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NeoFuse is a user-friendly pipeline for the prediction of fusion neoantigens from tumor RNA-seq data.

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NeoFuse

NeoFuse is a user-friendly pipeline for the prediction of fusion neoantigens from tumor RNA-seq data.

NeoFuse takes single-sample FASTQ files of RNA-seq reads (single- or paired-end) as input and predicts putative fusion neoantigens through five main analytical modules based on state-of-the-art computational tools:

  • Genotyping of class-I Human Leukocyte Antigen (HLA) genes at 4-digit resolution using OptiType (Szolek et al., 2014).
  • Prediction of fusion peptides using Arriba (https://github.com/suhrig/arriba), together with confidence scores reflecting the likelihood that a fusion is caused by a tumor-specific genomic rearrangement and is not due to technical artifacts.
  • Prediction of the binding affinity of fusion peptides to HLA types, quantified as half maximal inhibitory concentration (IC50) and percentile rank, using MHCflurry (O’Donnell et al., 2018) or netMHCpan (Jurtz et al., 2017).
  • Quantification of gene expression levels, as transcripts per million (TPM), using STAR (Dobin et al., 2013) and featureCounts (Liao et al., 2014).
  • Neoantigen prioritization based on IC50 binding affinity and confidence score, and annotation of each neoantigen with: IC50, percentile rank, confidence score, binding HLA type, expression of the fusion and HLA genes in TPM, and information about the presence of a premature stop codon that might cause nonsense mediated decay of the fusion transcript.

We advise using paired-end data to increase sensitivity and accuracy of gene fusion detection.

Requirements

  • At least 30 GB of RAM
  • Docker (version 19.03 or later) or
  • Singularity (version 3.0 or later)

1. Installation

NeoFuse can be installed through the following four steps.

1.1. Install Docker or Singularity engine locally

Instructions for Docker installation

Instructions for Singularity installation

1.2. Download the NeoFuse script

The script is freely available here.

Unzip the archive and add it to PATH:

$ export PATH=$PATH:~/path/to/NeoFuse

1.3. Pull/build the NeoFuse image

The NeoFuse image can be automatically generated using the NeoFuse script:

Docker:

$ NeoFuse -B --docker

Singularity:

$ NeoFuse -B --singularity

1.4. Download reference genomes and build STAR indexes

The NeoFuse script can be also used to generate the genomes and indexes required by the analysis:

Docker:

$ Neofuse -R -o </path/to/output_folder> -n [cores] -V [genome version] --docker

Singularity:

$ Neofuse -R -o </path/to/output_folder> -n [cores] -V [genome version] --singularity

<Arguments>

-o: Output directory

[Options]

-n: Number of cores (default: 1)

-V: Genome version, either “GRCh37” and “GRCh38” (default: GRCh38)

Note: this process may take more than 1 hour, depending on the internet connection and the processing power.

2. Usage

Notes

  • On Mac OS X, you need to have Docker running to execute NeoFuse.

  • On Mac OS X, you might need to increase CPUs, Memory and Swap in Docker settings (Settings > Preferences > Advanced).

2.1. Analysis of single samples

NeoFuse can process single samples with the following command:

$ NeoFuse <arguments> [options] --singularity (or --docker) 

<Arguments>

-1: Path to read 1 FASTQ file (mandatory)

-2: Path to read 2 FASTQ fie (optional for single-end reads)

-s: Path to STAR index directory (mandatory)

-g: Path to reference genome FASTA file (mandatory)

-a: Path to annotation GTF file (mandatory)

Note: All input files passed as arguments must be unzipped.

[Options]

-d: Run ID (the name of the output files)

-m: Minimum peptide length (values: 8, 9, 10, or 11; default: 8)

-M: Maximum peptide length (values: 8, 9, 10, or 11; default: 8) *

-n: Number of cores (default: 1)

-t: IC50 binding affinity threshold (default: 500)

-T: Percentile rank threshold (default: Inf)

-c: Mimimum confidence score (values: H, M, or L; default: L) **

--singularity: NeoFuse will use the Singularity image

--docker: NeoFuse will use the Docker image

* NeoFuse will compute the binding affinity for all the possible lengths of peptides between the minimum and maximum input. For example if a user specifies '-m 8' and '-M 11', NeoFuse will compute the binding affinity for all peptides of length 8, 9, 10, and 11. To consider just one specific length, use only the '-m' argument.

