The ProteoGenomics database generation workflow creates different protein databases for ProteoGenomics data analysis.

public 1yr ago Version: 1.0.0 0 bookmarks

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The ProteoGenomics database generation workflow ( pgdb ) use the pypgatk and nextflow to create different protein databases for ProteoGenomics data analysis.

Introduction

nf-core/pgdb is a bioinformatics pipeline to generate proteogenomics databases. pgdb allows users to create proteogenomics databases using EMSEMBL as the reference proteome database. Three different major databases can be attached to the final proteogenomics database:

The reference proteome (ENSEMBL Reference proteome)
Non canonical proteins: pseudo-genes, sORFs, lncRNA.
Variants: COSMIC, cBioPortal, GENOMAD variants

The pipeline allows to estimate decoy proteins with different methods and attach them to the final proteogenomics database.

The pipeline is built using Nextflow , a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation trivial and results highly reproducible.

Quick Start

Install nextflow
Install any of Docker , Singularity , Podman , Shifter or Charliecloud for full pipeline reproducibility (please only use Conda as a last resort; see docs )
Download the pipeline and test it on a minimal dataset with a single command (This run will download the canonical ENSEMBL reference proteome and create proteomics database with it):
```
nextflow run nf-core/pgdb -profile test,<docker/singularity/podman/shifter/charliecloud/conda/institute>
```
Please check nf-core/configs to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use -profile <institute> in your command. This will enable either docker or singularity and set the appropriate execution settings for your local compute environment.
Start running your own analysis!
```
nextflow run nf-core/pgdb -profile <docker/singularity/podman/conda/institute> --ncrna true --pseudogenes true --altorfs true
```
This will create a proteogenomics database with the ENSEMBL reference proteome and non canonical proteins like pseudo genes, non coding rnas or alternative open reading frames.

See usage docs for all of the available options when running the pipeline.

Pipeline Summary

By default, the pipeline currently performs the following:

ProteoGenomics Database

Download protein databases from ENSEMBL
Translate from Genomics Variant databases into ProteoGenomics Databases ( COSMIC , GNOMAD )
Add to a Reference proteomics database, non-coding RNAs + pseudogenes.
Compute Decoy for a proteogenomics databases

Documentation

The nf-core/pgdb pipeline comes with documentation about the pipeline: usage and output .

Credits

nf-core/pgdb was originally written by Husen M. Umer (EMBL-EBI) & Yasset Perez-Riverol (Karolinska Institute)

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines .

For further information or help, don't hesitate to get in touch on the Slack #pgdb channel (you can join with this invite ).

Citations

You can cite the nf-core publication as follows:

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x .

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.

Code Snippets

"""
echo $workflow.manifest.version > v_pipeline.txt
echo $workflow.nextflow.version > v_nextflow.txt
scrape_software_versions.py &> software_versions_mqc.yaml
"""

NextFlow From line 138 of 1.0.0/main.nf

"""
pypgatk_cli.py ensembl-downloader \\
    --config_file $ensembl_downloader_config \\
    --ensembl_name $params.ensembl_name \\
    -sv -sc
"""

NextFlow pypgatk From line 164 of 1.0.0/main.nf

"""
cat $reference_proteome >> reference_proteome.fa
"""

NextFlow From line 184 of 1.0.0/main.nf

"""
cat $a >> total_cdnas.fa
cat $b >> total_cdnas.fa
"""

NextFlow From line 203 of 1.0.0/main.nf

"""
pypgatk_cli.py dnaseq-to-proteindb \\
    --config_file "$ensembl_config" \\
    --input_fasta $x \\
    --output_proteindb ncRNAs_proteinDB.fa \\
    --include_biotypes "${params.biotypes['ncRNA']}" \\
    --skip_including_all_cds --var_prefix ncRNA_
"""

NextFlow pypgatk From line 225 of 1.0.0/main.nf

"""
pypgatk_cli.py dnaseq-to-proteindb \\
    --config_file "$ensembl_config" \\
    --input_fasta "$x" \\
    --output_proteindb pseudogenes_proteinDB.fa \\
    --include_biotypes "${params.biotypes['pseudogene']}" \\
    --skip_including_all_cds \\
    --var_prefix pseudo_
"""

NextFlow pypgatk From line 253 of 1.0.0/main.nf

"""
pypgatk_cli.py dnaseq-to-proteindb \\
    --config_file "$ensembl_config" {{
    --input_fasta "$x" \\
    --output_proteindb altorfs_proteinDB.fa \\
    --include_biotypes "${params.biotypes['protein_coding']}'" \\
    --skip_including_all_cds \\
    --var_prefix altorf_
"""

