This document describes the output produced by the pipeline. Most of the plots are taken from the MultiQC report, which summarises results at the end of the pipeline.

The directories listed below will be created in the results directory after the pipeline has finished. All paths are relative to the top-level results directory.

Pipeline overview

The pipeline is built using Nextflow and processes data using the following steps:

  • Download and build references - Build references needed to run the rest of the pipeline
  • STAR - Alignment for arriba, squid and STAR-fusion
  • Cat - Concatenated fastq files per sample ID
  • Arriba - Arriba fusion detection
  • Pizzly - Pizzly fusion detection
  • Squid - Squid fusion detection
  • STAR-fusion - STAR-fusion fusion detection
  • StringTie - StringTie assembly
  • FusionCatcher - Fusion catcher fusion detection
  • Samtools - SAM/BAM file manipulation
  • Fusion-report - Summary of the findings of each tool and comparison to COSMIC, Mitelman and FusionGBD databases
  • FusionInspector - IGV-based visualisation tool for fusions filtered by fusion-report
  • Arriba visualisation - Arriba visualisation report for FusionInspector fusions
  • Qualimap - Quality control of alignment
  • Picard - Collect metrics
  • FastQC - Raw read quality control
  • MultiQC - Aggregate report describing results and QC from the whole pipeline
  • Pipeline information - Report metrics generated during the workflow execution

Download and build references

Output files
  • genomes_base/
    • arriba
      • blacklist_hg38_GRCh38_v2.1.0.tsv.gz
      • protein_domains_hg38_GRCh38_v2.1.0.gff3
      • cytobands_hg38_GRCh38_v2.1.0.tsv
    • ensembl
      • Homo_sapiens.GRCh38.{ensembl_version}.all.fa
      • Homo_sapiens.GRCh38.{ensembl_version}.cdna.all.fa.gz
      • Homo_sapiens.GRCh38.{ensembl_version}.gtf
      • Homo_sapiens.GRCh38.{ensembl_version}.chr.gtf
      • Homo_sapiens.GRCh38.{ensembl_version}.chr.gtf.refflat
      • Homo_sapiens.GRCh38.{ensembl_version}.interval_list
    • fusioncatcher
      • human_v<version> - dir with all references for fusioncatcher
    • fusion_report_db
      • cosmic.db
      • fusiongdb.db
      • fusiongdb2.db
      • mitelman.db
    • pizzly
      • kallisto - file containing the kallisto index
    • star - dir with STAR index
    • starfusion
      • files and dirs used to build the index
      • ctat_genome_lib_build_dir - dir containing the index

(Only files or folders used by the pipeline are mentioned explicitly.)


STAR is used to align to genome reference

STAR is run 3 times:

For arriba with the parameters:

--readFilesCommand zcat \
--outSAMtype BAM Unsorted \
--outSAMunmapped Within \
--outBAMcompression 0 \
--outFilterMultimapNmax 50 \
--peOverlapNbasesMin 10 \
--alignSplicedMateMapLminOverLmate 0.5 \
--alignSJstitchMismatchNmax 5 -1 5 5 \
--chimSegmentMin 10 \
--chimOutType WithinBAM HardClip \
--chimJunctionOverhangMin 10 \
--chimScoreDropMax 30 \
--chimScoreJunctionNonGTAG 0 \
--chimScoreSeparation 1 \
--chimSegmentReadGapMax 3 \
--chimMultimapNmax 50

For squid with the parameters:

--twopassMode Basic \
--chimOutType SeparateSAMold \
--chimSegmentMin 20 \
--chimJunctionOverhangMin 12 \
--alignSJDBoverhangMin 10 \
--outReadsUnmapped Fastx \
--outSAMstrandField intronMotif \
--outSAMtype BAM SortedByCoordinate \
--readFilesCommand zcat

For STAR-fusion with the parameters:

--twopassMode Basic \
--outReadsUnmapped None \
--readFilesCommand zcat \
--outSAMstrandField intronMotif \
--outSAMunmapped Within \
--chimSegmentMin 12 \
--chimJunctionOverhangMin 8 \
--chimOutJunctionFormat 1 \
--alignSJDBoverhangMin 10 \
--alignMatesGapMax 100000 \
--alignIntronMax 100000 \
--alignSJstitchMismatchNmax 5 -1 5 5 \
--chimMultimapScoreRange 3 \
--chimScoreJunctionNonGTAG -4 \
--chimMultimapNmax 20 \
--chimNonchimScoreDropMin 10 \
--peOverlapNbasesMin 12 \
--peOverlapMMp 0.1 \
--alignInsertionFlush Right \
--alignSplicedMateMapLminOverLmate 0 \
--alignSplicedMateMapLmin 30 \
--chimOutType Junctions

STAR_FOR_STARFUSION uses ${params.ensembl_ref}/Homo_sapiens.GRCh38.${params.ensembl_version}.chr.gtf whereas STAR_FOR_ARRIBA and STAR_FOR_SQUID use ${params.ensembl_ref}/Homo_sapiens.GRCh38.${params.ensembl_version}.gtf

Output files
  • star_for_<tool> _ Common _ <sample_id> _ <sample_id>.Log.progress.out _ <sample_id> _ For arriba: _ <sample_id>.Aligned.out.bam _ For squid: _ <sample_id>.Aligned.sortedByCoord.out.bam _ <sample_id>.Chimeric.out.sam _ <sample_id>.unmapped_1.fastq.gz _ <sample_id>.unmapped_2.fastq.gz _ For starfusion: _ <sample_id>.Aligned.sortedByCoord.out.bam _ <sample_id>.Chimeric.out.junction


Cat is used to concatenate fastq files belonging to the same sample.