** The mimimum Arriba confidence score can be set to: H (to return only high confidence fusions), M (for high and medium confidence fusions), or L (for high, medium, and low confidence fusions).

2.2. Analysis of multiple samples

For multiple-sample analysis, a TSV input file reporting the sample identifiers and path to input files has to be prepared. Format:

Paired-end reads:

#ID	Read1	Read2
Sample1 /path/to/Sample1_read_1.fastq	/path/to/Sample1_read_2.fastq
Sample2 /path/to/Sample2_read_1.fastq	/path/to/Sample2_read_2.fastq


Single-end reads:

#ID	Read1
Sample1	/path/to/Sample1_read_1.fastq
Sample2	/path/to/Sample2_read_1.fastq


Notes: The first line of the TSV should start with an hashtag. There should always be one blank row at the end of the TSV file.

Once the TSV file is created, the samples can be analyzed with the following command:

$ NeoFuse <arguments> [options] --singularity (or --docker)

<Arguments>

-i: Path to the input TSV file (mandatory)

-s: Path to STAR index directory (mandatory)

-g: Path to reference genome FASTA file (mandatory)

-a: Path to annotation GTF file (mandatory)

Note: All input files passed as arguments must be unzipped.

[Options]

-m: Minimum peptide length (values: 8, 9, 10, or 11; default: 8)

-M: Maximum peptide length (values: 8, 9, 10, or 11; default: 8) *

-n: Number of cores (default: 1)

-t: IC50 binding affinity threshold (default: 500)

-T: Percentile rank threshold (default: Inf)

-c: Mimimum confidence score (values: H, M, or L; default: L) **

--singularity: NeoFuse will use the Singularity image

--docker: NeoFuse will use the Docker image

* NeoFuse will compute the binding affinity for all the possible lengths of peptides between the minimum and maximum input. For example if a user specifies '-m 8' and '-M 11', NeoFuse will comppute the binding affinity for all peptides of length 8, 9, 10, and 11. To consider just one specific length, use only the '-m' argument.

** The mimimum Arriba confidence score can be set to: H (to return only high confidence fusions), M (for high and medium confidence fusions), or L (for high, medium, and low confidence fusions).

2.3. Binding affinity prediction with netMHCpan

Due to license compatability issues, netMHCpan is fully integrated but not distributed as part of NeoFuse.

If there is an existing local installation of netMHCpan, peptide-HLA binding affinity (IC50 and rank) can be predicted with netMHCpan instead of MHCflurry using the following command:

$ NeoFuse <arguments> [options] -N [/path/to/netMHCpan_direcotry] --singularity (or --docker)

3. Results

3.1. Main output directory

NeoFuse will create an output directory with the following structure:

/NeoFuse/output/directory/
├── Sample1
│   ├── Arriba
│   ├── LOGS
│   ├── NeoFuse
│   ├── OptiType
│   └── TPM
├── Sample2
│   ├── Arriba
│   ├── LOGS
│   ├── NeoFuse
│   ├── OptiType
│   └── TPM
…
└── SampleN
    ├── Arriba
    ├── LOGS
    ├── NeoFuse
    ├── OptiType
    └── TPM

3.2. Output subdirectories

3.2.1. Arriba

Sample.fusions.tsv file contains a list of gene fusions sorted from highest to lowest confidence.

Sample.fusions.discarded.tsv contains all events that Arriba classified as artifacts or that are also observed in healthy tissues.

/Arriba
├── Sample1.fusions.discarded.tsv
└── Sample1.fusions.tsv

3.2.2. LOGS

The standard output (sdout and stderr) for every tool used in the run is stored in the LOGS directory. File names may differ depending on the tools, peptide length, etc.

