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abyssum / nextNEOpi

nextNEOpi: a comprehensive pipeline for computational neoantigen prediction

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nextNEOpi overview

NeoEpitope predictions Nextflow Pipeline

Pipeline takes fastq files from Tumor and Normal samples (WES or WGS) and optionally RNAseq from Tumor to predict neoepitopes.

The pipeline uses the following tools:

  • MuTect2
  • MuTect1
  • VarScan2
  • Strelka2/Manta
  • Sequenza
  • ASCAT
  • CNVkit
  • OptiType
  • HLA-HD
  • pVACseq (netMHCpan, netMHCIIpan, mhcflurry)
  • NeoFuse
  • mixMHC2pred
  • mixcr

It outputs a vcf file with the annotated and filtered SNPs and Indels, which where called with each of the callers and a high confidence vcf file (hc) in which only variants that were called by a minimum of 2 of the callers are listed. All vcf files are annotated with VEP. In addition the germline variants are called using HaploTypeCaller and a phased vcf for pVACseq is generated as well. Copy number variations are analyzed using CNVkit, ASCAT, and sequenza. Tumor purity is estimated by ASCAT and Sequenza and is used to derive the clonality measure for the predicted neoantigens. Tumor mutational burden (TMB) is calculated for all variants over the entire read covered genome and for coding variants on read covered exons. HLA class I and class II alleles are predicted with OptiType and HLA-HD. Class I and Class II neoepitopes are predicted with pVACseq using netMHCpan, netMHCIIpan and mhcflurry. In addition mixMHC2pred is used as complement Class II neoepitope predictor. Fusion neoantigens are predicted with NeoFuse. CSiN immunogenicity score is reported for Class I, Class II and combined neoepitopes. A GBM model [1] is used to predict immunogenicity scores for MHC class I single nucleotide variant (SNV) neoantigens 8-11 amino acid residues in length. Finally mixcr is run to predict the TCR and BCR repertoire.

[1] https://github.com/vincentlaboratories/neoag/.

nextNEOpi overview

Hardware recommendations

nextNEOpi is designed to run on high memory multi-core servers (recommended > 16 cores, min. 64GB RAM, > 5 TB of free disk space). For analysis of larger cohorts we strongly recommend running nextNEOpi on a HPC cluster with multiple compute nodes that meet these specifications. However, by tuning the memory and CPU parameters in params.config and process.config it should also be possible to run nextNEOpi on systems with lower CPU and memory resources.

1. Installation

1.1 Nextflow

The command below may be used to install Nextflow. Please see also the installation instructions at: https://www.nextflow.io/index.html#GetStarted

curl -s https://get.nextflow.io | bash

1.2 Analysis tools and software packages

The pipeline will install almost all required tools via conda environments or Singularity images.

The software that needs to be present on the system is Java (minimum version 8), Nextflow (see above), Conda, Singularity.

Optional but recommended: Due to license restrictions you may also need to download and install HLA-HD by your own, and set the installation path in conf/params.config. If HLA-HD is not available Class II neoepitopes will NOT be predicted

[Manual installaton: Not recommended]:

If you prefer local installation of the analysis tools please install the following software:

  • FASTQC (Version >= 0.11.8)
  • FASTP (Version >= v0.20.1)
  • JAVA7 (Version 1.7)
  • JAVA8 (Version 1.8)
  • BWA (Version 0.7.17)
  • SAMTOOLS (Version 1.9)
  • GATK3 (Version 3.8-0)
  • GATK4 (Version >= 4.1.7.0)
  • VARSCAN (Version 2.4.3)
  • MUTECT1 (Version 1.1.7) ---- optional
  • BAMREADCOUNT (Version 0.8.0)
  • VEP (Version v103)
  • BGZIP
  • TABIX
  • BCFTOOLS
  • MANTA
  • STRELKA
  • SAMBAMBA
  • OPTITYPE
  • PYTHON
  • PERL
  • CONDA
  • YARA
  • HLA-HD
  • ALLELECOUNT
  • RSCRIPT (R > 3.6.1)
  • SEQUENZA (3.0)
  • CNVkit

all these tools need be available via the $PATH environment variable. However, you still need Java, Nextflow, Conda and Singularity installed on your system.

