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Agent skill

Bio Read Qc Rnaseq Qc

by GPTomics in GPTomics/bioSkills

Runs RNA-seq-specific post-alignment QC - strandedness inference, gene-body 5'-3' coverage, read distribution (exonic/intronic/intergenic), rRNA/globin/mitochondrial rate, transcript integrity…

MITAuto-check passedResearch & Science

Install Bio Read Qc Rnaseq Qc

skills CLI
$ npx skills add GPTomics/bioSkills --skill bio-read-qc-rnaseq-qc -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install GPTomics/bioSkills bio-read-qc-rnaseq-qc --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/read-qc/rnaseq-qc .claude/skills/bio-read-qc-rnaseq-qc && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
bio-read-qc-rnaseq-qc
GitHub stars
1.2k
Used in
1 other repo
Token cost
~3.4k tokens
SKILL.md length
1,387 words
Files
4
Skills in repo
559
Repo updated
First seen
Licence
MIT

At a glance

Runs RNA-seq-specific post-alignment QC - strandedness inference, gene-body 5'-3' coverage, read distribution (exonic/intronic/intergenic), rRNA/globin/mitochondrial rate, transcript integrity…

  • Works in 3 steps: These are POST-ALIGNMENT QC: every… → Getting strandedness wrong SILENTLY… → In standard bulk RNA-seq WITHOUT UMIs,…
  • Validating RNA-seq libraries before quantification
  • SKILL.md covers Version Compatibility, The Single Most Important…, Tool Taxonomy and Strandedness -- infer, then…, plus 9 more sections
  • Runs Shell scripts from its folder; calls pip

What it does

Bio Read Qc Rnaseq Qc is an agent skill from GPTomics/bioSkills. Runs RNA-seq-specific post-alignment QC - strandedness inference, gene-body 5'-3' coverage, read distribution (exonic/intronic/intergenic), rRNA/globin/mitochondrial rate, transcript integrity (TIN), and saturation - with RSeQC, Qualimap, RNA-SeQC, and Picard. Use when validating RNA-seq libraries before quantification or differential expression, diagnosing degradation or gDNA contamination, or determining library strandedness. For raw-FASTQ QC use quality-reports; for UMI dedup use umi-processing.

Its SKILL.md is about 3.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files (for example `examples/check_rrna.sh`, `examples/rnaseq_qc.sh` and `usage-guide.md`).

It sits in Research & Science, covering Bioinformatics. The repository describes itself as: a set of SKILLS.md for doing bioinformatics with agents like claude code. The licence is MIT.

When your agent uses it

  • Validating RNA-seq libraries before quantification
  • Differential expression
  • Diagnosing degradation
  • GDNA contamination

Example prompts

  • “/bio-read-qc-rnaseq-qc”

Requirements

  • A Bash shell

Workflow steps

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

  1. These are POST-ALIGNMENT QC: every metric needs an aligned BAM AND a gene model (BED12 / GTF / refFlat / collapsed-GTF), which is the line…
  2. Getting strandedness wrong SILENTLY HALVES OR ZEROS the counts -- no error is thrown. dUTP (TruSeq Stranded mRNA, most rRNA-depletion…
  3. In standard bulk RNA-seq WITHOUT UMIs, do NOT mark or remove duplicates. A highly expressed gene legitimately produces many fragments…

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.

    Shell commands in SKILL.md call:

    • pip

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

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    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 Read Qc Rnaseq Qc loads about 3.4k tokens when it runs. Until then it costs about 131 tokens; SKILL.md has 1,387 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~131
When it runs · the whole SKILL.md, loaded when a task matches
~3.4k

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

Auto-check passed

The automated check found no risky patterns in SKILL.md.

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). 1,387 words, ~3,363 tokens.

Download SKILL.mdSave it as .claude/skills/bio-read-qc-rnaseq-qc/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
bio-read-qc-rnaseq-qc
description
Runs RNA-seq-specific post-alignment QC - strandedness inference, gene-body 5'-3' coverage, read distribution (exonic/intronic/intergenic), rRNA/globin/mitochondrial rate, transcript integrity (TIN), and saturation - with RSeQC, Qualimap, RNA-SeQC, and Picard. Use when validating RNA-seq libraries before quantification or differential expression, diagnosing degradation or gDNA contamination, or determining library strandedness. For raw-FASTQ QC use quality-reports; for UMI dedup use umi-processing.
tool_type
mixed
primary_tool
RSeQC

Version Compatibility

Reference examples tested with: RSeQC 5.0+, Qualimap 2.3+, RNA-SeQC 2.4+, Picard 3.1+, salmon 1.10+, samtools 1.19+

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

  • CLI: <tool> --version then <tool> --help to confirm flags
  • Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

RNA-seq QC -- post-alignment metrics that have no DNA analogue

Assess strandedness, integrity, feature distribution, and enrichment on the ALIGNED BAM, using RSeQC / Qualimap / RNA-SeQC / Picard against a gene model.

