GeneFuse

A tool to detect and visualize target gene fusions by scanning FASTQ files directly. This tool accepts FASTQ files and reference genome as input, and outputs detected fusion results in TEXT, JSON and HTML formats.

Take a quick glance of the informative report

Sample HTML report: http://opengene.org/GeneFuse/report.html
Sample JSON report: http://opengene.org/GeneFuse/report.json
Dataset for testing: http://opengene.org/dataset.html Please download the paired-end FASTQ files for GeneFuse testing (Illumina platform)

Get genefuse program

install with Bioconda

conda install -c bioconda genefuse

download binary

This binary is only for Linux systems, http://opengene.org/GeneFuse/genefuse

# this binary was compiled on CentOS, and tested on CentOS/Ubuntu
wget http://opengene.org/GeneFuse/genefuse
chmod a+x ./genefuse

or compile from source

# get source (you can also use browser to download from master or releases)
git clone https://github.com/OpenGene/genefuse.git

# build
cd genefuse
make

# Install
sudo make install

Usage

You should provide following arguments to run genefuse

the reference genome fasta file, specified by -r or --ref=
the fusion setting file, specified by -f or --fusion=
the fastq file(s), specified by -1 or --read1= for single-end data. If dealing with pair-end data, specify the read2 file by -2 or --read2=
use -h or --html= to specify the file name of HTML report
use -j or --json= to specify the file name of JSON report
the plain text result is directly printed to STDOUT, you can pipe it to a file using a >

Example

genefuse -r hg19.fasta -f genes/druggable.hg19.csv -1 genefuse.R1.fq.gz -2 genefuse.R2.fq.gz -h report.html > result

Reference genome

The reference genome should be a single whole FASTA file containg all chromosome data. This file shouldn't be compressed. For human data, typicall hg19/GRch37 or hg38/GRch38 assembly is used, which can be downloaded from following sites:

hg19/GRch37: ftp://ftp.ncbi.nlm.nih.gov/sra/reports/Assembly/GRCh37-HG19_Broad_variant/Homo_sapiens_assembly19.fasta
hg38/GRch38: http://hgdownload.cse.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz Remember to decompress hg38.fa.gz since it is gzipped and is not supported currently.

Fusion file

The fusion file is a list of coordinated target genes together with their exons. A sample is:

>EML4_ENST00000318522.5,chr2:42396490-42559688
1,42396490,42396776
2,42472645,42472827
3,42483641,42483770
4,42488261,42488434
5,42490318,42490446
...

>ALK_ENST00000389048.3,chr2:29415640-30144432
1,30142859,30144432
2,29940444,29940563
3,29917716,29917880
4,29754781,29754982
5,29606598,29606725
...

The coordination system should be consistent with the reference genome.

Fusion files provided in this package

Four fusion files are provided with genefuse:

genes/druggable.hg19.csv: all druggable fusion genes based on hg19/GRch37 reference assembly.
genes/druggable.hg38.csv: all druggable fusion genes based on hg38/GRch38 reference assembly.
genes/cancer.hg19.csv: all COSMIC curated fusion genes (http://cancer.sanger.ac.uk/cosmic/fusion) based on hg19/GRch37 reference assembly.
genes/cancer.hg38.csv: all COSMIC curated fusion genes (http://cancer.sanger.ac.uk/cosmic/fusion) based on hg38/GRch38 reference assembly.

Notes:

genefuse runs almost ~5X faster with druggable genes than cancer genes, since druggable genes are only a small subset of cancer genes. Use this one if you only care about the fusion related personalized medicine for cancers.
The cancer genes should be enough for most cancer related studies, since all COSMIC curated fusion genes are included.
If you want to create a custom gene list, please follow the instructions given on next section.

Create a fusion file based on hg19 or hg38

If you'd like to create a custom fusion file, you can use scripts/gen_fusion_file.jl, which is based on the Julia library OpenGene.jl to generate the fusion file you want.
You should prepare a file containing all genes you want, seperated by space or line break. Please note that comma is not supported. Each gene should be the HGNC standard name.
By default, the primary transcript (named as GENE_001) will be used. But you can specify the transcript by add _TranscriptId to the gene. For example: use CD74_ENST00000009530 to specify the transcript of CD74.
When the gene list file (genes.txt) is prepared, you can used following command to generate a fusion file (fusion.csv):

julia scripts/gen_fusion_file.jl -r hg19 -g genes.txt -f fusion.csv

The reference genome is specified by -r option, which can be hg19/GRch37/GRch38.

HTML report

GeneFuse can generate very informative and interactive HTML pages to visualize the fusions with following information:

the fusion genes, along with their transcripts.
the inferred break point with reference genome coordinations.
the inferred fusion protein, with all exons and the transcription direction.
the supporting reads, with all bases colorized according to their quality scores.
the number of supporting reads, and how many of them are unique (the rest may be duplications)

A HTML report example

See the HTML page of this picture: http://opengene.org/GeneFuse/report.html

All options

options:
  -1, --read1       read1 file name (string)
  -2, --read2       read2 file name (string [=])
  -f, --fusion      fusion file name, in CSV format (string)
  -r, --ref         reference fasta file name (string)
  -u, --unique      specify the least supporting read number is required to report a fusion, default is 2 (int [=2])
  -d, --deletion    specify the least deletion length of a intra-gene deletion to report, default is 50 (int [=50])
  -h, --html        file name to store HTML report, default is genefuse.html (string [=genefuse.html])
  -j, --json        file name to store JSON report, default is genefuse.json (string [=genefuse.json])
  -t, --thread      worker thread number, default is 4 (int [=4])
  -?, --help        print this message

Cite GeneFuse

If you used GeneFuse in you work, you can cite it as:

Shifu Chen, Ming Liu, Tanxiao Huang, Wenting Liao, Mingyan Xu and Jia Gu. GeneFuse: detection and visualization of target gene fusions from DNA sequencing data. International Journal of Biological Sciences, 2018; 14(8): 843-848. doi: 10.7150/ijbs.24626

eveilyeverafter/GeneFuse