Introduction

Provides a docker image for using FusionCatcher. There is also a procedure for being able to create synthetic data from Fusim and Art Illumina.

How to create synthetic data

Define the file genes_panels.txt where there are the genes to be used to create the data.

Genes panel example:

RUNX1
ETV6
RIPOR1
CTCF
KMT2A
EZR
PAX5
PTEN
PMEL
TAL1
CRLF2
MEF2D
BCL9
TCF3
ZNF384
PBX1

Fusim

Fusim allows you to generate merges from genes_panels.txt.

To install fusim:

wget https://github.com/aebruno/fusim/raw/master/releases/fusim-0.2.2-bin.zip
unzip fusim-0.2.2-bin.zip
rm fusim-0.2.2-bin.zip

wget -O refFlat.txt.gz http://hgdownload.cse.ucsc.edu/goldenPath/hg19/database/refFlat.txt
gunzip refFlat.txt.gz
mv refFlat.txt fusim-0.2.2/refFlat.txt

wget http://hgdownload.cse.ucsc.edu/goldenPath/hg19/bigZips/chromFa.tar.gz
tar -xzf chromFa.tar.gz
cat chr*.fa > fusim-0.2.2/hg19.fa
rm chromFa.tar.gz
rm chr*.fa

apt install samtools
samtools faidx fusim-0.2.2/hg19.fa

To run fusim on two genes gene1 and gene2, use the following command specifying dir_path:

java -jar ./fusim-0.2.2/fusim.jar \
        --gene-model=./fusim-0.2.2/refFlat.txt \
        --fusions=10 \
        --reference=./fusim-0.2.2/hg19.fa \
        --fasta-output=dir_path/fusion_gene1_gene2.fasta \
        --text-output=dir_path/fusion_gene1_gene2.txt \
        -1 gene1 \
        -2 gene2 \
        --cds-only \
        --auto-correct-orientation

Run the following python script fusim_data.py to form all possible merges between genes contained in genes_panel.txt.

ART ILLUMINA

ART ILLUMINA is the tool that allows you to obtain synthetics read from fasta files.

To install ART ILLUMINA install the following package:

apt install art-nextgen-simulation-tools

ART ILLUMINA takes a folder of fasta files and returns the synthetic reads. Run the following python script art_data.py to get the reads of all fasta files obtained from fusim.

---

Fusion Catcher

Run Fusion Catcher

We will use FusionCatcher for human genome.

Download human_v102 inside the data folder by running the following command:

mkdir -p data
wget --no-check-certificate http://sourceforge.net/projects/fusioncatcher/files/data/human_v102.tar.gz.aa -P data
wget --no-check-certificate http://sourceforge.net/projects/fusioncatcher/files/data/human_v102.tar.gz.ab -P data
wget --no-check-certificate http://sourceforge.net/projects/fusioncatcher/files/data/human_v102.tar.gz.ac -P data
wget --no-check-certificate http://sourceforge.net/projects/fusioncatcher/files/data/human_v102.tar.gz.ad -P data

cat human_v102.tar.gz.* > human_v102.tar.gz
rm -f human_v102.tar.gz.*

tar -xzf human_v102.tar.gz

Start a container using the fusioncatcher image fusioncatcher_data:v1:

sudo docker run -v "$(pwd)/data":/opt/fusioncatcher/v1.30/data -it fusioncatcher_data:v1

Once the container is started enter the fusioncatcher installation folder, to run the tool call the python file fusioncatcher.py contained in the bin folder.

Usage (Docs):

python2 fusioncatcher.py \
-d /some/human/data/directory/ \
-i /some/input/directory/containing/fastq/files/ \
-o /some/output/directory/

Usage with human_v102:

cd /opt/fusioncatcher/v1.30/
python2 bin/fusioncatcher.py -d data/human_v102 -i some/input/directory/containing/fastq/files/ -o /some/output/directory/

In case you want to use fusioncatcher on 'single-end' reads they have to be longer than 130 and you have to specify the --single-end parameter.

Run Fusion Catcher on the ART ILLUMINA sequences.

Run the docker container by mounting the data folder making sure that the fasta.fq outputs of ART ILLUMINA are present.

sudo docker run -v "$(pwd)/data":/opt/fusioncatcher/v1.30/data -it fusioncatcher_data:v1

Run Fusion Catcher:

cd opt/fusioncatcher/v1.30/
mkdir -p data/some_output
python2 bin/fusioncatcher.py -d data/human_v102 -i data/art_output -o data/some_output

Notes on the input of Fusion Catcher

Fusion catcher assumes that the folder containing fastq files are paired-read, specifically they will be paired two by two in alphabetical order. The following are some examples:

Consider a folder with the following files:

L002_R1.fastq.gz
L002_R2.fastq.gz
L003_R1.fastq.gz
L003_R2.fastq.gz

FusionCatcher will assume that the first two files are a pair and the second two are another pair.

