arriba

Gene fusion
RNA-Seq
Structural variants
Chimeric alignments

Description

Arriba is a command-line tool for the detection of gene fusions from RNA-Seq data. It was developed for the use in a clinical research setting. Therefore, short runtimes and high sensitivity were important design criteria.

Arriba is based on the STAR RNA-Seq aligner and post-processes the alignments (output from STAR) to:

  1. detect split reads and discordant mates, which are indicative of structural rearrangements,

  2. find reads supporting gene fusions (i.e., reads spanning the breakpoints of gene fusions),

  3. perform various filtering steps to remove false positives, and

  4. output the final predictions in a standardized format.

In contrast to many other fusion detection tools, Arriba does not require to reduce the STAR parameter --alignIntronMax (maximum intron size). Reducing this parameter impairs detection of long introns and may affect expression quantification. Arriba reliably filters translocation-based false positives even when large maximum intron sizes are used.

Important: Arriba requires BAM files that were aligned with STAR using specific chimeric alignment parameters, particularly --chimOutType WithinBAM HardClip. See the official workflow documentation for the complete set of recommended STAR parameters.

Type

bash_script

License

MIT

Keywords

Gene fusion
RNA-Seq
Structural variants
Chimeric alignments

Contributors

Run this component

Run the following command to execute this component with Nextflow:

cat > params.yaml <<'EOM'  
fusions: "$id.$key.fusions.tsv"  
fusions_discarded: "$id.$key.fusions_discarded.tsv"  
id: "run"  
publish_dir: "output/"  
EOM

nextflow run https://packages.viash-hub.com/vsh/biobox.git \  
  -revision v0.4.1 \  
  -main-script target/nextflow/arriba/main.nf \  
  -params-file params.yaml  

Inputs

Name
Type & Properties
--bam
-x
file
required
--genome
-a
file
required
--gene_annotation
-g
file
required
--known_fusions
-k
file
--blacklist
-b
file
--structural_variants
-d
file
--tags
-t
file
--protein_domains
-p
file

Outputs

Name
Type & Properties
--fusions
-o
file
required
output
--fusions_discarded
-O
file
output

Arguments

Name
Type & Properties
--max_genomic_breakpoint_distance
-D
long
--strandedness
-s
string
--interesting_contigs
-i
string
multiple
--viral_contigs
-v
string
multiple
--disable_filters
-f
string
multiple
--max_e_value
-E
double
--min_supporting_reads
-S
integer
--max_mismappers
-m
double
--max_homolog_identity
-L
double
--homopolymer_length
-H
integer
--read_through_distance
-R
integer
--min_anchor_length
-A
integer
--many_spliced_events
-M
integer
--max_kmer_content
-K
double
--max_mismatch_pvalue
-V
double
--fragment_length
-F
integer
--max_reads
-U
integer
--quantile
-Q
double
--exonic_fraction
-e
double
--top_n
-T
integer
--covered_fraction
-C
double
--max_itd_length
-l
integer
--min_itd_allele_fraction
-z
double
--min_itd_supporting_reads
-Z
integer
--skip_duplicate_marking
-u
boolean_true
--extra_information
-X
boolean_true
--fill_gaps
-I
boolean_true

Collaborate Accelerate Succeed

Empower your organization with secure, scalable workflow solutions that simplify development, ensure compliance, and drive innovation.

Viash Hub is a platform developed by Data Intuitive, a Belgian-based bioinformatics company specializing in data workflow development and deployment.