neofox 1.1.0

Annotation of mutated peptide sequences (mps) with published or novel potential neo-epitope descriptors

NeoFox - NEOantigen Feature tOolboX

NeoFox annotates neoantigen candidate sequences with published neoantigen features.

For a detailed documentation, please check out https://neofox.readthedocs.io

If you use NeoFox, please cite the following publication:
Franziska Lang, Pablo Riesgo-Ferreiro, Martin Löwer, Ugur Sahin, Barbara Schrörs, NeoFox: annotating neoantigen candidates with neoantigen features, Bioinformatics, Volume 37, Issue 22, 15 November 2021, Pages 4246–4247, https://doi.org/10.1093/bioinformatics/btab344

Table of Contents

1 Implemented neoantigen features
2 NeoFox requirements
3 Usage from the command line
4 Input data
5 Output data

1 Implemented Neoantigen Features

NeoFox covers the following neoantigen features and prediction algorithms:

Name	Reference	DOI
MHC I binding affinity/rank score (netMHCpan-v4.1)	Reynisson et al, 2020, Nucleic Acids Research	https://doi.org/10.4049/jimmunol.1700893
MHC II binding affinity/rank score (netMHCIIpan-v4.0)	Reynisson et al, 2020, Nucleic Acids Research	https://doi.org/10.1111/imm.12889
MixMHCpred score v2.2 §	Bassani-Sternberg et al., 2017, PLoS Comp Bio; Gfeller, 2018, J Immunol.	https://doi.org/10.1371/journal.pcbi.1005725 , https://doi.org/10.4049/jimmunol.1800914
MixMHC2pred score v2.0.2 §	Racle et al, 2019, Nat. Biotech. 2019	https://doi.org/10.1038/s41587-019-0289-6
Differential Agretopicity Index (DAI)	Duan et al, 2014, JEM; Ghorani et al., 2018, Ann Oncol.	https://doi.org/10.1084/jem.20141308
Self-Similarity	Bjerregaard et al, 2017, Front Immunol.	https://doi.org/10.3389/fimmu.2017.01566
IEDB immunogenicity	Calis et al, 2013, PLoS Comput Biol.	https://doi.org/10.1371/journal.pcbi.1003266
Neoantigen dissimilarity	Richman et al, 2019, Cell Systems	https://doi.org/10.1016/j.cels.2019.08.009
PHBR-I §	Marty et al, 2017, Cell	https://doi.org/10.1016/j.cell.2017.09.050
PHBR-II §	Marty Pyke et al, 2018, Cell	https://doi.org/10.1016/j.cell.2018.08.048
Generator rate	Rech et al, 2018, Cancer Immunology Research	https://doi.org/10.1158/2326-6066.CIR-17-0559
Recognition potential §	Łuksza et al, 2017, Nature; Balachandran et al, 2017, Nature	https://doi.org/10.1038/nature24473 , https://doi.org/10.1038/nature24462
Vaxrank	Rubinsteyn, 2017, Front Immunol	https://doi.org/10.3389/fimmu.2017.01807
Priority score	Bjerregaard et al, 2017, Cancer Immunol Immunother.	https://doi.org/10.1007/s00262-017-2001-3
Tcell predictor	Besser et al, 2019, Journal for ImmunoTherapy of Cancer	https://doi.org/10.1186/s40425-019-0595-z
PRIME §	Schmidt et al., 2021, Cell Reports Medicine	https://doi.org/10.1016/j.xcrm.2021.100194
HEX §	Chiaro et al., 2021, Cancer Immunology Research	https://doi.org/10.1158/2326-6066.CIR-20-0814

§ currently not supported for mouse

2 NeoFox Requirements

NeoFox depends on the following tools:

• Python >=3.7, <=3.8
• R 3.6.0
• BLAST 2.10.1
• netMHCpan 4.1
• netMHCIIpan 4.0
• MixMHCpred 2.2 (optional)
• MixMHC2pred 2.0.2 (optional)
• PRIME 2.0 (optional)

Install from PyPI:

pip install neofox

Or install from bioconda:

conda install bioconda::neofox

3 Usage from the command line

NeoFox can be used from the command line as shown below or programmatically (see https://neofox.readthedocs.io for more information).

neofox --candidate-file/--json-file neoantigens_candidates.tab/neoantigens_candidates.json --patient-data/--patient-data-json patient_data.txt/patient_data.json --output-folder /path/to/out --output-prefix out_prefix [--patient-id] [--with-table] [--with-json] [--num-cpus] [--affinity-threshold]

• --candidate-file: tab-separated values table with neoantigen candidates represented by long mutated peptide sequences as described here
• --json-file: JSON file neoantigens in NeoFox model format as described here
• --patient-id: patient identifier (optional, this will be used if the column patientIdentifier is missing in the candidate input file)
• --patient-data: a table of tab separated values containing metadata on the patient as described here
• --output-folder: path to the folder to which the output files should be written
• --output-prefix: prefix for the output files (optional)
• --with-table: output file in tab-separated format (default)
• --with-json: output file in JSON format (optional)
• --num-cpus: number of CPUs to use (optional)
• --config: a config file with the paths to dependencies as shown below (optional)
• --organism: the organism to which the data corresponds. Possible values: [human, mouse]. Default value: human
• --affinity-threshold: a affinity value (optional) neoantigen candidates with a best predicted affinity greater than or equal than this threshold will be not annotated with features that specifically model neoepitope recognition. A threshold that is commonly used is 500 nM.