NextFlow pypgatk From line 282 of 1.0.0/main.nf

"""
pypgatk_cli.py cosmic-downloader \\
    --config_file "$cosmic_config" \\
    --username $params.cosmic_user_name \\
    --password $params.cosmic_password
"""

NextFlow pypgatk From line 315 of 1.0.0/main.nf

"""
pypgatk_cli.py cosmic-to-proteindb \\
    --config_file "$cosmic_config" \\
    --input_mutation $m --input_genes $g \\
    --filter_column 'Histology subtype 1' \\
    --accepted_values $params.cosmic_cancer_type \\
    --output_db cosmic_proteinDB.fa
"""

NextFlow pypgatk From line 340 of 1.0.0/main.nf

"""
pypgatk_cli.py cosmic-to-proteindb \\
    --config_file "$cosmic_config" \\
    --input_mutation $m \\
    --input_genes $g \\
    --filter_column 'Sample name' \\
    --accepted_values $params.cosmic_cellline_name \\
    --output_db cosmic_celllines_proteinDB.fa
"""

NextFlow pypgatk From line 369 of 1.0.0/main.nf

"""
pypgatk_cli.py ensembl-downloader \\
    --config_file $ensembl_downloader_config \\
    --ensembl_name $params.ensembl_name \\
    -sg -sp -sc -sd -sn
"""

NextFlow pypgatk From line 397 of 1.0.0/main.nf

"""
awk 'BEGIN{FS=OFS="\t"}{if(\$1~"#" || (\$5!="" && \$4!="")) print}' $vcf_file > checked_$vcf_file
"""

NextFlow From line 420 of 1.0.0/main.nf

"""
pypgatk_cli.py vcf-to-proteindb \\
    --config_file $e \\
    --af_field "$ensembl_af_field" \\
    --input_fasta $f \\
    --gene_annotations_gtf $g \\
    --vcf $v \\
    --output_proteindb "${v}_proteinDB.fa"  \\
    --var_prefix ensvar \\
    --annotation_field_name 'CSQ'
"""

NextFlow pypgatk From line 446 of 1.0.0/main.nf

"""
gffread -w transcripts.fa -g $f $g
"""

NextFlow gffread From line 482 of 1.0.0/main.nf

"""
awk 'BEGIN{FS=OFS="\t"}{if(\$1=="chrM") \$1="MT"; gsub("chr","",\$1); print}' \\
    $v > ${v.baseName}_changedChrNames.vcf

pypgatk_cli.py vcf-to-proteindb \\
    --config_file $e \\
    --af_field "$af_field" \\
    --input_fasta $f \\
    --gene_annotations_gtf $g \\
    --vcf ${v.baseName}_changedChrNames.vcf \\
    --output_proteindb ${v.baseName}_proteinDB.fa \\
    --annotation_field_name ''
"""

NextFlow pypgatk From line 504 of 1.0.0/main.nf

"""
wget ${g}/gencode.v19.pc_transcripts.fa.gz
wget ${g}/gencode.v19.annotation.gtf.gz
gunzip *.gz
"""

NextFlow From line 540 of 1.0.0/main.nf

"""
gsutil cp $g .
"""

NextFlow From line 562 of 1.0.0/main.nf

"""
zcat $g > ${g}.vcf
"""

NextFlow From line 582 of 1.0.0/main.nf

"""
pypgatk_cli.py vcf-to-proteindb \\
    --config_file $e \\
    --vcf $v \\
    --input_fasta $f \\
    --gene_annotations_gtf $g \\
    --output_proteindb "${v}_proteinDB.fa" \\
    --af_field controls_AF \\
    --transcript_index 6 \\
    --annotation_field_name vep  \\
    --var_prefix gnomadvar
"""

NextFlow Variant Effect Predictor (VEP) pypgatk From line 605 of 1.0.0/main.nf

"""
wget ftp://ftp.ensembl.org/pub/release-75/fasta/homo_sapiens/cds/Homo_sapiens.GRCh37.75.cds.all.fa.gz
gunzip *.gz
"""

