EasyFuse is a pipeline to detect fusion transcripts from RNA-seq data with high accuracy. EasyFuse uses five fusion gene detection tools, STAR-Fusion, InFusion, MapSplice2, Fusioncatcher, and SoapFuse along with a powerful read filtering strategy, stringent re-quantification of supporting reads and machine learning for highly accurate predictions.
- Documentation: EasyFuse Wiki
- Paper: https://doi.org/10.1038/s41587-022-01247-9
We recommend using EasyFuse with the Docker container.
The Docker image can be downloaded from dockerhub using the following command:
docker pull tronbioinformatics/easyfuse:1.3.4
Before running EasyFuse the following reference annotation data needs to be downloaded (~92 GB).
# Download reference archive
wget ftp://easyfuse.tron-mainz.de/easyfuse_ref_v2.tar.gz
wget ftp://easyfuse.tron-mainz.de/easyfuse_ref_v2.tar.gz.md5
# Check MD5 sums for consistency
md5sum -c easyfuse_ref_v2.tar.gz.md5 easyfuse_ref_v2.tar.gz
# Extract reference archive
tar xvfz easyfuse_ref_v2.tar.gz
EasyFuse will require three folders:
- The input data folder containing FASTQ files, in this example
/path/to/input_data. - The reference data folder, in this example
/path/to/easyfuse_ref - The output folder, in this example
/path/to/output
Now EasyFuse can be started by mapping the input data, references and output folders.
docker run \
--name easyfuse_container \
-v </path/to/easyfuse_ref>:/ref \
-v </path/to/data>:/data \
-v </path/to/output>:/output \
--rm \
-it easyfuse:1.3.4 \
python /code/easyfuse-1.3.4/processing.py -i /data -o /output
The output can be found in </path/to/output>/results/. The Output format is described in the wiki page EasyFuse Output
The EasyFuse pipeline depends on multiple external fusion prediction tools and other dependencies. For example:
- STAR (2.6.1d)
- STAR-Fusion (2.6.1d)
- Fusioncatcher(1.00)
- MapSplice2 (2.2.1)
- InFusion (0.8)
- SOAPfuse (1.2.7)
The custom installation of EasyFuse is described in the EasyFuse Wiki