"Run RNA-seq QC" -> Infer strandedness, gene-body coverage, exonic/intronic/intergenic distribution, rRNA rate, and TIN from the BAM.

  • CLI: infer_experiment.py -i aligned.bam -r genes.bed12 (strandedness)
  • CLI: picard CollectRnaSeqMetrics / qualimap rnaseq / rnaseqc collapsed.gtf in.bam out/

Scope: this skill OWNS transcriptome QC on the aligned BAM. Raw-FASTQ QC (adapters, base quality) -> read-qc/quality-reports. UMI dedup -> read-qc/umi-processing. Quantification -> rna-quantification/featurecounts-counting. OUT OF SCOPE: differential expression (differential-expression/deseq2-basics).

The Single Most Important Modern Insight

  1. These are POST-ALIGNMENT QC: every metric needs an aligned BAM AND a gene model (BED12 / GTF / refFlat / collapsed-GTF), which is the line that separates them from FastQC. FastQC answers "is the sequencer output clean?"; RNA-seq QC answers "did I sequence the transcriptome I think I sequenced, in the orientation I think, with the integrity I think?" The metrics below (strand, exonic rate, rRNA rate, 5'-3' bias) have no DNA analogue because DNA has no exons, no strand of transcription, and no rRNA fraction. The most common setup error is feeding the wrong gene-model format (RSeQC wants BED12; Qualimap a GTF; RNA-SeQC a COLLAPSED GTF; Picard a refFlat + ribosomal_intervals).

  2. Getting strandedness wrong SILENTLY HALVES OR ZEROS the counts -- no error is thrown. dUTP (TruSeq Stranded mRNA, most rRNA-depletion kits) is fr-firststrand = REVERSE = featureCounts -s 2 = htseq reverse = salmon ISR = STAR ReadsPerGene column 4. Run it as "forward" and reads land on the antisense gene: counts collapse toward zero and the antisense neighbor inflates (running stranded data as UNSTRANDED, by contrast, roughly doubles counts). The tell is a huge "assigned to no feature" fraction or counts ~2x below the unstranded run. ALWAYS infer strandedness empirically (infer_experiment.py, salmon -l A, or how_are_we_stranded_here) before quantifying -- never assume from the kit name.

  3. In standard bulk RNA-seq WITHOUT UMIs, do NOT mark or remove duplicates. A highly expressed gene legitimately produces many fragments sharing identical coordinates; at the read level a PCR duplicate and a natural duplicate are INDISTINGUISHABLE. Coordinate dedup (Picard MarkDuplicates) preferentially deletes reads from the most abundant and shortest transcripts, introducing an expression- and length-dependent bias. This is the OPPOSITE of DNA-seq. Duplication rate is a DIAGNOSTIC ("low complexity / over-sequenced / low input"), never a remove step. The only correct way to remove RNA PCR duplicates is UMIs (read-qc/umi-processing); UMI-protocol RNA-seq (QuantSeq, 10x) inverts the rule.

Integrity bonus: RIN is an electrophoresis estimate measured BEFORE library prep; gene-body coverage and TIN are the post-hoc TRUTH measured from the aligned reads. Use DV200 (% fragments >200 nt), not RIN, for FFPE/archival. In a cohort with variable quality, regress medTIN out as a covariate rather than discarding samples.