NextFlow From line 638 of 1.0.0/main.nf

"""
git clone https://github.com/cBioPortal/datahub.git .
git lfs install --local --skip-smudge
git lfs pull -I public --include "data*clinical*sample.txt"
git lfs pull -I public --include "data_mutations_mskcc.txt"
cat public/*/data_mutations_mskcc.txt > cbioportal_allstudies_data_mutations_mskcc.txt
cat public/*/*data*clinical*sample.txt | \\
    awk 'BEGIN{FS=OFS="\\t"}{if(\$1!~"#SAMPLE_ID"){gsub("#SAMPLE_ID", "\\nSAMPLE_ID");} print}' | \\
    awk 'BEGIN{FS=OFS="\\t"}{s=0; j=0; \\
        for(i=1;i<=NF;i++){ \\
            if(\$i=="CANCER_TYPE_DETAILED") j=1; \\
            if(\$i=="CANCER_TYPE") s=1; \\
        } \\
        if(j==1 && s==0){ \\
            gsub("CANCER_TYPE_DETAILED", "CANCER_TYPE"); \\
        } \\
        print; \\
    }' \\
    > cbioportal_allstudies_data_clinical_sample.txt
"""

NextFlow From line 658 of 1.0.0/main.nf

"""
pypgatk_cli.py cbioportal-downloader \\
    --config_file "$cbioportal_config" \\
    -d "$params.cbioportal_study_id"

tar -xzvf database_cbioportal/${params.cbioportal_study_id}.tar.gz
cat ${params.cbioportal_study_id}/data_mutations_mskcc.txt > cbioportal_allstudies_data_mutations_mskcc.txt
cat ${params.cbioportal_study_id}/data_clinical_sample.txt | \\
    awk 'BEGIN{FS=OFS="\\t"}{if(\$1!~"#SAMPLE_ID"){gsub("#SAMPLE_ID", "\\nSAMPLE_ID");} print}' | \\
    awk 'BEGIN{FS=OFS="\\t"}{s=0; j=0; \\
    for(i=1;i<=NF;i++){ \\
        if(\$i=="CANCER_TYPE_DETAILED") j=1; if(\$i=="CANCER_TYPE") s=1; \\
    } \\
    if(j==1 && s==0){gsub("CANCER_TYPE_DETAILED", "CANCER_TYPE");} print;}' \\
    > cbioportal_allstudies_data_clinical_sample.txt
"""

NextFlow pypgatk From line 679 of 1.0.0/main.nf

"""
pypgatk_cli.py cbioportal-to-proteindb \\
    --config_file $cbioportal_config \\
    --input_mutation $m \\
    --input_cds $g \\
    --clinical_sample_file $s \\
    --filter_column $params.cbioportal_filter_column \\
    --accepted_values $params.cbioportal_accepted_values \\
    --output_db cbioPortal_proteinDB.fa
"""

NextFlow pypgatk From line 715 of 1.0.0/main.nf

"""
cat proteindb* > merged_databases.fa
"""

NextFlow From line 747 of 1.0.0/main.nf

"""
pypgatk_cli.py ensembl-check \\
    -in "$file" \\
    --config_file "$e" \\
    -out database_clean.fa \\
    --num_aa "$params.minimum_aa" \\
    "$stop_codons"
"""

NextFlow pypgatk From line 779 of 1.0.0/main.nf

"""
pypgatk_cli.py generate-decoy \\
    --method "$params.decoy_method" \\
    --enzyme "$params.decoy_enzyme" \\
    --config_file $protein_decoy_config \\
    --input_database $f \\
    --decoy_prefix "$params.decoy_prefix" \\
    --output_database decoy_database.fa
"""

NextFlow pypgatk From line 811 of 1.0.0/main.nf

"""
markdown_to_html.py $output_docs -o results_description.html
"""

NextFlow From line 838 of 1.0.0/main.nf

"""
echo $workflow.manifest.version > v_pipeline.txt
echo $workflow.nextflow.version > v_nextflow.txt
scrape_software_versions.py &> software_versions_mqc.yaml
"""

NextFlow From line 138 of master/main.nf

"""
pypgatk_cli.py ensembl-downloader \\
    --config_file $ensembl_downloader_config \\
    --ensembl_name $params.ensembl_name \\
    -sv -sc
"""

NextFlow pypgatk From line 164 of master/main.nf

"""
cat $reference_proteome >> reference_proteome.fa
"""

NextFlow From line 184 of master/main.nf

"""
cat $a >> total_cdnas.fa
cat $b >> total_cdnas.fa
"""

NextFlow From line 203 of master/main.nf

"""
pypgatk_cli.py dnaseq-to-proteindb \\
    --config_file "$ensembl_config" \\
    --input_fasta $x \\
    --output_proteindb ncRNAs_proteinDB.fa \\
    --include_biotypes "${params.biotypes['ncRNA']}" \\
    --skip_including_all_cds --var_prefix ncRNA_
"""

