Pypgatk: Python Tools for ProteoGenomics

The Pypgatk framework and library provides a set of tools to perform proteogenomics analysis. In order to execute a task in pypgatk the user should use a COMMAND to perform the specific task and specify the task arguments:

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$: pypgatk_cli.py -h
   Usage: pypgatk_cli.py [OPTIONS] COMMAND [ARGS]...

   This is the main tool that gives access to all commands and options provided by the pypgatk_cli

   Options:
      -h, --help  Show this message and exit.

   Commands:
     ensembl-downloader       Command to download the ensembl information
     cbioportal-downloader    Command to download the the cbioportal studies
     cosmic-downloader        Command to download the cosmic mutation database
     dnaseq-to-proteindb      Command to translate sequences generated from RNA-seq and DNA sequences
     vcf-to-proteindb         Command to translate genomic variatns to protein sequences
     cbioportal-to-proteindb  Command to translate cbioportal mutation data into proteindb
     cosmic-to-proteindb      Command to translate Cosmic mutation data into proteindb
     generate-decoy           Command to generate decoy database from a proteindb
     ensembl-check                Command to fix protein sequences to only contain amino acid sequences

Installation

Clone the source code for pypgatk from source:

git clone https://github.com/bigbio/py-pgatk.git

pypgatk depends on several Python3 packages that are listed in requirements.txt, once in the downloaded directory install the dependencies using pip:

pip install -r requirements.txt

Install the pypgatk package from source:

python setup.py install

Data Downloader Tools

The Data downloader is a set of COMMANDs to download data from different Genomics data providers including ENSEMBL, COSMIC and cBioPortal.

Downloading ENSEMBL Data

Downloading data from ENSEMBL can be done using the command ensembl_downloader. The current tool enables downloading the following files for any taxonomy that is available ENSEMBL:

  • GTF
  • Protein Sequence (FASTA)
  • CDS (FASTA)
  • CDNA sequences (FASTA)
  • Non-coding RNA sequences (FASTA)
  • Nucleotide Variation (VCF)
  • Genome assembly DNA sequences (FASTA)

Command Options

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$: python pypgatk_cli.py ensembl-downloader -h
   Usage: pypgatk_cli.py ensembl-downloader [OPTIONS]

   This tool enables to download from ENSEMBL ftp the FASTA, GTF and VCF files

    Required parameters::
     -c, --config_file TEXT          Configuration file for the ensembl data downloader pipeline
     -o, --output_directory TEXT     Output directory for the peptide databases

   Optional parameters:
     -l, --list_taxonomies TEXT      List the available species from Ensembl, users can find the desired taxonomy identifier from this list.
     -fp, --folder_prefix_release    TEXT Output folder prefix to download the data
     -t, --taxonomy TEXT             Taxonomy identifiers (comma separated) that will be use to download the data from Ensembl
             -sv, --skip_vcf                 Skip the vcf file during the download
     -sg, --skip_gtf                 Skip the gtf file during the download
     -sp, --skip_protein             Skip the protein fasta file during download
     -sc, --skip_cds                 Skip the CDS file download
             -sn, --skip_ncrna               Skip the ncRNA file download
     -sdn, --skip_cdna               Skip the cDNA file download
     -sd, --skip_dna                 Skip the DNA file download
     -h, --help                      Show this message and exit.

Examples

  • List all species without downloading any data:

    python pypgatk_cli.py ensembl-downloader -l -sv -sg -sp -sc -sd -sn

  • Download all files except cDNA for Tureky (species id=9103, note that th species id cab be obtained from the list above):

    python pypgatk_cli.py ensembl-downloader -t 9103 -sd -o ensembl_files

  • [To be implemented] Download CDS file for Humans (species id=9606) from release 94 and genome assembly GRCh37

    python pypgatk_cli.py ensembl-downloader -t 9606 -sv -sg -sp -sd -sn -o ensembl_files --release 94 --assembly GRCh37

Note

By default the command ensembl-downloader downloads all datasets for all species from the latest ENSEMBL release. To limit the download to a particular species specify the species identifier using the -t option. To list all available species run the command with -l (--list_taxonomies) option.

