nf-hla-neo
nexflow pipeline to predict neoantigens from WGS
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Pipeline to predict neoantigens from WGS of T/N pairs
#using a tn_pairs file
nextflow run iarcbioinfo/nf-hla-neo -r v1.0 \
-profile singularity --ref chr6.mhc.fa \
--tn_file cohort_neoantigen.tsv --cram_dir cram \
--vcf_dir vcfs --vep_dir vep-db-99 --output_folder results_hla_neo
You can avoid installing all the external software by only installing Docker or singularity. See the IARC-nf repository for more information.
| Type | Description |
|---|---|
| --tn_file | [file] File containing list of T/N bam/cram files to be processed |
| --cram_dir | [dir] directory where the BAM or CRAM file are stored |
| --vcf_dir | [dir] directory where the VCF files are stored |
| --vep_dir | [dir] directory containing VEP database for annotation [hg38, GENCODE 33] |
| --ref | [file] fasta file of chr6 of reference genome [chr6-hg38.fa], shold be indexed with BWA-mem |
A text file tabular separated, with the following header:
id vcf normal_cram normal_id tumor_id
sample1 sample1.vcf.gz sample1_N.cram NORMAL1 TUMOR1
sample2 sample2.vcf.gz sample2_N.cram sample2_N.cram NORMAL2 TUMOR2
sample3 sample3.vcf.gz sample3_N.cram sample3_N.cram NORMAL3 TUMOR3_1,TUMOR3_2
| Name | type | Description |
|---|---|---|
| --pvactools_predictors | [string] | predictions tools to compute neoantigens [def:all_class_i,all_class_ii or NetMHCpan,NetMHCIIpan] |
| --bam | [flag] | active bam mode [def:cram] |
| --output_folder | [string] | name of output folder |
| --cpu | [Integer] | Number of CPUs[def:2] |
| --mem | [Integer] | Max memory [def:8Gb] |
results
└── xHLA # HLA-typing predictions
│ ├── report-MESO_001-hla.json
│ ├── report-MESO_002-hla.json
│ ├── report-MESO_003-hla.json
│ ├── report-MESO_004-hla.json
│ ├── report-MESO_005-hla.json
│ ├── report-MESO_006-hla.json
│ ├── report-MESO_007-hla.json
│ ├── report-MESO_008-hla.json
│ ├── report-MESO_009-hla.json
├── VEP # Annotation of variant impact
│ ├── MESO_001.vep.vcf
│ ├── MESO_002.vep.vcf
│ ├── MESO_003.vep.vcf
│ ├── MESO_004.vep.vcf
│ ├── MESO_005.vep.vcf
│ ├── MESO_006.vep.vcf
|
├── pVACTOOLS #pvactools predictions
│ ├── MESO_002.pvactools.log #pvactools log
│ ├── MESO_002_T1_pvactools
│ │ ├── combined # neoatigens for class I and class II
│ │ │ ├── B00JALW.all_epitopes.aggregated.tsv # raw predictions aggregated
│ │ │ ├── B00JALW.all_epitopes.tsv # raw predictions
│ │ │ └── B00JALW.filtered.tsv # filtered predictions
│ │ ├── MHC_Class_I #neoatigens for class I
│ │ │ ├── B00JALW.all_epitopes.aggregated.tsv
│ │ │ ├── B00JALW.all_epitopes.tsv
│ │ │ ├── B00JALW.filtered.tsv
│ │ │ └── log
│ │ │ └── inputs.yml
│ │ └── MHC_Class_II #neoatigens for class II
│ │ ├── B00JALW.all_epitopes.aggregated.tsv
│ │ ├── B00JALW.all_epitopes.tsv
│ │ ├── B00JALW.fasta
│ │ ├── B00JALW.filtered.tsv
│ │ └── log
| └── inputs.yml
├── nf-pipeline_info # Nextflow information directory
├── hla-neo_report.html
├── hla-neo_timeline.html
├── hla-neo_trace.txt
└── run_parameters_report.txt # Custom file providing info for software versions and calling parameters
The first time is necesary to get a local copy of the vep database, you can achieve this by running the following command within the vep singularity container:
#get the singularity container
singularity pull docker://docker.io/iarcbioinfo/ensembl-vep:v1.0
#open a shell and run the folloing command
singularity shell ensembl-vep_v1.0.sif
#get a local copy of the vep cache database (gencode v33)
vep_install -a cf -s homo_sapiens -y GRCh38 -c vep-db-99 --CONVERT
To perform the HLA-typing this pipeline extract reads from the CRAM/BAM file and remap them to the chr6 of GRCh38 using BWA-MEM (0.7.15-r1140), hence the chr6.fa of GRCh38 should be indexed (BWA index) to perform the mapping of the reads.
The first time that the container is built from the docker image, the TMPDIR should be defined in a non parallel file-system, you can set this like:
export TMPDIR=/tmp
| Name | Description | |
|---|---|---|
| Matthieu Foll* | [email protected] | Developer to contact for support (link to specific gitter chatroom) |
| Alex Di Genova | [email protected] | Developer |
Content type
Image
Digest
Size
530 MB
Last updated
about 5 years ago
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