For example, this NOT is a valid input:

10_L002_R2_05.fastq.gz
11_L002_R1_06.fastq.gz
12_L002_R2_06.fastq.gz
1_L002_R1_01.fastq.gz
2_L002_R2_01.fastq.gz
3_L002_R1_02.fastq.gz
4_L002_R2_02.fastq.gz
5_L002_R1_03.fastq.gz
6_L002_R2_03.fastq.gz
7_L002_R1_04.fastq.gz
8_L002_R2_04.fastq.gz
9_L002_R1_05.fastq.gz

It is important that the input folder contains only fastq files related to the reads to be analyzed.

Notes on the output of Fusion Catcher

Within the folder specified with the parameter -o data/output/ fusioncatcher will produce several output files, the two most relevant ones below:

final-list_candidate_fusion_genes.txt - Contains the final table with the new candidate fusion genes (fusion point with * is also present); summary_candidate_fusions.txt - Contains a summary of candidate fusion genes found;

Table 1 - Description of columns contained in final-list_candidate-fusion-genes.txt:

Column	Description
Gene_1_symbol(5end_fusion_partner)	Gene symbol of the 5' end fusion partner
Gene_2_symbol_2(3end_fusion_partner)	Gene symbol of the 3' end fusion partner
Gene_1_id(5end_fusion_partner)	Ensembl gene id of the 5' end fusion partner
Gene_2_id(3end_fusion_partner)	Ensembl gene id of the 3' end fusion partner
Exon_1_id(5end_fusion_partner)	Ensembl exon id of the 5' end fusion exon-exon junction
Exon_2_id(3end_fusion_partner)	Ensembl exon id of the 3' end fusion exon-exon junction
Fusion_point_for_gene_1(5end_fusion_partner)	Chromosomal position of the 5' end of fusion junction (chromosome:position:strand); 1-based coordinate
Fusion_point_for_gene_2(3end_fusion_partner)	Chromosomal position of the 3' end of fusion junction (chromosome:position:strand); 1-based coordinate
Spanning_pairs	Count of pairs of reads supporting the fusion (including also the multimapping reads)
Spanning_unique_reads	Count of unique reads (i.e. unique mapping positions) mapping on the fusion junction. Shortly, here are counted all the reads which map on fusion junction minus the PCR duplicated reads.
Longest_anchor_found	Longest anchor (hangover) found among the unique reads mapping on the fusion junction
Fusion_finding_method	Aligning method used for mapping the reads and finding the fusion genes. Here are two methods used which are: (i) BOWTIE = only Bowtie aligner is used for mapping the reads on the genome and exon-exon fusion junctions, (ii) BOWTIE+BLAT = Bowtie aligner is used for mapping reads on the genome and BLAT is used for mapping reads for finding the fusion junction, (iii) BOWTIE+STAR = Bowtie aligner is used for mapping reads on the genome and STAR is used for mapping reads for finding the fusion junction, (iv) BOWTIE+BOWTIE2 = Bowtie aligner is used for mapping reads on the genome and Bowtie2 is used for mapping reads for finding the fusion junction.
Fusion_sequence	The inferred fusion junction (the asterisk sign marks the junction point)
Fusion_description	Type of the fusion gene (see the Table 2)
Counts_of_common_mapping_reads	Count of reads mapping simultaneously on both genes which form the fusion gene. This is an indication how similar are the DNA/RNA sequences of the genes forming the fusion gene (i.e. what is their homology because highly homologous genes tend to appear show as candidate fusion genes). In case of completely different sequences of the genes involved in forming a fusion gene then here it is expected to have the value zero.
Predicted_effect	Predicted effect of the candidate fusion gene using the annotation from Ensembl database. This is shown in format effect_gene_1/effect_gene_2, where the possible values for effect_gene_1 or effect_gene_2 are: intergenic, intronic, exonic(no-known-CDS), UTR, CDS(not-reliable-start-or-end), CDS(truncated), or CDS(complete). In case that the fusion junction for both genes is within their CDS (coding sequence) then only the values in-frame or out-of-frame will be shown.
Predicted_fused_transcripts	All possible known fused transcripts in format ENSEMBL-TRANSCRIPT-1:POSITION-1/ENSEMBLE-TRANSCRIPT-B:POSITION-2, where are fused the sequence 1:POSITION-1 of transcript ENSEMBL-TRANSCRIPT-1 with sequence POSITION-2:END of transcript ENSEMBL-TRANSCRIPT-2
Predicted_fused_proteins	Predicted amino acid sequences of all possible fused proteins (separated by ";").

For more information on the abbreviations used by Fusion Catcher visit this github.