Metadata-Version: 1.0
Name: imfusion
Version: 0.3.0
Summary: Tool for identifying transposon insertions in Insertional Mutagenesis screens from gene-transposon fusions using single- and paired-end RNA-sequencing data.
Home-page: https://github.com/jrderuiter/im-fusion
Author: Julian de Ruiter
Author-email: julianderuiter@gmail.com
License: MIT license
Description: .. image:: https://img.shields.io/travis/jrderuiter/imfusion/develop.svg
            :target: https://travis-ci.org/jrderuiter/imfusion
        
        .. image:: https://img.shields.io/coveralls/jrderuiter/imfusion/develop.svg
            :target: https://coveralls.io/github/jrderuiter/imfusion
        
        IM-Fusion
        =========
        
        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.
        
        Documentation
        =============
        
        IM-Fusion's documentation is available at
        `jrderuiter.github.io/imfusion <http://jrderuiter.github.io/imfusion/>`_.
        
        References
        ==========
        de Ruiter, JR. *et al.*, 2017. **"Identifying transposon insertions and
        their effects from RNA-sequencing data"** (*Under revision*).
        
        License
        =======
        
        This software is released under the MIT license.
        
        
        =======
        History
        =======
        
        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.
        
Platform: UNKNOWN