[End manual installation: not recommended]

1.2 References

The pipeline requires different reference files, indexes and databases:

please see conf/resources.config

We prepared a bundle with all needed references, indexes and databases which can be obtained from:

https://apps-01.i-med.ac.at/resources/nextneopi/nextNEOpi_resources.tar.gz

download and extract the contents of the archive into the directory you specified for resourcesBaseDir in the conf/params.config file.

The structure should look as shown blow:

├── {resourcesBaseDir}
    ├── databases
    ├── ExomeCaptureKits
    └── references

Notes

  1. You may also provide your own versions of these files. To do so, please change the conf/resources.config accordingly.
  2. Due to license restriction, we do not provide a copy of the optional COSMIC database. If you also want to include COSMIC data, you may get a copy at https://cancer.sanger.ac.uk/cosmic
  3. We provide the region and bait files for two different Exome capturing kits from Agilent:
    • SureSelect Human All Exon V6 exome
    • SureSelect Human All Exon V7 exome
    • Twist Human comprehensive exome

You may add your own region and bait files by defining an entry in conf/resources.config

Refs:

2. Usage

Before running the pipeline, the config files in the conf/ directory may need to be edited. In the params.config parameters default settings are defined. The process.config is a template for the configuration of the single processes, you may check the number of CPUs assigned for each process and adjust according to your systems capabilities.

Most pipeline parameters can be edited in the params.config file or changed on run time with command line options by using --NameOfTheParameter given in the params.config. References, databases should be edited in the resources.config file.

nextflow run nextNEOpi.nf --readsTumor <tumorFastq> --readsNormal <normalFastq> [--reads_RNAseq] | --batchFile <batchFile_FASTQ.csv | batchFile_BAM.csv> -profile singularity|conda,[cluster] [-resume] -config conf/params.config

Profiles: conda or singularity

We highly recommend to use either the singularity or conda profile. You can specify one of the two profiles using the option -profile singularity or -profile conda. This way you do not have to care about installing all the required software including all its dependencies.

Profiles: cluster

We strongly recommend to run the pipeline on a HPC cluster. You can enable runs in cluster mode by using a profile named e.g. cluster and the option -profile singularity,cluster or -profile conda,cluster

For an example SGE cluster profile, please see profiles in conf/profiles.config. You may uncomment and adjust the cluster profile to your scheduling system.

Sequencing data input:

Besides raw reads in FASTQ fromated files, input data may also be provided in BAM format.

RNA reads from tag seq library i.e. 3-prime end sequencing protocol

--RNA_tag_seq turns off the "--trna-vaf" and "--trna-cov" filter from pVACseq epitope filtering. It also turns of HLA typing from RNAseq data. 3-prime end sequencing does not cover the entire transcript.

Mandatory arguments:

--batchFile [recommended]

Make sure that your batchFile CSV includes the column names as shown in the examples below as header line. See also example_batchFile_FASTQ.csv or example_batchFile_BAM.csv

FASTQ raw reads

  • e.g.: CSV-file with Tumor/Normal WES/WGS, and RNAseq reads, all paired end reads:
tumorSampleName readsTumorFWD readsTumorREV normalSampleName readsNormalFWD readsNormalREV readsRNAseqFWD readsRNAseqREV HLAfile sex
sample1 Tumor1_reads_1.fastq Tumor1_reads_2.fastq normal1 Normal1_reads_1.fastq Normal1_reads_2.fastq Tumor1_RNAseq_reads_1.fastq Tumor1_RNAseq_reads_2.fastq None XX
sample2 Tumor2_reads_1.fastq Tumor2_reads_2.fastq normal2 Normal2_reads_1.fastq Normal2_reads_2.fastq Tumor2_RNAseq_reads_1.fastq Tumor2_RNAseq_reads_2.fastq None XY
...
sampleN TumorN_reads_1.fastq TumorN_reads_2.fastq normalN NormalN_reads_1.fastq NormalN_reads_2.fastq TumorN_RNAseq_reads_1.fastq TumorN_RNAseq_reads_2.fastq custom_HLAs.txt XX
  • e.g.:CSV-file with Tumor/Normal WES/WGS, and RNAseq reads, e.g. all single end reads:
tumorSampleName readsTumorFWD readsTumorREV normalSampleName readsNormalFWD readsNormalREV readsRNAseqFWD readsRNAseqREV HLAfile sex
sample1 Tumor1_reads_1.fastq None normal1 Normal1_reads_1.fastq None Tumor1_RNAseq_reads_1.fastq None None XX
sample2 Tumor2_reads_1.fastq None normal2 Normal2_reads_1.fastq None Tumor2_RNAseq_reads_1.fastq None None XY
...
sampleN TumorN_reads_1.fastq None normalN NormalN_reads_1.fastq None TumorN_RNAseq_reads_1.fastq None custom_HLAs.txt XX
  • e.g.:CSV-file with Tumor/Normal WES/WGS, NO RNAseq reads, e.g. all single end reads:
tumorSampleName readsTumorFWD readsTumorREV normalSampleName readsNormalFWD readsNormalREV readsRNAseqFWD readsRNAseqREV HLAfile sex
sample1 Tumor1_reads_1.fastq None normal1 Normal1_reads_1.fastq None None None None XX
sample2 Tumor2_reads_1.fastq None normal2 Normal2_reads_1.fastq None None None None XY
...
sampleN TumorN_reads_1.fastq None normalN NormalN_reads_1.fastq None None None custom_HLAs.txt XX

