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. Clone the NeoFuse repo locally

$ git clone https://github.com/icbi-lab/NeoFuse.git

Or you can find the script freely available here (deprecated).

Add NeoFuse 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) **

-C: Custom HLA I list (TXT file containing custom HLA I types)

-k: Keep STAR output (BAM files)

-l: int>=0: maximum available RAM (bytes) for sorting BAM

-K: File containing known/recurrent fusions (see Arriba manual for more details)

-f: Comma separated list of Arriba filters to disable (do not use space separeted lists, see Arriba manual for more details)

-v: VCF or Tab-separated file with coordinates of structural variants found using whole-genome sequencing data (The file may be gzip-compressed, for more info refer to Arriba manual)

-S: Determines how far a genomic breakpoint may be away from a transcriptomic breakpoint to still consider it as a related event (used with -v parameter - Default = 100000)

--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 minimum 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 a hashtag. FASTQ files should be in the same directory. No whitespaces allowed.

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) **

-C: Custom HLA I list (TXT file containing custom HLA I types)

-k: Keep STAR output (BAM files)

-l: int>=0: maximum available RAM (bytes) for sorting BAM

-K: File containing known/recurrent fusions (see Arriba manual for more details)

-f: Comma separated list of Arriba filters to disable (do not use space separeted lists, see Arriba manual for more details)

-v: VCF or Tab-separated file with coordinates of structural variants found using whole-genome sequencing data (The file may be gzip-compressed, for more info refer to Arriba manual)

-S: Determines how far a genomic breakpoint may be away from a transcriptomic breakpoint to still consider it as a related event (used with -v parameter - Default = 100000)

--singularity: NeoFuse will use the Singularity image

--docker: NeoFuse will use the Docker image

** 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 compatibility 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.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.samtools.err
├── Sample1.samtools.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_coverage_plot.pdf
└── 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

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   Breakpoint1 Breakpoint2 Split_Reads1    Split_Reads2    Discordant_Reads    Closest_Breakpoint1 Closest_Breakpoint2 HLA_Type    Fusion_Peptide  IC50    Rank    Event_Type  Stop_Codon  Confidence  Gene1_TPM   Gene2_TPM   Avg_TPM HLA_TPM
SETD2-ELP6  SETD2   ELP6    chr3:47056821   chr3:47504448   11  19  30  chr3:47053118(3703) chr3:47053118(3703) HLA-B*35:02 TPPIVQGVSL  337.8201495211543   0.16212499999999996 Fusion  no  high    37.73   34.87   36.30   1296.32
SETD2-ELP6  SETD2   ELP6    chr3:47056821   chr3:47504448   11  19  30  chr3:47053118(3703) chr3:47053118(3703) HLA-B*35:08 TPPIVQGVSL  174.1196814867352   0.4007499999999997  Fusion  no  high    37.73   34.87   36.30   1296.32
NPAS3-ABHD4 NPAS3   ABHD4   chr14:33215587  chr14:22603390  2   2   32  .   .   HLA-B*35:08 TPCEGLQNKF  71.80709202149346   0.13862500000000008 Fusion  no  high    3.45    136.77  70.11   1296.32

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.

icbi-lab / NeoFuse

readme

NeoFuse

Requirements

1. Installation

1.1. Install Docker or Singularity engine locally

1.2. Clone the NeoFuse repo locally

1.3. Pull/build the NeoFuse image

1.4. Download reference genomes and build STAR indexes

2. Usage

2.1. Analysis of single samples

2.2. Analysis of multiple samples

2.3. Binding affinity prediction with netMHCpan

3. Results

3.1. Main output directory

3.2. Output subdirectories

3.2.1. Arriba

3.2.2. LOGS

3.2.3. OptiType

3.2.4. TPM

3.2.5. NeoFuse

4. References