Tool Taxonomy

ToolGene modelRole
RSeQCBED12The script suite: infer_experiment, geneBody_coverage, read_distribution, tin, junction_saturation, read_duplication
Qualimap 2GTFqualimap rnaseq: feature distribution + transcript 5'-3' profile + junctions in one HTML (bamqc is the generic, non-RNA mode)
RNA-SeQC 2COLLAPSED GTFGTEx/TOPMed tool; scales to tens of thousands of samples; exonic/intronic/intergenic + rRNA rate + TPM
Picard CollectRnaSeqMetricsrefFlat + ribosomal_intervalsPCT_CODING/UTR/INTRONIC/INTERGENIC/RIBOSOMAL, MEDIAN_5PRIME_TO_3PRIME_BIAS (cannot compute rRNA without the intervals)
SortMeRNArRNA databaseFilter/quantify rRNA reads directly

QC-gate order: (1) FastQC on raw FASTQ -> (2) align (STAR/HISAT2) -> (3) post-alignment QC: strandedness FIRST (it gates correct quantification), then read distribution, gene-body + TIN, rRNA/globin/MT, duplication + saturation -> (4) aggregate with MultiQC and judge each sample against the cohort.

Strandedness -- infer, then set every tool to match

bash
infer_experiment.py -i aligned.bam -r genes.bed12     # samples reads, reports the two fractions
salmon quant -i index -l A -r sample.fq.gz -o quant/  # -l A auto-detects; see lib_format_counts.json
Protocolinfer_experiment dominant fractionsalmon -l (PE/SE)featureCounts -shtseqSTAR ReadsPerGene col
Unstrandedboth ~0.5IU / U0no2
fr-secondstrand (forward)"1++,1--,2+-,2-+"ISF / SF1yes3
fr-firststrand (reverse, dUTP -- common)"1+-,1-+,2++,2--"ISR / SR2reverse4

Single-end infer_experiment drops the read-number prefix: forward = "++,--", reverse = "+-,-+". A STAR sanity check: the ReadsPerGene column with the most counts and fewest N_noFeature is the correct strand (the wrong column makes N_noFeature blow up). Picard STRAND_SPECIFICITY is a notorious inversion: NONE / FIRST_READ_TRANSCRIPTION_STRAND (= forward/fr-secondstrand) / SECOND_READ_TRANSCRIPTION_STRAND (= dUTP/reverse/fr-firststrand, the common case).

Gene-body coverage and integrity

bash
geneBody_coverage.py -i aligned.bam -r genes.bed12 -o coverage   # 5'->3' uniformity curve
tin.py -i aligned.bam -r genes.bed12 > tin.txt                   # per-transcript integrity; medTIN = sample score

3' bias (coverage piling at the 3' end) = RNA degradation OR oligo-dT priming of degraded/FFPE RNA -- which is why poly-A protocols fail on FFPE and rRNA-depletion + random priming is preferred there. 5' bias is rarer (5'-capture protocols / artifacts). Flat = intact RNA. RIN/DV200/TIN: RIN (1-10, pre-prep, electrophoresis) predicts degradation; DV200 (% >200 nt) is the FFPE metric because fragmented RNA has no rRNA peaks for RIN; TIN is measured from the data and can be used as a DE covariate.

Show full SKILL.md (582 more words)Show less

Read distribution and enrichment

bash
read_distribution.py -i aligned.bam -r genes.bed12 > distribution.txt
  • High INTRONIC = pre-mRNA / nuclear RNA or gDNA contamination (in snRNA-seq it is SIGNAL, not a fail).
  • High INTERGENIC = gDNA contamination or annotation gaps. gDNA drives intronic AND intergenic up together; an annotation gap drives only intergenic.
  • rRNA rate = the readout of poly-A-selection / rRNA-depletion efficiency (high = wasted reads, failed depletion).
  • Globin (HBA/HBB) crowds whole-blood PAXgene libraries -- deplete (GLOBINclear); globin% is the readout.
  • Mitochondrial %: high = degradation (bulk) or dying cells / ambient contamination (single-cell; in snRNA-seq it should be LOW).

Duplication and saturation -- diagnostic, not a remove step

bash
# Duplication as a DIAGNOSTIC only -- do NOT remove duplicates in non-UMI bulk RNA-seq
read_duplication.py -i aligned.bam -o dup                         # sequence- and mapping-based curves
junction_saturation.py -i aligned.bam -r genes.bed12 -o junc_sat  # enough depth for splicing?

Complete QC pipeline

Goal: Produce a per-sample RNA-seq QC summary covering strandedness, distribution, integrity, and Picard metrics.

