Tool for identifying transposon insertions in Insertional Mutagenesis screens from gene-transposon fusions using single- and paired-end RNA-sequencing data.
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 jrderuiter.github.io/imfusion.
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.
- 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.
- 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.
- First release on GitHub.
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