Goal: given an SRA ID, prioritize and quantify variants with respect to immunogenicity (single score) + variant annotation
- use pvacseq to generate the sequences around variants (wt/mutant)
- use FRED2 to make binding predictions
- given the different prediction score, make one immunogenicity score
- Annotate VCF
- Extract peptide sequences
- Define sample's MHC alleles
- Collect MHC-peptide affinity predictions
- Add our own classifier score
More info:
- Meaning of the different immunogenicity scores returned by FRED2
- How to run and work with the Docker image
- Notes on individual installations
nohup variant_effect_predictor.pl \
--input_file /home/data/vcf/hisat_tags_output_SRR1616919.sorted.vcf \
--format vcf \
--terms SO --offline --force_overwrite \
--plugin Wildtype --plugin Downstream \
--dir /home/data/vep --vcf --symbol \
--fork 16 --coding_only --no_intergenic \
--output_file /home/data/imm/hisat_tags_output_SRR1616919.sorted.annotated.vcf &
The genome information stored in the vcf file need to be translated into corresponding peptide sequences.
For this, we use parts of pVacSeq, which generates a csv file with 21-mers surrounding the mutation (both, WT and mutant form)
cd $HOME
git clone https://github.com/NCBI-Hackathons/Cancer_Epitopes_CSHL.git
source activate python3
cd $HOME/Cancer_Epitopes_CSHL/src
python -c 'import generate_fasta; generate_fasta.generate_fasta_dataframe("/home/devsci7/test.output.2", "/home/devsci7/step2.csv", 21,9)'
Tools for HLA genotyping typically re-align the raw reads in order to identify the HLA type from RNA-seq.
To obtain the reads roughly aligned to these genes we need to define the region and specify it during the alignment process.
The MHC complex consists of more than 200 genes located close together on chromosome 6.
The script hla_type.sh extracts reads overlapping with the MHC locus and turns them into two fastq files.
These reads are then re-aligned and analyzed by OptiType
src/hla_type.sh -b /home/data/hisat_tags_output_SRR1616919.sorted.bam \
-r NC_000006.12:29600000-33500000 -o test --path /opt/samtools/1.3.1/bin/
Run the script: src/imm_predict/fred2_allele_prediction.py to compute the MHC binding affinity predictions for each reference and alternative allele.
python2 ./src/imm_predict/fred2_allele_prediction.py \
./pvacseq_table.csv ./variant_immunogenicity.csv fred2_allele_prediction.py [--alleles=<alleles_list>] FILE_IN FILE_OUT
fred2_allele_prediction.py -h | --help
| Argument | Explanation |
|---|---|
| FILE IN (req.) | Input csv file with... ??? |
| FILE OUT (req.) | Name for output csv file |
| alleles | Comma separated list of target alleles, e.g., --alleles="B*27:20,B*83:01,A*32:15" [Default: use all] |
- GOAL: classifier learnt on peptides with reported immunogenic cancer neoantigens (WT sequences without immunogenicity)
To play around with the data sets, here are the current workflows:
# read in the data (this expects that you're in Cancer_Epitopes_CSHL;
# just check the paths that are hard-coded at the moment within the
# script
Rscript --vanilla --slave src/imm_explore/0-read.R
# this should have created data/immunogenic_SNVs-training_sets.csv
export TMPDIR=/tmp/
PYTHON=/opt/modules/i12g/anaconda/3-4.1.1/envs/python27/bin/python
# calculate the binding affinities
PYTHON src/imm_explore/fred2_design_matrix.py \
--input=data/immunogenic_SNVs-training_sets.csv \
--output=data/immunogenic_SNVs-model_data.csv
This needs some discussion and perhaps work?
Given a FASTA file with all human proteins, compute the immunogenicity for all posible 9-mer peptides.
NOTE: This will take a very long time to compute!
Script: src/imm_predict/fred2_background.py.
fred2_background.py [--alleles=<alleles_list> --top_N=N] FILE_IN FILE_OUT
fred2_background.py -h | --help
| Argument | Explanation |
|---|---|
| FILE IN (req.) | Input FASTA file with all human proteins, can be retrieved from ENSEMBL |
| FILE OUT (req.) | Name for output csv file |
| top N | Number of top N proteins to compute the background for. [Default: all]. |
| alleles | Comma separated list of target alleles, e.g., --alleles="B*27:20,B*83:01,A*32:15" [Default: use all] |
python2 ./src/imm_predict/fred2_background.py \
./Homo_sapiens.GRCh38.pep.all.fixheader.fa ./background_peptides.csvNot sure where this belongs?
cp /home/devsci7/step2.fasta /home/data/imm