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Gokegg Analysis

OrganizationPopular
aipoch
gokegg-analysis

Use when performing GO and KEGG enrichment on a gene list from bulk RNA-seq or microarray studies, then generating a combined GO/KEGG dot chart. NOT for single-cell RNA-seq, methylation data, or non-expression data.

Overview

Publisheraipoch
Repositorymedical-research-skills
Skill namegokegg-analysis
Stars
1.9K
Forks
175
Bundled files
11
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 11 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by aipoch on GitHub. Read the source before you install it.

Installation

Install the Gokegg Analysis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Use it in TypingMind

Enable Gokegg Analysis in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Gokegg Analysis on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Gokegg Analysis is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Source: https://github.com/aipoch/medical-research-skills

When To Read External Files

SituationFile To ReadPurpose
Need algorithm detailsreferences/algorithm.mdStatistical methods and formulas
Need to run the analysisscripts/main.RFull execution command
Encounter an errorreferences/troubleshooting.mdTroubleshooting guidance
Need CLI examplesreferences/cli-guide.mdParameter usage examples

When To Use

Use this skill for:

  • GO and KEGG enrichment from a gene list derived from bulk RNA-seq or microarray studies
  • Supported gene ID types: SYMBOL, ENSEMBL, ENTREZID
  • Supported species databases: org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db

Do not use this skill for:

  • Single-cell RNA-seq analysis
  • Methylation, proteomics, or non-expression omics workflows
  • Differential expression testing from raw count matrices

Usage

Main analysis and plotting: Rscript scripts/main.R --feature "TP53,EGFR,BRCA1,MYC" --output_dir ./output --sp org.Hs.eg.db --gene_type SYMBOL --pvalue_cutoff 0.05 --qvalue_cutoff 0.2 --pAdjustMethod BH --seed 66 --go_top_n 3 --kegg_top_n 3 --format pdf

Notes:

  • scripts/main.R is the only command-line entry point
  • scripts/dochart.R currently provides plotting functions and is sourced by scripts/main.R
  • If --go_input, --kegg_input, or --outdir are omitted, main.R uses output_dir/temp/GO_list.rda, output_dir/temp/KEGG_list.rda, and output_dir/plot automatically

Agent Output

On success, the agent should report:

  • Whether GO enrichment completed successfully
  • Whether KEGG enrichment completed successfully
  • The normalized input gene count after trimming and parsing
  • The main output directory
  • The generated files, especially GO_df.csv, KEGG_df.csv, GO_list.rda, KEGG_list.rda, and the combined dot chart
  • The path to session_info.txt

Post-run checklist:

  • Re-parse the original --feature string using the documented separator rules and report the deduplicated gene count after trimming
  • Check temp/GO_df.csv and temp/GO_list.rda before claiming GO success
  • Check temp/KEGG_df.csv and temp/KEGG_list.rda before claiming KEGG success
  • Check plot/gokegg_dot_chart.<format>, plot/gokegg_dot_chart_data.csv, plot/gokegg_dot_chart_data.rda, and session_info.txt before claiming full success
  • Summarize the final result with: parsed gene count, GO status, KEGG status, plot status, output directory, and key output files

On failure, the agent should report:

  • The exact SKILL_* error code
  • The failing step, such as gene parsing, ID conversion, enrichment, or plotting
  • The actionable next step, such as fixing input IDs, checking missing packages, or regenerating .rda files

