Busco Status Interpretation

BUSCO Status Interpretation Guide

Overview

BUSCO (Benchmarking Universal Single-Copy Orthologs) is the standard tool for assessing genome, transcriptome, and proteome completeness by searching for conserved single-copy orthologs from the OrthoDB database. Correct interpretation of BUSCO output is essential for genome quality assessment, comparative genomics, and publication-ready reporting. The most common analytical error is excluding Duplicated BUSCOs from completeness counts, which artificially penalizes polyploid organisms and assemblies with legitimate gene duplications.

This guide covers BUSCO status categories, output file formats, parsing strategies, cross-proteome comparisons, lineage dataset selection, and common pitfalls in BUSCO interpretation.

Key Concepts

BUSCO Status Categories

BUSCO assigns each searched ortholog one of four statuses:

The headline completeness percentage (C%) reported by BUSCO is always S + D combined. Individual category counts (S, D, F, M) are reported for transparency and should be included in publications.

Why Duplicated Equals Complete

A Duplicated BUSCO means the ortholog IS present and fully intact in the genome or proteome -- it simply exists in more than one copy. This can occur through:

• Whole-genome duplication (common in plants, fish, and amphibians)
• Tandem or segmental duplication events
• Recent polyploidy
• Proteomes containing multiple isoforms per gene

The gene is not incomplete or absent. Excluding Duplicated BUSCOs from completeness counts would incorrectly penalize polyploid organisms, recently duplicated genomes, or proteomes that include isoform-level annotations. The correct completeness formula is always:

Completeness (%) = (Complete_single_copy + Duplicated) / Total_BUSCOs * 100

A high Duplicated fraction is not inherently problematic -- it is biologically informative. For example, the zebrafish genome (a teleost with an ancient whole-genome duplication) routinely shows 15-25% Duplicated BUSCOs, and this is expected.

BUSCO Output Formats

BUSCO produces two primary output formats relevant to downstream analysis:

Short summary format -- a single-line notation found in short_summary.*.txt:

C:95.0%[S:90.0%,D:5.0%],F:3.0%,M:2.0%,n:255

Where C = Complete (S + D), S = Single-copy, D = Duplicated, F = Fragmented, M = Missing, and n = total BUSCO groups searched.

Full table format -- a TSV file (full_table.tsv) with per-ortholog results containing columns for BUSCO ID, Status, Sequence, Score, and Length. This file enables detailed per-gene analysis, filtering, and cross-species comparisons.

Decision Framework

When deciding whether and how to use BUSCO for quality assessment:

Question: What are you assessing?
├── Genome assembly completeness
│   ├── Draft assembly → Run BUSCO in genome mode
│   └── Polished/final assembly → Run BUSCO in genome mode, report in publication
├── Transcriptome completeness
│   └── De novo assembly → Run BUSCO in transcriptome mode (expect higher D%)
├── Proteome / annotation completeness
│   └── Predicted proteins → Run BUSCO in protein mode
└── Comparing multiple assemblies
    └── Same lineage dataset across all → Use compare_proteome_completeness pattern

Lineage Dataset Selection

General rule: Use the most specific lineage dataset that encompasses your organism. More specific datasets contain more BUSCOs and provide higher resolution, but using a dataset that does not include your organism will produce misleadingly low scores.

Best Practices

1. Always report all four categories (S, D, F, M): Do not report only the headline C% value. Reviewers and readers need the breakdown to assess whether high completeness comes from single-copy genes (expected for haploid organisms) or duplicated genes (expected for polyploids). This is now a standard expectation in genome papers.

2. Use the same lineage dataset for all comparisons: When comparing assemblies or proteomes, every run must use the identical lineage dataset and BUSCO version. Mixing lineage datasets (e.g., comparing one assembly run with eukaryota_odb10 against another with metazoa_odb10) produces incomparable results.

3. Choose the most specific lineage available: More specific lineage datasets provide more BUSCO markers and finer resolution. A vertebrate genome assessed with eukaryota_odb10 (255 markers) gives a much coarser picture than one assessed with mammalia_odb10 (9,226 markers).

4. Interpret Duplicated percentage in biological context: High D% in plants, teleost fish, or salmonids is expected due to known whole-genome duplication events. High D% in a haploid bacterium, however, may indicate assembly artifacts (e.g., uncollapsed haplotypes or contamination).

5. Run BUSCO on the correct input type: Use genome mode for assemblies (FASTA of contigs/scaffolds), transcriptome mode for de novo transcriptome assemblies, and protein mode for predicted proteomes. Using the wrong mode produces misleading results because BUSCO applies different search strategies for each.

6. Include BUSCO version and dataset in methods sections: Reproducibility requires reporting the exact BUSCO version, OrthoDB dataset version, and any non-default parameters used. Example: "Completeness was assessed with BUSCO v5.4.7 using the mammalia_odb10 dataset."

7. Validate with BUSCO's built-in plotting: Use generate_plot.py to create the standard BUSCO stacked bar chart for visual comparison across assemblies. This standardized visualization is widely recognized by reviewers.