Approach: Infer strandedness first, run the RSeQC suite, then Picard with STRAND_SPECIFICITY set to the inferred protocol, and append to one report (do NOT dedup).

bash
#!/bin/bash
set -euo pipefail
SAMPLE=$1; BAM=$2; BED12=$3; REFFLAT=$4; RRNA_INTERVALS=$5
STRAND=${6:-SECOND_READ_TRANSCRIPTION_STRAND}   # SECOND = dUTP/reverse (common); FIRST = forward; NONE = unstranded

REPORT="${SAMPLE}_rnaseq_qc.txt"
echo "=== RNA-seq QC: $SAMPLE ===" > "$REPORT"

echo "--- Strandedness (set downstream tools to match) ---" >> "$REPORT"
infer_experiment.py -i "$BAM" -r "$BED12" >> "$REPORT"

echo "--- Read distribution ---" >> "$REPORT"
read_distribution.py -i "$BAM" -r "$BED12" >> "$REPORT"

geneBody_coverage.py -i "$BAM" -r "$BED12" -o "${SAMPLE}_genebody"
tin.py -i "$BAM" -r "$BED12"                       # writes <bam>.summary.txt (mean/median TIN) + <bam>.tin.xls
echo "--- TIN (medTIN = median column of the summary) ---" >> "$REPORT"
cat *.summary.txt >> "$REPORT" 2>/dev/null

echo "--- Picard RNA-seq metrics (STRAND=$STRAND) ---" >> "$REPORT"
picard CollectRnaSeqMetrics I="$BAM" O="${SAMPLE}_picard.txt" \
    REF_FLAT="$REFFLAT" STRAND_SPECIFICITY="$STRAND" RIBOSOMAL_INTERVALS="$RRNA_INTERVALS"

cat "$REPORT"

The collapsed gene model

A standard GTF lists many overlapping isoforms per gene, so a read that is exonic in isoform A but intronic in B is ambiguous and overlapping isoforms double-count the same base. RNA-SeQC 2 REQUIRES a COLLAPSED model (one flattened transcript per gene, inter-gene overlaps excluded), built with GTEx collapse_annotation.py. Mismatched or un-collapsed models are a leading cause of "my exonic rate looks wrong". Picard PCT_* metrics are FRACTIONS (0-1), not percentages, despite the name.

Quantitative Thresholds

MetricAnchorSource / rationale
Mapping rate> 0.2 exclude below (GTEx); > 85% typicalGTEx v8 RNA-SeQC gate
Intergenic rate< 0.3GTEx; above = gDNA / annotation
rRNA rate< 0.3 (GTEx); <5% polyA, <10% depleted in practicedepletion efficiency
Uniquely mapped reads>= 30M (ENCODE human)ENCODE long-RNA standard
medTIN> 70 good, 50-70 moderate, < 50 poorRSeQC TIN
5'-to-3' biasnear 1 flat; > 2 strong degradationPicard MEDIAN_5PRIME_TO_3PRIME_BIAS

Thresholds are protocol-specific: an intronic rate that fails a poly-A bulk sample is normal/required for snRNA-seq (nuclei are >50% intronic); a 3' bias that condemns fresh poly-A is expected for FFPE. Apply cohort-relative outlier logic on top.

Common Errors

SymptomCauseSolution
Counts ~halved / huge "no feature" fractionWrong strandednessInfer first; set featureCounts/htseq/salmon/Picard to match
RNA-seq DE has odd length biasMarked duplicates on non-UMI bulk RNA-seqDo not dedup; report duplication as a diagnostic
Exonic rate looks wrong in RNA-SeQCUn-collapsed multi-isoform GTFUse a collapsed GTF (GTEx collapse_annotation.py)
Picard rRNA metric is 0/blankNo ribosomal_intervals suppliedBuild the interval list from rRNA features + BAM dict
snRNA-seq "fails" high intronic rateBulk gate applied to nuclear RNAIntronic reads are signal in snRNA; use an intron-inclusive reference
Picard percentages look 100x too smallPCT_* are fractions (0-1)Multiply by 100 for display

References

Wang L, Wang S, Li W. 2012. RSeQC: quality control of RNA-seq experiments. Bioinformatics 28(16):2184-2185. Okonechnikov K, Conesa A, Garcia-Alcalde F. 2016. Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data. Bioinformatics 32(2):292-294. Graubert A, Aguet F, Ravi A, Ardlie KG, Getz G. 2021. RNA-SeQC 2: efficient RNA-seq quality control and quantification for large cohorts. Bioinformatics 37(18):3048-3050. Schroeder A, Mueller O, Stocker S, et al. 2006. The RIN: an RNA integrity number for assigning integrity values to RNA measurements. BMC Molecular Biology 7:3. Wang L, Nie J, Sicotte H, et al. 2016. Measure transcript integrity using RNA-seq data. BMC Bioinformatics 17:58. Smith T, Heger A, Sudbery I. 2017. UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy. Genome Research 27(3):491-499.