Note

Any of the file types can be skipped using the corresponding option. For example, to avoid downloading the protein sequence fasta file, use the argument --skip_protein. Also, note that not all file types exists for all species so obviously the downloaded files depends on availabiliy of the dataset in ENSEMBL.

Hint

a VCF file per chromosome is downloaded for homo sapiens due to the large file size they have been distributed this way by ENSEMBL. For other species, a single VCF including all chromosomes is downloaded.

Downloading COSMIC Data.

Downloading mutation data from COSMIC is performed using the COMMAND cosmic-downloader. The current COMMAND allows users to download the following files:

  • Cosmic mutation file (CosmicMutantExport)
  • Cosmic all genes (All_COSMIC_Genes)

Command Options

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$: python pypgatk_cli.py cosmic-downloader -h
   Usage: pypgatk_cli.py cosmic-downloader [OPTIONS]

   Required parameters:
     -u, --username TEXT          Username for cosmic database -- please if you dont have one register here (https://cancer.sanger.ac.uk/cosmic/register)
     -p, --password TEXT          Password for cosmic database -- please if you dont have one register here (https://cancer.sanger.ac.uk/cosmic/register)

        Optional parameters:
     -c, --config_file TEXT       Configuration file for the ensembl data downloader pipeline
     -o, --output_directory TEXT  Output directory for the peptide databases
     -h, --help                   Show this message and exit.

Note

In order to be able to download COSMIC data, the user should provide a user and password. Please first register in COSMIC database (https://cancer.sanger.ac.uk/cosmic/register).

Examples

  • Downlaod CosmicMutantExport.tsv.gz and All_COSMIC_Genes.fasta.gz:

    python pypgatk_cli.py cosmic-downloader -u userName -p passWord -c config/cosmic_config.yaml -o cosmic_files

Downloading cBioPortal Data.

Downloading mutation data from cBioPortal is performed using the command cbioportal-downloader. cBioPortal stores mutation data from multiple studies (https://www.cbioportal.org/datasets). Each dataset in cBioPortal has an associated study_id.

Command Options

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$: python3.7 pypgatk_cli.py cbioportal-downloader -h
   Usage: pypgatk_cli.py cbioportal-downloader [OPTIONS]

   Parameters:
     -c, --config_file TEXT Configuration file for the ensembl data downloader pipeline
     -o, --output_directory TEXT  Output directory for the peptide databases
     -l, --list_studies           Print the list of all the studies in cBioPortal (https://www.cbioportal.org)
     -d, --download_study TEXT    Download a specific Study from cBioPortal -- (all to download all studies)
     -h, --help                   Show this message and exit.

Note

The argument -l (--list_studies) allows the user to list all the studies stored in cBioPortal. The -d (--download_study) argument can be used to obtain mutation data from a particular study.

Examples

  • Download data for study ID blca_mskcc_solit_2014:

    python pypgatk_cli.py cbioportal-downloader -d blca_mskcc_solit_2014 -o cbiportal_files

  • Download data for all studies in cBioPortal:

    python pypgatk_cli.py cbioportal-downloader -d all -o cbioportal_files

If you face issues downloading all studies from cBioPortal using the cbioportal-downloader, please download the studies from the data hub through git-lfs which is used to download large files from gitHub repositories, see installation instructions:.

Following instructions given on the datahub repositority, download the entire list of datasets using:

git clone https://github.com/cBioPortal/datahub.git
cd datahub
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"

Generate Protein Databases

The Pypgatk framework provides a set of tools (COMMAND) to generate protein databaseas in FASTA format from DNA sequences, variants, and mutations. In order to perform this task, we have implemented multiple commands depending on data type provided by the user and the public data providers (cBioPortal, COSMIC and ENSEMBL).

