# Bioconductor Cogeqc

Cogeqc aims to facilitate systematic quality checks on standard comparative genomics...

- Repository: https://github.com/bioMate-AI/biomate-bioconductor-kb (793 stars)
- Category: Engineering
- License: see repository
- Last updated: 2026-06-20
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-cogeqc -g -a claude-code -y`
Codex: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-cogeqc -g -a codex -y`
Cursor: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-cogeqc -g -a cursor -y`
Gemini CLI: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-cogeqc -g -a gemini-cli -y`

## Description

cogeqc aims to facilitate systematic quality checks on standard comparative genomics analyses to help researchers detect issues and select the most suitable parameters for each data set. cogeqc can be used to asses: i. genome assembly and annotation quality with BUSCOs and comparisons of statistics with publicly available genomes on the NCBI; ii. orthogroup inference using a protein domain-based approach and; iii. synteny detection using synteny network properties. There are also data visualizat

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# cogeqc

## Workflows

### Standard Workflow

Assess genome assembly and annotation quality by comparing custom statistics against NCBI genomes and evaluating gene space completeness with BUSCO.

```r
library(cogeqc)

# 1. Retrieve and compare genome assembly statistics
maize_stats <- get_genome_stats(taxon = "Zea mays")
my_stats <- data.frame(
    accession = "my_lovely_maize",
    sequence_length = 2.4 * 1e9,
    gene_count_total = 50000,
    CC_ratio = 2
)
comparison <- compare_genome_stats(ncbi_stats = maize_stats, user_stats = my_stats)
plot_genome_stats(ncbi_stats = maize_stats, user_stats = my_stats)

# 2. Read and plot BUSCO summary statistics
output_dir <- system.file("extdata", package = "cogeqc")
busco_summary <- read_busco(output_dir)
plot_busco(busco_summary)
```

*Input: Custom genome assembly statistics and BUSCO output directories; Output: Quality control comparison tables and summary visualization plots.*

## When to Use
- Assessing genome assembly and annotation quality by comparing custom statistics against NCBI genomes using `get_genome_stats`, `compare_genome_stats`, and `plot_genome_stats`.
- Assessing gene space completeness using BUSCO via `run_busco`, `read_busco`, and `plot_busco`.
- Assessing orthogroup inference (e.g., from OrthoFinder or OrthoMCL) using a protein domain-based approach with `read_orthogroups` and `assess_orthogroups`.

## When NOT to Use
- For performing the actual genome assembly, gene annotation, or orthogroup clustering itself. Use external tools like Flye, Liftoff, or OrthoFinder, then use `cogeqc` for downstream quality control.

The rest: https://github.com/bioMate-AI/biomate-bioconductor-kb/blob/HEAD/skills/genomics/cogeqc/SKILL.md