Output files
  • cat
    • <sample_id>_1.merged.fastq.gz
    • <sample_id>_2.merged.fastq.gz


Arriba is used for i) detect fusion and ii) output a PDF report for the fusions found (visualisation):


Output files
  • arriba
    • <sample_id>.arriba.fusions.tsv - contains the identified fusions
    • <sample_id>.arriba.fusions.discarded.tsv


Output files
  • arriba_visualisation
    • <sample_id>.pdf


The first step of the pizzly workflow is to run kallisto quant:


Output files
  • kallisto
    • <sample_id>.kallisto_quant.fusions.txt

Pizzly refines kallisto output.


Pizzly uses the following arguments:

-k 31 \
--align-score 2 \
--insert-size 400 \
--cache index.cache.txt
Output files
  • pizzly
    • <sample_id>.pizzly.txt - contains the identified fusions
    • <sample_id>.pizzly.unfiltered.json


Squid is run in two steps: i) fusion detection and ii) fusion annotation but the output is in a common squid directory.

Output files
  • squid
    • <sample_id>.squid.fusions_sv.txt - contains the identified fusions
    • <sample_id>.squid.fusions.annotated.txt- contains the identified fusions annotatedvi


Output files
  • starfusion
    • <sample_id>.starfusion.fusion_predictions.tsv - contains the identified fusions
    • <sample_id>.starfusion.abridged.tsv
    • - contains the identified fusions.starfusion.abridged.coding_effect.tsv


Output files
  • stringtie/<sample_id>/stringtie.merged.gtf - merged gtf from annotation and stringtie output gtfs


Output files
  • fusioncatcher _ <sample_id>.fusioncatcher.fusion-genes.txt _ <sample_id>.fusioncatcher.summary.txt * <sample_id>.fusioncatcher.log


Samtools view

Samtools view is used to convert the chimeric SAM output from STAR_FOR_SQUID to BAM

Output files
  • samtools_view_for_squid
    • <sample_id>_chimeric.bam - sorted BAM file

Samtools sort

Samtools sort is used to sort BAM files from STAR_FOR_STARFUSION (for arriba visualisation) and the chimeric BAM from STAR_FOR_SQUID

Output files
  • samtools_sort_for_<arriba/squid>
    • <sample_id>(_chimeric)_sorted.bam - sorted BAM file

Samtools index

Samtools index is used to index BAM files from STAR_FOR_ARRIBA (for arriba visualisation) and STAR_FOR_STARFUSION (for QC)

Output files
  • samtools_for_<arriba/qc>
    • <sample_id>.(Aligned.sortedByCoord).out.bam.bai -


Output files
  • fusionreport
    • <sample_id>
      • <sample_id>.fusionreport.tsv
      • <sample_id>.fusionreport_filtered.tsv
      • index.html - general report for all filtered fusions
      • <fusion>.html - specific report for each filtered fusion

The score is explained on the original fusion-report github page.


Output files
  • fusioninspector
    • <sample_id>.fusion_inspector_web.html - visualisation report described in details here
    • FusionInspector.log
    • <sample_id>.FusionInspector.fusions.abridged.tsv


Output files
  • qualimap
    • qualimapReport.html - HTML report
    • rnaseq_qc_results.txt - TXT results
    • css - dir for html style
    • images_qualimapReport- dir for html images
    • raw_data_qualimapReport - dir for html raw data


Picard CollectRnaMetrics and picard MarkDuplicates share the same outpur directory.

Output files
  • picard
    • <sample_id>.MarkDuplicates.metrics.txt - metrics from CollectRnaMetrics
    • <sample_id>_rna_metrics.txt - metrics from MarkDuplicates
    • <sample_id>.bam - BAM file with marked duplicates


Output files
  • fastqc/
    • *_fastqc.html: FastQC report containing quality metrics.
    • * Zip archive containing the FastQC report, tab-delimited data file and plot images.

FastQC gives general quality metrics about your sequenced reads. It provides information about the quality score distribution across your reads, per base sequence content (%A/T/G/C), adapter contamination and overrepresented sequences. For further reading and documentation see the FastQC help pages.

MultiQC - FastQC sequence counts plot

MultiQC - FastQC mean quality scores plot

MultiQC - FastQC adapter content plot

NB: The FastQC plots displayed in the MultiQC report shows untrimmed reads. They may contain adapter sequence and potentially regions with low quality.


Output files
  • multiqc/
    • multiqc_report.html: a standalone HTML file that can be viewed in your web browser.
    • multiqc_data/: directory containing parsed statistics from the different tools used in the pipeline.
    • multiqc_plots/: directory containing static images from the report in various formats.

MultiQC is a visualization tool that generates a single HTML report summarising all samples in your project. Most of the pipeline QC results are visualised in the report and further statistics are available in the report data directory.

Results generated by MultiQC collate pipeline QC from supported tools e.g. FastQC. The pipeline has special steps which also allow the software versions to be reported in the MultiQC output for future traceability. For more information about how to use MultiQC reports, see

Pipeline information

Output files
  • pipeline_info/
    • Reports generated by Nextflow: execution_report.html, execution_timeline.html, execution_trace.txt and
    • Reports generated by the pipeline: pipeline_report.html, pipeline_report.txt and software_versions.yml. The pipeline_report* files will only be present if the --email / --email_on_fail parameter’s are used when running the pipeline.
    • Reformatted samplesheet files used as input to the pipeline: samplesheet.valid.csv.

Nextflow provides excellent functionality for generating various reports relevant to the running and execution of the pipeline. This will allow you to troubleshoot errors with the running of the pipeline, and also provide you with other information such as launch commands, run times and resource usage.