Skip to main content

fusilli-multiqc

Custom MultiQC modules for the FUSILLI fusion library pipeline.

This package is made to be run with the FUSILLI pipeline: https://github.com/user/fusilli. It provides metrics and visualizations specific to the pipeline's outputs.

  • Detection Metrics: Detection efficiency, library coverage, and sensitivity analysis
  • Diversity Metrics: Library diversity metrics
  • Processing Metrics: Read and base retention and loss through processing steps
  • Partner Detection: Partner detection metrics and coverage across samples

Installation

Through FUSILLI

The FUSILLI pipeline uses a conda environment to install multiqc and this module. You should not need to install this module separately if you are using FUSILLI.

Through pip

pip install fusilli-multiqc

Requirements

  • Python >= 3.9
  • MultiQC >= 1.25
  • pandas >= 1.0
  • numpy >= 1.0

Usage

After installation, the modules will be automatically discovered by MultiQC when you run:

multiqc <results_directory>

The modules will automatically detect and parse the following FUSILLI output files:

  • fusion_qc_metrics.csv - Detection efficiency and coverage metrics
  • sensitivity_metrics.csv - Sensitivity analysis data
  • fusion_counts_summary.csv - Fusion count data for diversity analysis
  • decay_metrics.csv - Read decay through preprocessing steps
  • trim_metrics.csv, contam_metrics.csv, quality_metrics.csv - Per-filter read/base removal
  • unmerged_qc_metrics.csv - Pre-merge (unmerged R1/R2) detection metrics
  • partner_counts_summary.csv - Partner detection counts

Module Descriptions

Detection Metrics Module (fusilli_detection)

Visualizes:

  • Detection Efficiency: Fraction of processed reads that matched fusion sequences
  • Prefilter Efficiency: Fraction of reads containing partner domain 3' ends
  • Matching Efficiency: Fraction of prefiltered reads that matched specific breakpoints
  • Library Coverage: Variant, breakpoint, and partner coverage metrics
  • Sensitivity Analysis: Sensitivity index vs expected detection fraction
  • Unmerged Read QC: Detection counts recovered from pre-merge R1/R2 reads

Diversity Metrics Module (fusilli_diversity)

Visualizes:

  • Shannon Diversity: Measures both richness and evenness
  • Simpson Diversity: Measures dominance
  • Evenness: Pielou's evenness index
  • Top N Fractions: Distribution of counts across top variants
  • Variant Count Distribution: Histogram of variant abundances

Preprocessing Metrics Module (fusilli_preprocessing)

Visualizes:

  • Read Decay: Read counts at each preprocessing step (log scale)
  • Retention Rates: Fraction of reads retained after each step
  • Step Loss Breakdown: Cumulative loss of reads through preprocessing
  • Filtering Removal: Fraction of reads removed by the contaminant, adapter-trim, and quality filters

Partner Detection Module (fusilli_partners)

Visualizes:

  • Partner Detection Heatmap: Binary detection matrix of partners across samples
  • Partner Coverage: Number of samples with detection per partner
  • Partner End vs Linker: Comparison of detection methods

License

MIT License - see LICENSE file for details.

Metadata

Release files for fusilli-multiqc 1.1.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for fusilli-multiqc 1.1.0
File Size Uploaded Provenance
fusilli_multiqc-1.1.0.tar.gz 26.5 kB With provenance Details

Built distribution (wheel)

Table of built distributions (wheels) for fusilli-multiqc 1.1.0
File Interpreter ABI Platform
fusilli_multiqc-1.1.0-py3-none-any.whl Python 3 none any Details

Total release size: 49.5 kB

Release files / fusilli_multiqc-1.1.0.tar.gz

Download URL fusilli_multiqc-1.1.0.tar.gz
Size 26.5 kB
Tags Source
SHA-256 checksum
How to use checksums
4617be1f4c95c526023d3a1ad85d56df46f72d69be0bed5c52faa2986b48fd5e
BLAKE2b-256 checksum
How to use checksums
ee980527e3d94cd3d5fdeaacd6498139012ad57f63efa020ac9537465b5f973a
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.12

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Jun 19, 2026.

Transparency log

Release files / fusilli_multiqc-1.1.0-py3-none-any.whl

Download URL fusilli_multiqc-1.1.0-py3-none-any.whl
Size 23.0 kB
Tags Python 3
SHA-256 checksum
How to use checksums
ea02c8f39c718c8bf6e98cb08c9723da948a7e320a9e1ae0fa32c7ed235d0fea
BLAKE2b-256 checksum
How to use checksums
821b76d40a44830ba618ba6c1ea6d730476f8da974a019376a12005d5a338a47
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.12

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Jun 19, 2026.

Transparency log

Security for fusilli-multiqc 1.1.0

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

Full provenance

All 2 files in this release have been uploaded with provenance metadata.

Select from 2 files

Attestation for fusilli_multiqc-1.1.0.tar.gz

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Jun 19, 2026.

Transparency log

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

Release history Release notifications | RSS feed

This release

1.1.0 This release

2 release files Full provenance

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page