Bio Workflows Smrna Pipeline

What it does

When your agent uses it

• 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

Example prompts

• “Use the bio-workflows-smrna-pipeline skill to orchestrate the end-to-end small RNA-seq pipeline from FASTQ to differential miRNAs and…”
• “/bio-workflows-smrna-pipeline”

Requirements

• A Bash shell

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

1. 1The library kit fixes adapter + UMI/4N handling, committed once at trimming and un-reversible. The insert (~22 nt) is far shorter than the…
2. 2The normalizer is the dominant analytic commitment — more than the DE model. miRNA counts are few-featured and compositional; a handful of…
3. 3RAW counts (not RPM) flow into DESeq2, and size factors must be inspected. A large rise in one abundant miRNA mechanically deflates all…
4. 4Targets are hypotheses, not findings. miRanda output must be intersected with anti-correlated mRNA DE and validated databases before…

What it can do on your machine

Read from SKILL.md and the folder at commit d91ed3d. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships script files (Shell), which the agent can run.

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Bio Workflows Smrna Pipeline loads about 3.8k tokens when it runs. Until then it costs about 212 tokens; SKILL.md has 1,253 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~212

When it runs · the whole SKILL.md, loaded when a task matches

~3.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from GPTomics/bioSkills at commit d91ed3d, republished under its MIT licence (© GPTomics). 1,253 words, ~3,762 tokens.

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.

tool_type

mixed

primary_tool

miRge3.0

workflow

true

depends_on

small-rna-seq/smrna-preprocessing, small-rna-seq/mirge3-analysis, small-rna-seq/mirdeep2-analysis, small-rna-seq/differential-mirna…

FASTQ (single-end small RNA)
  | [1] Trim (kit-aware) --------> cutadapt (+ umi_tools / 4N)   (small-rna-seq/smrna-preprocessing)
  v     ^-- commitment: kit adapter + UMI/4N; --discard-untrimmed; NO position-dedup w/o UMI
  | [2] Quantify OR discover ----> miRge3.0 (known+isomiR) | miRDeep2 (novel)  (small-rna-seq/mirge3-analysis, mirdeep2-analysis)
  v
  | [3] Differential expression -> DESeq2 on RAW counts; INSPECT size factors   (small-rna-seq/differential-mirna)
  v     ^-- commitment: the NORMALIZER (drives which miRNAs are DE)
  | [4] Target prediction -------> miRanda, filtered by anti-correlated mRNA DE  (small-rna-seq/target-prediction)
  v
Differential miRNAs + expression-supported targets
   (tRF/piRNA reads -> small-rna-seq/trf-pirna-profiling)

# Kit-aware trim: adapter on EVERY read, so --discard-untrimmed drops no-insert junk (inverse of gDNA).
# NEXTflex 4N: cutadapt -u 4 -u -4 AFTER adapter. QIAseq UMIs: umi_tools. NEVER position-dedup non-UMI.
cutadapt -a TGGAATTCTCGGGTGCCAAGG --minimum-length 18 --maximum-length 30 --discard-untrimmed \
    -o trimmed.fastq.gz reads.fastq.gz

# Known-miRNA + isomiR quantification (the common case). NOTE: v3 has NO 'annotate' subcommand
# (that was miRge2); it is `miRge3.0 -s ...`. Reads are already cutadapt-trimmed, so -a is OMITTED
# (v3 has no 'none' keyword; passing one makes cutadapt treat it as an adapter sequence and fail).
# isomiRs are annotated by default; -gff emits the isomiR GFF.
# (-ai would instead compute A-to-I editing, a separate analysis, not isomiR reporting.)
miRge3.0 -s trimmed.fastq.gz -lib /path/to/miRge3_Lib -on human -db mirbase \
    -gff -cpu 8 -o mirge_out

library(DESeq2)
# RAW counts (miR.Counts.csv), NOT RPM. A few miRNAs can dominate, so inspect sizeFactors first.
# miRge3.0 writes into a timestamped subfolder (mirge_out/miRge.YYYY-M-D_h-m-s/), not directly into -o
counts <- read.csv(Sys.glob('mirge_out/miRge.*/miR.Counts.csv')[1], row.names = 1)
dds <- DESeqDataSetFromMatrix(round(counts), colData, ~condition)
dds <- dds[rowSums(counts(dds)) >= 10, ]      # lower prefilter than mRNA
dds <- DESeq(dds)
print(sizeFactors(dds))                       # a dominant-miRNA shift distorts these -> spurious calls
res <- lfcShrink(dds, coef = 'condition_treated_vs_control', type = 'apeglm')

# Targets are a hypothesis: intersect with anti-correlated mRNA DE before trusting them.
# -sc 140: miRanda default alignment-score floor; -en -20: keep duplexes with MFE <= -20 kcal/mol (stable binding)
miranda mature_mirnas.fa target_3utrs.fa -sc 140 -en -20 -strict -out targets.txt

gunzip -kc trimmed.fastq.gz > trimmed.fastq            # mapper.pl reads plain-text FASTQ, not gzip
mapper.pl trimmed.fastq -e -h -i -j -l 18 -m -p genome_index \
    -s reads_collapsed.fa -t reads_collapsed_vs_genome.arf
miRDeep2.pl reads_collapsed.fa genome.fa reads_collapsed_vs_genome.arf \
    mature_ref.fa none hairpin_ref.fa -t Human      # score cutoff from survey.pl signal-to-noise

© GPTomics, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files in workflows/smrna-pipeline of GPTomics/bioSkills.

• SKILL.md
• examples/smrna_full_pipeline.sh
• usage-guide.md

Open the folder on GitHubat commit d91ed3d

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in GPTomics/bioSkills, which our catalogue first saw on October 7, 2026.

• PKU-YuanGroup/OpenAI4Sskills/bioskills/bio-workflows-smrna-pipeline