Parameter Reference

scripts/main.R

ShortLongTypeDefaultRequiredDescription
-f--featurecharacter""YesGene list separated by commas, Chinese commas, semicolons, tabs, or newlines
-o--output_dircharacter./output/NoMain output directory
-s--spcharacterorg.Hs.eg.dbNoSpecies database
-g--gene_typecharacterSYMBOLNoInput gene ID type
-p--pvalue_cutoffnumeric0.05NoEnrichment p-value cutoff
-q--qvalue_cutoffnumeric0.2NoEnrichment q-value cutoff
-m--pAdjustMethodcharacterBHNoP-value adjustment method
--seedinteger66NoRandom seed
--go_inputcharacterNULLNoOptional GO .rda; defaults to output_dir/temp/GO_list.rda
--kegg_inputcharacterNULLNoOptional KEGG .rda; defaults to output_dir/temp/KEGG_list.rda
--outdircharacterNULLNoPlot output directory; defaults to output_dir/plot
--go_top_nnumeric3NoTop GO terms per ontology
--kegg_top_nnumeric3NoTop KEGG pathways
-w--widthnumeric20NoPlot width in cm
--heightnumeric16NoPlot height in cm
--formatcharacterpdfNoPlot format: pdf, png, svg
--dpinumeric300NoDPI for raster output
-c--colorscharacter#E41A1C,#FFFF33,#2E86AB,#4DAF4ANoColors for GO:BP,GO:CC,GO:MF,KEGG
--titlecharacterGO + KEGG Dot ChartNoPlot title
--xlabcharacterNULLNoHorizontal axis label override
--ylabcharacterNULLNoVertical axis label override
--dot_sizenumeric4.5NoDot size
--shapenumeric19NoDot shape
--rotate / --no-rotatelogical flagTRUENoRotate plot orientation on or off
--sortingcharacterdescendingNoDot sorting order
--label_widthnumeric35NoLabel wrap width
--title_sizenumeric12NoTitle font size
--axis_title_sizenumeric9NoAxis title font size
--axis_text_sizenumeric8NoAxis text font size
--legend_title_sizenumeric8NoLegend title font size
--legend_text_sizenumeric7NoLegend text font size
--legend_positioncharactertopNoLegend position
--plot_margincharacter10,10,10,10NoPlot margins: top,right,bottom,left
--axis_line_sizenumeric0.5NoAxis line width
--axis_ticks_sizenumeric0.5NoAxis tick width
--show_gridlogicalFALSENoShow grid lines
-v--verboselogicalFALSENoEnable verbose logging

Input Format

Main Analysis Input

  • --feature should be provided as a gene list
  • Preferred separator: comma
  • Also accepted: Chinese commas, semicolons, tabs, and newlines
  • Leading and trailing spaces around each gene are removed automatically with trimming
  • The gene ID type must match --gene_type
  • --sp supports only org.Hs.eg.db, org.Mm.eg.db, and org.Rn.eg.db

Examples: TP53,EGFR,BRCA1,MYC

TP53, EGFR, BRCA1, MYC

TP53;EGFR;BRCA1;MYC

TP53\nEGFR\nBRCA1\nMYC

Example command with minimal input: Rscript scripts/main.R --feature "TP53,EGFR,BRCA1,MYC" --output_dir ./example_output --sp org.Hs.eg.db --gene_type SYMBOL

Example command with custom plotting parameters: Rscript scripts/main.R --feature "TP53,EGFR,BRCA1,MYC" --output_dir ./example_plot_output --sp org.Hs.eg.db --gene_type SYMBOL --go_top_n 5 --kegg_top_n 8 --colors "#E41A1C,#FFFF33,#2E86AB,#4DAF4A" --title "Custom GO + KEGG Dot Chart" --xlab="-log10(adjusted p-value)" --ylab="Enriched Terms" --width 24 --height 18 --label_width 40 --format png --dpi 300 --no-rotate --verbose

Note: values passed to --xlab or --ylab that start with - should use --option=value syntax to avoid being parsed as flags.

Note: separator variants are supported only when they are passed inside a single --feature argument value.