The optional config file with the paths to the dependencies can look like this:

NEOFOX_REFERENCE_FOLDER=path/to/reference/folder
NEOFOX_RSCRIPT=`which Rscript`
NEOFOX_BLASTP=path/to/ncbi-blast-2.10.1+/bin/blastp
NEOFOX_NETMHCPAN=path/to/netMHCpan-4.1/netMHCpan
NEOFOX_NETMHC2PAN=path/to/netMHCIIpan-4.0/netMHCIIpan
NEOFOX_MIXMHCPRED=path/to/MixMHCpred-2.2/MixMHCpred
NEOFOX_MIXMHC2PRED=path/to/MixMHC2pred-2.0.1/MixMHC2pred_unix
NEOFOX_MAKEBLASTDB=path/to/ncbi-blast-2.8.1+/bin/makeblastdb
NEOFOX_PRIME=/path/to/PRIME-2.0/PRIME

4 Input data

4.1 Neoantigen candidates in tabular format

This is an dummy example of a table with neoantigen candidates:

gene	wildTypeXmer	mutatedXmer	patientIdentifier	rnaExpression	rnaVariantAlleleFrequency	dnaVariantAlleleFrequency	external_annotation_1	external_annotation_2
BRCA2	AAAAAAAAAAAAALAAAAAAAAAAAAA	AAAAAAAAAAAAAFAAAAAAAAAAAAA	Ptx	7.942	0.85	0.34	some_value	some_value
BRCA2	AAAAAAAAAAAAAMAAAAAAAAAAAAA	AAAAAAAAAAAAARAAAAAAAAAAAAA	Ptx	7.942	0.85	0.34	some_value	some_value
BRCA2	AAAAAAAAAAAAAGAAAAAAAAAAAAA	AAAAAAAAAAAAAKAAAAAAAAAAAAA	Ptx	7.942	0.85	0.34	some_value	some_value
BRCA2	AAAAAAAAAAAAACAAAAAAAAAAAAA	AAAAAAAAAAAAAEAAAAAAAAAAAAA	Ptx	7.942	0.85	0.34	some_value	some_value
BRCA2	AAAAAAAAAAAAAKAAAAAAAAAAAAA	AAAAAAAAAAAAACAAAAAAAAAAAAA	Ptx	7.942	0.85	0.34	some_value	some_value

where:

• gene: the HGNC gene symbol
• mutatedXmer: the neoantigen candidate sequence, i.e. the mutated amino acid sequence. The mutation should be located in the middle, flanked by 13 amino acid on both sites (IUPAC 1 respecting casing, eg: A)
• wildTypeXmer: the equivalent non-mutated amino acid sequence (IUPAC 1 respecting casing, eg: A)
• patientIdentifier: the patient identifier
• rnaExpression: RNA expression. (optional) (see NOTE) This value can be in any format chosen by the user (e.g. TPM, RPKM) but it is recommended to be consistent for data that should be compared.
• rnaVariantAlleleFrequency: the variant allele frequency calculated from the RNA (optional, this will be estimated using the dnaVariantAlleleFrequency if not available)
• dnaVariantAlleleFrequency: the variant allele frequency calculated from the DNA (optional)

NOTE: If rnaExpression is not provided, expression will be estimated by gene expression in a respective TCGA cohort and this value will be used for relevant features. The TCGA cohort to be used for imputation of gene expression needs to be indicated in the tumorType in the patient data (see below). If tumorType is not provided, expression will not be imputed.

4.2 Neoantigen candidates in JSON format

Besides tabular format, neoantigen candidates can be provided as a list of neoantigen models in JSON format as shown below. To simplify, only one full neoantigen model is shown:

[{
    "patientIdentifier": "Ptx",
    "gene": "BRCA2",
    "mutation": {
        "wildTypeXmer": "AAAAAAAAAAAAALAAAAAAAAAAAAA",
        "mutatedXmer": "AAAAAAAAAAAAAFAAAAAAAAAAAAA"
    }
}]

4.3 Patient-data format

This is an dummy example of a patient file:

identifier	mhcIAlleles	mhcIIAlleles	tumorType
Ptx	HLA-A*03:01,HLA-A*29:02,HLA-B*07:02,HLA-B*44:03,HLA-C*07:02,HLA-C*16:01	HLA-DRB1*03:01,HLA-DRB1*08:01,HLA-DQA1*03:01,HLA-DQA1*05:01,HLA-DQB1*01:01,HLA-DQB1*04:02,HLA-DPA1*01:03,HLA-DPA1*03:01,HLA-DPB1*13:01,HLA-DPB1*04:02	HNSC
Pty	HLA-A*02:01,HLA-A*30:01,HLA-B*07:34,HLA-B*44:03,HLA-C*07:02,HLA-C*07:02	HLA-DRB1*04:02,HLA-DRB1*08:01,HLA-DQA1*03:01,HLA-DQA1*04:01,HLA-DQB1*03:02,HLA-DQB1*14:01,HLA-DPA1*01:03,HLA-DPA1*02:01,HLA-DPB1*02:01,HLA-DPB1*04:01	HNSC

where:

• identifier: the patient identifier
• mhcIAlleles: comma separated MHC I alleles of the patient for HLA-A, HLA-B and HLA-C. If homozygous, the allele should be added twice.
• mhcIIAlleles: comma separated MHC II alleles of the patient for HLA-DRB1, HLA-DQA1, HLA-DQB1, HLA-DPA1 and HLA-DPB1. If homozygous, the allele should be added twice.
• tumorType: tumour entity in TCGA study abbreviation format (https://gdc.cancer.gov/resources-tcga-users/tcga-code-tables/tcga-study-abbreviations). This field is required for expression imputation and at the moment the following tumor types are supported:

5 Output data

The output data is returned by default in a short wide tab separated values file (--with-table). Optionally, it can be provided in JSON format (--with-json).