/LOGS
├── Sample1_10_MHCFlurry.log
├── Sample1_11_MHCFlurry.log
├── Sample1_8_MHCFlurry.log
├── Sample1_9_MHCFlurry.log
├── Sample1.arriba.err
├── Sample1.arriba.log
├── Sample1.cleave_peptides.log
├── Sample1.counts_to_tpm.log
├── Sample1.featureCounts.log
├── Sample1.final.log
├── Sample1.Log.final.out
├── Sample1.Log.out
├── Sample1.Log.std.out
├── Sample1.optitype.log
├── Sample1.razer1.log
├── Sample1.razer2.log
├── Sample1.STAR.err
├── Sample1.STAR.log
└── Sample1.association.log

3.2.3. OptiType

HLA_Optitype.txt contains the HLA types predicted by OptiType

/OptiType
└── Sample1_HLA_Optitype.txt

3.2.4. TPM

Contains all TPM expression values for all the genes

/TPM
└── Sample1.tpm.txt

3.2.5. NeoFuse

Contains the final output of the pipeline, which consists of three files:

/NeoFuse
├── Sample1_filtered.tsv
├── Sample1_unfiltered.tsv
└── Sample1_unsupported.txt

Sample_unsupported.txt contains the HLA types predicted by OptiType that are not supported by MHCflurry. Note: if netMHCpan is used instead of MHCfurry, this file is not generated.

Sample_unfiltered.tsv contains all the predicted fusion peptides and their annotations.

Sample_filtered.tsv contains a list of putative fusion neoantigens (selected considering the user-defined IC50/rank and confidence score thresholds). This file reports for each putative neoantigen: confidence score, binding HLA type, expression of the fusion and HLA genes in TPM, and information about the presence of a premature stop codon that might cause nonsense mediated decay of the fusion transcript. Example format:

Fusion	Gene1	Gene2	HLA_Type	Fusion_Peptide	IC50	Rank	Event_Type	Stop_Codon	Confidence	Gene1_TPM	Gene2_TPM	Avg_TPM	HLA_TPM
BCAS4-BCAS3	BCAS4	BCAS3	HLA-A*02:01	FLTPDPGAEV	51.07703105715209	0.3441249999999996	Fusion-out-of-frame	yes	high	29.07	219.64	124.35	22.40
RSBN1-AP4B1-AS1	RSBN1	AP4B1-AS1	HLA-A*02:01	STLEAPLAPA	372.5722360454703	1.3481250000000005	Fusion-out-of-frame	no	high	15.99	5.10	10.54	22.40
PPM1D-USP32	PPM1D	USP32	HLA-B*44:02	LEMGSPYVAQ	361.06034776084135	0.7239999999999998	Fusion-out-of-frame	no	high	129.58	178.94	154.26	28.59
TXLNG-SYAP1	TXLNG	SYAP1	HLA-A*02:01	KTIYLTLHLL	281.7035889624228	1.1387500000000002	Fusion-out-of-frame	yes	low	21.70	46.29	33.99	22.40
NAV1-GPR37L1	NAV1	GPR37L1	HLA-A*02:01	RLSASVPWAL	18.46819203380198	0.11887500000000008	Fusion-out-of-frame	no	low	0.89	2.89	1.89	22.40
NAV1-GPR37L1	NAV1	GPR37L1	HLA-A*02:01	RLSASVPWAL	18.46819203380198	0.11887500000000008	Fusion-out-of-frame	no	low	0.89	2.89	1.89	22.40

4. References

Dobin,A. et al. (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics, 29, 15–21.

Jurtz, V. et al. (2017) NetMHCpan-4.0: Improved Peptide–MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data. J. Immunol., 199, 3360-3368.

Liao,Y. et al. (2014) featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics, 30, 923–930.

O’Donnell,T.J. et al. (2018) MHCflurry: Open-Source Class I MHC Binding Affinity Prediction. Cell Syst, 7, 129–132.e4.

Szolek,A. et al. (2014) OptiType: precision HLA typing from next-generation sequencing data. Bioinformatics, 30, 3310–3316.

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