multiqc
Create aggregate bioinformatics analysis reports across many samples and tools
Decision gist · record as of 2026-08-14
Yes. MultiQC is actively maintained, has low install friction, and solves a concrete problem for bioinformatics workflows. The GPLv3 license is standard in research contexts. The 26 dependencies are substantial but well-established (plotly, numpy, pydantic, polars). Install it if you regularly analyze multiple bioinformatics samples and need unified QC reporting; skip it if you work with single-sample analyses or already have a custom reporting pipeline.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low install friction with a pure-Python wheel.
- Active maintenance with recent commits and 1486 GitHub stars.
- Supports current Python versions (3.9+).
License · maintenance · safety
copyleft license (copyleft) — GPLv3 copyleft license: any derivative work or distribution must also be released under GPLv3. Suitable for research and open-source pipelines; verify compatibility if integrating into proprietary workflows.
last release 2026-05-13 (93 days) · last repo commit 2026-08-12 · 1,486 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 83,223 downloads/mo, #14,091 on PyPI
Alternatives
Verify before relying
pip install multiqc
multiqc .
# Generates multiqc_report.html and multiqc_data/ directory- Whether the 26 runtime dependencies (including plotly, pydantic, polars, pyarrow) are all required for basic operation or if some are optional for specific modules
- Performance characteristics when processing very large sample sets or complex nested directory structures
What it is and what it does
MultiQC is a command-line tool that aggregates bioinformatics analysis results from many samples into a single interactive report. It works by scanning specified directories for recognized log files from common bioinformatics tools (FastQC, Bowtie, STAR, and many others), parsing them, and generating an HTML report with interactive plots and summary statistics. The tool also produces tab-delimited data files for further inspection.
The package is designed for routine quality control in sequencing pipelines, allowing researchers to assess results across large sample batches at a glance. It supports custom content via configuration, has an extensible module system, and runs on Unix-like systems and macOS. With 26 runtime dependencies including plotly for visualization, numpy for data handling, and pydantic for validation, it provides a complete reporting pipeline for bioinformatics workflows.
Use it for
- Generate QC reports after running FastQC on hundreds of sequencing samples in a single command
- Aggregate alignment statistics from multiple STAR or Bowtie2 runs into one comparative report
- Create summary dashboards for variant calling pipelines combining results from multiple tools
- Parse custom bioinformatics script output and include it in standardized reports via Custom Content
- Monitor batch processing quality across different sequencing runs or experimental conditions
- Export aggregated statistics in YAML or JSON format for downstream analysis or archival
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
MultiQC is actively maintained, has low install friction, and solves a concrete problem for bioinformatics workflows. The GPLv3 license is standard in research contexts. The 26 dependencies are substantial but well-established (plotly, numpy, pydantic, polars). Install it if you regularly analyze multiple bioinformatics samples and need unified QC reporting; skip it if you work with single-sample analyses or already have a custom reporting pipeline.
Install
multiqc on PyPI
Before you install
Low install friction with a pure-Python wheel. Active maintenance with recent commits and 1486 GitHub stars. Supports current Python versions (3.9+).
License in practice
GPLv3 copyleft license: any derivative work or distribution must also be released under GPLv3. Suitable for research and open-source pipelines; verify compatibility if integrating into proprietary workflows.
Quickstart
pip install multiqc
multiqc .
# Generates multiqc_report.html and multiqc_data/ directoryVerify before relying
- Whether the 26 runtime dependencies (including plotly, pydantic, polars, pyarrow) are all required for basic operation or if some are optional for specific modules
- Performance characteristics when processing very large sample sets or complex nested directory structures
Package facts
| License | copyleft license copyleft |
| Python support | Supports the current Python release !=3.14.1,>=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 26 packagesboto3clickhumanizeimportlib_metadatajinja2kaleidomarkdownnumpypackagingrequestsPillowplotlypyyamlrichrich-clickcoloredlogsspectrapydantictypeguardtqdmpython-dotenvnatsorttiktokenjsonschemapolarspyarrow |
| Maintenance | Actively maintained 93 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 83,223 / month, #14,091 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableEnvironment :: ConsoleEnvironment :: Web EnvironmentIntended Audience :: Science/ResearchLicense :: OSI Approved :: GNU General Public License v3 (GPLv3)Natural Language :: EnglishOperating System :: MacOS :: MacOS XOperating System :: POSIXOperating System :: UnixProgramming Language :: JavaScriptProgramming Language :: PythonTopic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Bio-InformaticsTopic :: Scientific/Engineering :: Visualization |
Evidence: multiqc-1.35-py3-none-any.whl
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