytic commitment — more than the DE model. miRNA counts are few-featured and compositional; a handful of miRNAs can dominate the library. Global scaling (TMM's 30%/5% trimming, median-of-ratios) is built for ~20k mRNAs and is harmful among hundreds of miRNAs (Garmire 2012). The normalizer choice CHANGES which miRNAs are called DE. Commit and report it; do not default silently.
3. RAW counts (not RPM) flow into DESeq2, and size factors must be inspected. A large rise in one abundant miRNA mechanically deflates all others (the closed-composition artifact), manufacturing spurious "down" calls. Confirm the input is raw integer counts and inspect before trusting any call.