Plot Input

  • Plotting is triggered by scripts/main.R
  • --go_input: optional .rda file containing a GO_list object
  • --kegg_input: optional .rda file containing a KEGG_list object
  • If not provided, main.R uses the newly generated files under output_dir/temp
  • Plotting requires result tables with at least Description and p.adjust

Output Files

File NameFormatDescription
temp/GO_df.csvCSVGO enrichment result table
temp/GO_list.rdaRDAFull GO enrichment object
temp/KEGG_df.csvCSVKEGG enrichment result table
temp/KEGG_list.rdaRDAFull KEGG enrichment object
plot/gokegg_dot_chart.pdf etc.PDF/PNG/SVGCombined GO/KEGG dot chart
plot/gokegg_dot_chart_data.csvCSVCombined plotting table used for the figure
plot/gokegg_dot_chart_data.rdaRDAPlot bundle with plotting data and parameters
session_info.txtTXTRuntime session information

Error Handling

Common error codes and fixes:

  • SKILL_FILE_NOT_FOUND: Input file does not exist; check the path and permissions
  • SKILL_FILE_FORMAT_ERROR: .rda cannot be read or is malformed; regenerate upstream results
  • SKILL_MISSING_COLUMNS: Result table is missing Description or p.adjust
  • SKILL_EMPTY_DATA: Input genes are empty after parsing, cannot be converted, or enrichment results are empty
  • SKILL_INVALID_PARAMETER: Required parameter missing, unsupported species, or insufficient color count
  • SKILL_PACKAGE_NOT_FOUND: Required package is not installed
  • SKILL_ANALYSIS_FAILED: Internal GO/KEGG enrichment failure; verify gene_type, sp, and input genes

For detailed troubleshooting, read references/troubleshooting.md.

Testing

Minimal test dataset: use a small built-in gene list directly, with no extra files required.

Smoke test command: Rscript scripts/main.R --feature "TP53,EGFR,BRCA1,MYC" --output_dir ./test_output --sp org.Hs.eg.db --gene_type SYMBOL --pvalue_cutoff 0.05 --qvalue_cutoff 0.2 --pAdjustMethod BH --seed 66 --go_top_n 3 --kegg_top_n 3 --format pdf --verbose

Expected smoke-test outputs:

  • ./test_output/temp/GO_list.rda
  • ./test_output/temp/KEGG_list.rda
  • ./test_output/temp/GO_df.csv
  • ./test_output/temp/KEGG_df.csv
  • ./test_output/plot/gokegg_dot_chart.pdf
  • ./test_output/plot/gokegg_dot_chart_data.csv
  • ./test_output/plot/gokegg_dot_chart_data.rda
  • ./test_output/session_info.txt
  • Exit status code 0

Automated regression script: Rscript test/test_regressions.R

The regression script covers:

  • Separator parsing with comma, Chinese semicolon, newline, tab, and mixed separators
  • Empty parsed-gene handling
  • Invalid --plot_margin validation
  • Plot input validation for missing GO/KEGG inputs

Separator examples for manual CLI verification: Rscript scripts/main.R --feature "TP53,EGFR,BRCA1,MYC" --output_dir ./test_sep_comma Rscript scripts/main.R --feature "TP53;EGFR;BRCA1;MYC" --output_dir ./test_sep_cn_semicolon Rscript scripts/main.R --feature $'TP53\nEGFR\nBRCA1\nMYC' --output_dir ./test_sep_newline Rscript scripts/main.R --feature $'TP53\tEGFR\tBRCA1\tMYC' --output_dir ./test_sep_tab Rscript scripts/main.R --feature $'TP53; EGFR, BRCA1 MYC' --output_dir ./test_sep_mixed

Note: all separators must be passed inside a single --feature argument value.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Gokegg Analysis AI skill do?

Use when performing GO and KEGG enrichment on a gene list from bulk RNA-seq or microarray studies, then generating a combined GO/KEGG dot chart. NOT for single-cell RNA-seq, methylation data, or non-expression data.

Why use Gokegg Analysis on TypingMind?

Because you install it once and use it with any model. Gokegg Analysis is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Gokegg Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aipoch/medical-research-skills/tree/main/awesome-med-research-skills/Data%20Analysis/gokegg. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Gokegg Analysis?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Gokegg Analysis?

As many as you like. As long as a model supports skills, you can use Gokegg Analysis with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Gokegg Analysis AI skill free?

Yes. It is published on GitHub by aipoch under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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