or

--readsTumor reads_{1,2}.fastq or reads_1.fastq; paired-end or single-end reads; FASTQ (may be gziped)

--readsNormal reads_{1,2}.fastq or reads_1.fastq; paired-end or single-end reads; FASTA files (may be gziped)

(optional but recommended: --readsRNAseq reads_{1,2}.fastq or reads_1.fastq; paired-end or single-end reads; FASTQ (may be gziped))

BAM files

Note: If BAM files are used it is very much recommended that they also include also the unmapped and multimapping reads. These reads can be helpful for HLA-typing.

  • e.g.: CSV-file with Tumor/Normal WES/WGS, and RNAseq data:
tumorSampleName bamTumor normalSampleName bamNormal bamRNAseq HLAfile sex
sample1 Tumor1.bam normal1 Normal1.bam Tumor1_RNAseq.bam None XX
sample2 Tumor2.bam normal2 Normal2.bam Tumor2_RNAseq.bam None XY
...
sampleN TumorN.bam normalN NormalN.bam TumorN_RNAseq.bam None XX
  • e.g.:CSV-file with Tumor/Normal WES/WGS, NO RNAseq data:
tumorSampleName bamTumor normalSampleName bamNormal bamRNAseq HLAfile sex
sample1 Tumor1.bam normal1 Normal1.bam None None XX
sample2 Tumor2.bam normal2 Normal2.bam None None XY
...
sampleN TumorN.bam normalN NormalN.bam None None XX

or

--tumorBam tumor_1.bam

--normalBam normal_1.bam

(optional but recommended: --rnaBam tumor_1_RNAseq.bam

Notes

  • You must not mix samples with single-end and paired-end reads in a batch file. Though, it is possible to have for e.g. all DNA reads paired-end and all RNAseq reads single-end or vice-versa.

  • in the HLAfile coulumn a user suppiled HLA types file may be specified for a given sample, see also --customHLA option below

  • the sex column can be "XX", "female" or "Female", "XY", "male" or "Male". If not specified or "None" Male is assumed

  • when providing paired end fastq files via commandline options (--readsTumor, --readsNormal, --readsRNA), please make sure you put the filename pattern into qoutes: e.g. --readsTumor "reads_{1,2}.fastq.gz"

  • Please make sure your /tmp (or the directory set as $TMPDIR) has a lot of free space (> 50GB, depending on input data size). You may change the tmp dir used by nextNEOpi by setting the parameter tmpDir in params.config or on the command line --tmpDir to a directory of your choice.

Example run command with batchfile:

nextflow run nextNEOpi.nf \
    --batchFile batchfile.csv \
    -config conf/params.config \
    --outputDir /data/results/nextNEOpi/myResults \
    --trim_adapters true \
    --trim_adapters_RNAseq true \
    --use_NetChop false \
    -profile singularity,cluster \
    -resume

Optional argument:

--tumorSampleName tumor sample name. If not specified samples will be named according to the fastq filenames.

--normalSampleName normal sample name. If not specified samples will be named according to the fastq filenames.

