Skip to main content
Warning: You are using the test version of PyPI. This is a pre-production deployment of Warehouse. Changes made here affect the production instance of TestPyPI (
Help us improve Python packaging - Donate today!

Tool for identifying transposon insertions in Insertional Mutagenesis screens from gene-transposon fusions using single- and paired-end RNA-sequencing data.

Project Description


IM-Fusion is a tool for identifying transposon insertion sites in insertional mutagenesis screens using single- and paired-end RNA-sequencing data. It essentially identifies insertion sites from gene-transposon fusions in the RNA-sequencing data, which represent splicing events between the transposon and endogeneous genes.

IM-Fusion also identifies candidate genes for a given screen using a statistical test (based on the Poisson distribution) that identifies Commonly Targeted Genes (CTGs) – genes that are more frequently affected by insertions than would be expected by chance. To further narrow down a list of CTGs, which may contain hundreds of genes, IM-Fusion also tests if insertions in a CTG have a significant effect on the expression of the gene, which is a strong indicator of them having an actual biological effect.

IM-Fusion has the following key features:

  • It identifies transposon insertion sites from both single- and paired-end RNA-sequencing data, without having any special sequencing requirements.
  • It uses a gene-centric approach – both for the identification of insertions and for testing of differential expression for identified candidate genes – which greatly reduces the number of false positive candidate genes.
  • It implements several exon-level and gene-level differential expression tests, which provide detailed insight into the effects of insertions on the expression of their target gene(s). By providing both a group-wise and a single-sample version of the test, IM-Fusion can identify effects for a single insertion in a specific sample, or determine the general effect of insertions on a given gene within the tumor cohort.

For more details on the approach and a comparison with existing methods, please see our manuscript.


IM-Fusion’s documentation is available at


de Ruiter, JR. et al., 2017. “Identifying transposon insertions and their effects from RNA-sequencing data” (Under revision).


This software is released under the MIT license.


0.3.0 (2017-05-04)

  • Refactored external tools into the imfusion.external module.
  • Use docker/tox for testing against multiple Python versions locally.
  • Added additional checks for inputs and improved error messages.
  • Added support for DataFrame insertion inputs to DE testing functions.
  • Added building of exon gtf as part of imfusion-build.
  • Added identification of endogenous fusions using STAR-Fusion as part of imfusion-insertions (using STAR). Also adds script for building (murine) STAR-Fusion references.
  • Made matplotlib/seaborn lazy imports that are only required when actually using the plotting functions. This makes IM-Fusion easier to use on headless servers/HPCs.

0.2.0 (2017-03-09)

  • Added support for the STAR aligner.
  • Added detection of novel transcripts using StringTie.
  • Changed reference building to generate a self-contained reference.
  • Refactored differential expression tests + added gene-level test.

0.1.0 (2016-03-26)

  • First release on GitHub.
Release History

Release History

This version
History Node


Download Files

Download Files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
imfusion-0.3.0-py3-none-any.whl (73.5 kB) Copy SHA256 Checksum SHA256 py3 Wheel May 5, 2017
imfusion-0.3.0.tar.gz (141.5 kB) Copy SHA256 Checksum SHA256 Source May 5, 2017

Supported By

WebFaction WebFaction Technical Writing Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Rackspace Rackspace Cloud Servers DreamHost DreamHost Log Hosting