read-qc/quality-reports - Raw-FASTQ QC before alignment read-qc/umi-processing - Molecule-accurate dedup for UMI RNA-seq read-qc/contamination-screening - rRNA and cross-species contamination read-alignment/star-alignment - Aligner that emits ReadsPerGene strandedness columns rna-quantification/featurecounts-counting - Strand-aware quantification after QC differential-expression/deseq2-basics - Use medTIN as a covariate in the design

© 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 3 other files in read-qc/rnaseq-qc of GPTomics/bioSkills.

  • SKILL.md
  • examples/check_rrna.sh
  • examples/rnaseq_qc.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.

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Categories

Questions about Bio Read Qc Rnaseq Qc

What does Bio Read Qc Rnaseq Qc do?

Runs RNA-seq-specific post-alignment QC - strandedness inference, gene-body 5'-3' coverage, read distribution (exonic/intronic/intergenic), rRNA/globin/mitochondrial rate, transcript integrity…. Bio Read Qc Rnaseq Qc is an agent skill from GPTomics/bioSkills. Runs RNA-seq-specific post-alignment QC - strandedness inference, gene-body 5'-3' coverage, read distribution (exonic/intronic/intergenic), rRNA/globin/mitochondrial rate, transcript integrity (TIN), and saturation - with RSeQC, Qualimap, RNA-SeQC, and Picard.

When should I use Bio Read Qc Rnaseq Qc?

Bio Read Qc Rnaseq Qc fits situations like: validating RNA-seq libraries before quantification; differential expression; diagnosing degradation; GDNA contamination.

How do I install Bio Read Qc Rnaseq Qc in Claude Code?

Run `npx skills add GPTomics/bioSkills --skill bio-read-qc-rnaseq-qc -a claude-code`. Or copy the skill folder (read-qc/rnaseq-qc in GPTomics/bioSkills) into .claude/skills/bio-read-qc-rnaseq-qc in your project. Claude Code loads it when a task matches its description.

How do I install Bio Read Qc Rnaseq Qc in Codex?

Run `npx skills add GPTomics/bioSkills --skill bio-read-qc-rnaseq-qc -a codex`. Or copy the skill folder (read-qc/rnaseq-qc in GPTomics/bioSkills) into .agents/skills/bio-read-qc-rnaseq-qc in your project. Codex loads it when a task matches its description.

Can I use Bio Read Qc Rnaseq Qc in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add GPTomics/bioSkills --skill bio-read-qc-rnaseq-qc -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bio-read-qc-rnaseq-qc, .gemini/skills/bio-read-qc-rnaseq-qc, .github/skills/bio-read-qc-rnaseq-qc and .opencode/skills/bio-read-qc-rnaseq-qc in your project.

What does Bio Read Qc Rnaseq Qc need to run?

Going by SKILL.md and its folder, Bio Read Qc Rnaseq Qc needs a shell for the scripts in its folder and the command-line tools its instructions call (pip). Our summary lists: A Bash shell.

Does Bio Read Qc Rnaseq Qc access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Bio Read Qc Rnaseq Qc safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Bio Read Qc Rnaseq Qc use?

Bio Read Qc Rnaseq Qc is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Bio Read Qc Rnaseq Qc use?

About 3.4k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Bio Read Qc Rnaseq Qc?

Skills that share tags, products or a category with Bio Read Qc Rnaseq Qc: Alphagenome Single Variant Analysis (google-deepmind/science-skills, 3.2k stars), 13C Metabolic Flux Analysis (K-Dense-AI/scientific-agent-skills, 48k stars), Clinvar Database (google-deepmind/science-skills, 3.2k stars) and Metabolic Study Planner (aiming-lab/AutoResearchClaw, 15k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Bio Read Qc Rnaseq Qc?

GPTomics (a GitHub organization) maintains it in GPTomics/bioSkills, which has 1,218 GitHub stars. The repository holds 559 skills in this directory. The repository was last updated on August 15, 2026.

Source: GPTomics/bioSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.