--trim_adapters If true adpter sequences are automatically determined and will be trimmed from reads. If --adapterSeq (string of atapter sequence) or --adapterSeqFile (fasta file with adapter sequences) is provided then adapters will be used as specified (no automatic detection). Default: false

--trim_adapters_RNAseq If true adpter sequences are automatically determined and will be trimmed from RNAseq reads. If --adapterSeqRNAseq (string of atapter sequence) or --adapterSeqFileRNAseq (fasta file with adapter sequences) is provided then adapters will be used as specified (no automatic detection). Default: false

--adapterSeq String of atapter sequence (see --trim_adapers) --adapterSeqFile Fasta file with atapter sequence(s) (see --trim_adapers)

--adapterSeqRNAseq String of atapter sequence (see --trim_adapers_RNAseq) --adapterSeqFileRNAseq Fasta file with atapter sequence(s) (see --trim_adapers_RNAseq)

--mutect2ponFile Panel of Normals file for Mutect2 (https://gatk.broadinstitute.org/hc/en-us/articles/360035890631-Panel-of-Normals-PON-) Default: false

--priorityCaller Set the variant caller used as base for the hc variants. Only variants that are confirmed by any of the two confirming callers (e..g. mutect1, varscan) will be retained. M2 = mutect2, M1 = mutect1, VS = varscan, ST = strelka Default: M2

--minAD Minimum allelic depth (reads covering a variant) Default: 5

--use_NetChop Use NetChop to generate peptides Default: false

--TCR Run mixcr for TCR prediction Default: true

--customHLA Provide a custom HLA types file. The HLA types in this file will be used in addition to those derived from the sequencing data in the WES/WGS/RNAseq fastq files. One type per line in 4 digit format (e.g. HLA-A*01:01)

--HLAHD_DIR Specify the path to your HLA-HD installation. Needed if Class II neoantigens should be predicted.

--HLA_force_RNA Use only RNAseq for HLA typing. Default: false

--HLA_force_DNA Use only WES/WGS for HLA typing. Default: false

--run_HLAHD_RNA Run HLA-HD also on RNAseq. Highly accurate but can be very slow on larger fastq files. Default: false

--disable_OptiType Disable OptiType for HLA typing. If set, HLA-HD or a user supplied custom HLA file must be available (see --HLAHD_DIR and/or --customHLA)

--sex Provide the sex of the sample (XX or Female, XY or Male, None)

--pVACseq_filter_set Can be one of [standard, relaxed, custom]. The standard filter set is using the pVACseq default filters. The relaxed filter set is filtering only for ic50 < 500 & rank < 2 & expn-val > 2. With filter set custom users can define a custom set of filters by providing the desired filters (space separated) using the --pVACseq_custom_filters option. E.g. --pVACseq_filter_set custom --pVACseq_custom_filters "--binding-threshold 250 --percentile-threshold 1". For filter options please see also the pVACseq manual. Default: standard

--pVACseq_custom_filters See --pVACseq_filter_set

Further options: There are many more options that can be set in the params.conf file or specified on the commandline (see conf/params.config)

3. Output

The Pipeline stores its ouput in the following structure:

RESULTS
├── analyses
│   ├── Subject_01
│   │   ├── 01_preprocessing
│   │   ├── 02_alignments
│   │   ├── 03_baserecalibration
│   │   ├── 03_realignment
│   │   ├── 04_expression
│   │   ├── 04_variations
│   │   ├── 05_vep
│   │   ├── 06_proteinseq
│   │   ├── 07_MutationalBurden
│   │   ├── 08_CNVs
│   │   ├── 09_CCF
│   │   ├── 10_HLA_typing
│   │   ├── 11_Fusions
│   │   ├── 12_pVACseq
│   │   ├── 13_mixMHC2pred
│   │   ├── 14_CSiN
│   │   ├── 14_IGS
│   │   ├── 15_BCR_TCR
│   │   └── QC
│   ├── Subject_02
│   │   ├── [...]
│   ├── [...]
│   │   ├── [...]
│   ├── Subject_n
│   │   ├── [...]
├── Documentation
├── neoantigens
│   ├── Subject_ID
│   │   ├── Class_I
│   │   ├── Class_II
│   │   └── Final_HLAcalls
│   ├── Subject_02
│   │   ├── [...]
│   ├── [...]
│   │   ├── [...]
│   ├── Subject_n
│   │   ├── [...]
├── pipeline_info
│   └── icbi
└── supplemental
    ├── 00_prepare_Intervals
    └── 01_prepare_CNVkit

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nextNEOpi: a comprehensive pipeline for computational neoantigen prediction

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