NextFlow pypgatk From line 225 of master/main.nf

"""
pypgatk_cli.py dnaseq-to-proteindb \\
    --config_file "$ensembl_config" \\
    --input_fasta "$x" \\
    --output_proteindb pseudogenes_proteinDB.fa \\
    --include_biotypes "${params.biotypes['pseudogene']}" \\
    --skip_including_all_cds \\
    --var_prefix pseudo_
"""

NextFlow pypgatk From line 253 of master/main.nf

"""
pypgatk_cli.py dnaseq-to-proteindb \\
    --config_file "$ensembl_config" {{
    --input_fasta "$x" \\
    --output_proteindb altorfs_proteinDB.fa \\
    --include_biotypes "${params.biotypes['protein_coding']}'" \\
    --skip_including_all_cds \\
    --var_prefix altorf_
"""

NextFlow pypgatk From line 282 of master/main.nf

"""
pypgatk_cli.py cosmic-downloader \\
    --config_file "$cosmic_config" \\
    --username $params.cosmic_user_name \\
    --password $params.cosmic_password
"""

NextFlow pypgatk From line 315 of master/main.nf

"""
pypgatk_cli.py cosmic-to-proteindb \\
    --config_file "$cosmic_config" \\
    --input_mutation $m --input_genes $g \\
    --filter_column 'Histology subtype 1' \\
    --accepted_values $params.cosmic_cancer_type \\
    --output_db cosmic_proteinDB.fa
"""

NextFlow pypgatk From line 340 of master/main.nf

"""
pypgatk_cli.py cosmic-to-proteindb \\
    --config_file "$cosmic_config" \\
    --input_mutation $m \\
    --input_genes $g \\
    --filter_column 'Sample name' \\
    --accepted_values $params.cosmic_cellline_name \\
    --output_db cosmic_celllines_proteinDB.fa
"""

NextFlow pypgatk From line 369 of master/main.nf

"""
pypgatk_cli.py ensembl-downloader \\
    --config_file $ensembl_downloader_config \\
    --ensembl_name $params.ensembl_name \\
    -sg -sp -sc -sd -sn
"""

NextFlow pypgatk From line 397 of master/main.nf

"""
awk 'BEGIN{FS=OFS="\t"}{if(\$1~"#" || (\$5!="" && \$4!="")) print}' $vcf_file > checked_$vcf_file
"""

NextFlow From line 420 of master/main.nf

"""
pypgatk_cli.py vcf-to-proteindb \\
    --config_file $e \\
    --af_field "$ensembl_af_field" \\
    --input_fasta $f \\
    --gene_annotations_gtf $g \\
    --vcf $v \\
    --output_proteindb "${v}_proteinDB.fa"  \\
    --var_prefix ensvar \\
    --annotation_field_name 'CSQ'
"""

NextFlow pypgatk From line 446 of master/main.nf

"""
gffread -w transcripts.fa -g $f $g
"""

NextFlow gffread From line 482 of master/main.nf

"""
awk 'BEGIN{FS=OFS="\t"}{if(\$1=="chrM") \$1="MT"; gsub("chr","",\$1); print}' \\
    $v > ${v.baseName}_changedChrNames.vcf

pypgatk_cli.py vcf-to-proteindb \\
    --config_file $e \\
    --af_field "$af_field" \\
    --input_fasta $f \\
    --gene_annotations_gtf $g \\
    --vcf ${v.baseName}_changedChrNames.vcf \\
    --output_proteindb ${v.baseName}_proteinDB.fa \\
    --annotation_field_name ''
"""

NextFlow pypgatk From line 504 of master/main.nf

"""
wget ${g}/gencode.v19.pc_transcripts.fa.gz
wget ${g}/gencode.v19.annotation.gtf.gz
gunzip *.gz
"""

NextFlow From line 540 of master/main.nf

"""
gsutil cp $g .
"""

NextFlow From line 562 of master/main.nf

"""
zcat $g > ${g}.vcf
"""

NextFlow From line 582 of master/main.nf

"""
pypgatk_cli.py vcf-to-proteindb \\
    --config_file $e \\
    --vcf $v \\
    --input_fasta $f \\
    --gene_annotations_gtf $g \\
    --output_proteindb "${v}_proteinDB.fa" \\
    --af_field controls_AF \\
    --transcript_index 6 \\
    --annotation_field_name vep  \\
    --var_prefix gnomadvar
"""

NextFlow Variant Effect Predictor (VEP) pypgatk From line 605 of master/main.nf

"""
wget ftp://ftp.ensembl.org/pub/release-75/fasta/homo_sapiens/cds/Homo_sapiens.GRCh37.75.cds.all.fa.gz
gunzip *.gz
"""

NextFlow From line 638 of master/main.nf

"""
git clone https://github.com/cBioPortal/datahub.git .
git lfs install --local --skip-smudge
git lfs pull -I public --include "data*clinical*sample.txt"
git lfs pull -I public --include "data_mutations_mskcc.txt"
cat public/*/data_mutations_mskcc.txt > cbioportal_allstudies_data_mutations_mskcc.txt
cat public/*/*data*clinical*sample.txt | \\
    awk 'BEGIN{FS=OFS="\\t"}{if(\$1!~"#SAMPLE_ID"){gsub("#SAMPLE_ID", "\\nSAMPLE_ID");} print}' | \\
    awk 'BEGIN{FS=OFS="\\t"}{s=0; j=0; \\
        for(i=1;i<=NF;i++){ \\
            if(\$i=="CANCER_TYPE_DETAILED") j=1; \\
            if(\$i=="CANCER_TYPE") s=1; \\
        } \\
        if(j==1 && s==0){ \\
            gsub("CANCER_TYPE_DETAILED", "CANCER_TYPE"); \\
        } \\
        print; \\
    }' \\
    > cbioportal_allstudies_data_clinical_sample.txt
"""

NextFlow From line 658 of master/main.nf

"""
pypgatk_cli.py cbioportal-downloader \\
    --config_file "$cbioportal_config" \\
    -d "$params.cbioportal_study_id"

tar -xzvf database_cbioportal/${params.cbioportal_study_id}.tar.gz
cat ${params.cbioportal_study_id}/data_mutations_mskcc.txt > cbioportal_allstudies_data_mutations_mskcc.txt
cat ${params.cbioportal_study_id}/data_clinical_sample.txt | \\
    awk 'BEGIN{FS=OFS="\\t"}{if(\$1!~"#SAMPLE_ID"){gsub("#SAMPLE_ID", "\\nSAMPLE_ID");} print}' | \\
    awk 'BEGIN{FS=OFS="\\t"}{s=0; j=0; \\
    for(i=1;i<=NF;i++){ \\
        if(\$i=="CANCER_TYPE_DETAILED") j=1; if(\$i=="CANCER_TYPE") s=1; \\
    } \\
    if(j==1 && s==0){gsub("CANCER_TYPE_DETAILED", "CANCER_TYPE");} print;}' \\
    > cbioportal_allstudies_data_clinical_sample.txt
"""

NextFlow pypgatk From line 679 of master/main.nf

"""
pypgatk_cli.py cbioportal-to-proteindb \\
    --config_file $cbioportal_config \\
    --input_mutation $m \\
    --input_cds $g \\
    --clinical_sample_file $s \\
    --filter_column $params.cbioportal_filter_column \\
    --accepted_values $params.cbioportal_accepted_values \\
    --output_db cbioPortal_proteinDB.fa
"""

NextFlow pypgatk From line 715 of master/main.nf

"""
cat proteindb* > merged_databases.fa
"""

NextFlow From line 747 of master/main.nf

"""
pypgatk_cli.py ensembl-check \\
    -in "$file" \\
    --config_file "$e" \\
    -out database_clean.fa \\
    --num_aa "$params.minimum_aa" \\
    "$stop_codons"
"""

NextFlow pypgatk From line 779 of master/main.nf

"""
pypgatk_cli.py generate-decoy \\
    --method "$params.decoy_method" \\
    --enzyme "$params.decoy_enzyme" \\
    --config_file $protein_decoy_config \\
    --input_database $f \\
    --decoy_prefix "$params.decoy_prefix" \\
    --output_database decoy_database.fa
"""

NextFlow pypgatk From line 811 of master/main.nf

"""
markdown_to_html.py $output_docs -o results_description.html
"""

NextFlow From line 838 of master/main.nf

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Created: 1yr ago

Updated: 1yr ago

Maitainers: public

URL: https://nf-co.re/pgdb

Name: pgdb

Version: 1.0.0

Badge:

Insert copied code into your website to add a link to this workflow.

License: None

Keywords:

FASTA raw sequence reads Variant calling genetic variants gffread pypgatk Variant Effect Predictor (VEP) Proteogenomics

Refs:

https://nf-co.re/pgdb

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