Cosmic Mutations to Protein Sequences

COSMIC the Catalogue of Human Somatic Mutations in Cancer – is the world’s largest source of expert manually curated somatic mutation information relating to human cancers. The command cosmic-to-proteindb converts the cosmic somatic mutations file into a protein sequence database file.

Command Options

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$: python pypgatk_cli.py cosmic-to-proteindb -h
   Usage: pypgatk_cli.py cosmic-to-proteindb [OPTIONS]

   Required parameters:
     -in, --input_mutation TEXT   Cosmic Mutation data file
     -fa, --input_genes TEXT      All Cosmic genes
     -out, --output_db TEXT       Protein database including all the mutations

   Optional parameters:
     -c, --config_file TEXT       Configuration file for the cosmic data pipelines
     -f, --filter_column          Column name to use for filtering or splitting mutations by, default value is ``Primary site``
     -a, --accepted_values        Only consider mutations from records that belong to these groups as specified by ``-filter_column`` option, by default mutations from all groups are considered (default ``all``)
     -s,     --split_by_filter_column Generate a proteinDB output file for each group in the mutations file (affected by ``--filter_column``) (default ``False``)
     -h, --help                   Show this message and exit.

The file input of the tool -in (--input_mutation) is the cosmic mutation data file. The genes file -fa (--input_genes) contains the original CDS sequence for all genes used by the COSMIC team to annotate the mutations. Use cosmic-downloader to obtain the input files from COSMIC.

The output of the tool is a protein fasta file and is written in the following path -out (--output_db)

Examples

  • Generate cancer-type specific protein databases. For each cancer type in COSMIC generate a protein database based on the Primary site given in the mutations file:

    python pypgatk_cli.py cosmic-to-proteindb -in CosmicMutantExport.tsv -fa All_COSMIC_Genes.fasta -out cosmic_proteinDB.fa --split_by_filter_column

  • Generate cell-line specific protein databases. For each cell line in COSMIC cell lines generate a protein database based on the Sample name given in the mutations file:

    python pypgatk_cli.py cosmic-to-proteindb -in CosmicCLP_MutantExport.tsv -fa All_CellLines_Genes.fasta -out cosmicCLP_proteinDB.fa --split_by_filter_column --filter_column 'Sample name'

cBioPortal Mutations to Protein Sequences

The cBioPortal for Cancer Genomics provides visualization, analysis and download of large-scale cancer genomics data sets. The available datasets can be viewed in this web page (https://www.cbioportal.org/datasets). The command cbioportal-to-proteindb converts the bcioportal mutations file into a protein sequence database file.

Command Options

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$: python pypgatk_cli.py cbioportal-to-proteindb -h
   Usage: pypgatk_cli.py cbioportal-to-proteindb [OPTIONS]

    Required parameters:
     -c, --config_file TEXT           Configuration for cBioportal
     -in, --input_mutation TEXT       Cbioportal mutation file
     -fa, --input_cds TEXT            CDS genes from ENSEMBL database
     -out, --output_db TEXT           Protein database including the mutations

    Optional parameters:
     -f, --filter_column TEXT         Column in the VCF file to be used for filtering or splitting mutations
     -a, --accepted_values TEXT       Limit mutations to groups (values) (tissue type, sample name, etc) considered for generating proteinDBs, by default mutations from all records are considered
     -s, --split_by_filter_column     Use this flag to generate a proteinDB per group as specified in the filter_column, default is False
     -cl, --clinical_sample_file TEXT  Clinical sample file that contains the cancery type per sample identifier (required when ``-t`` or ``-s`` is given).
     -h, --help                       Show this message and exit.

Note

The clinical sample file for each mutation file can be found under the same directory as the mutation file downloaded from cBioportal (It should have at least two columns named: Cancer Type and Sample Identifier). The file is only needed if generating tissue type databases is desired (that is when -s or -a is given).

The file input of the tool -in (--input_mutation) is the cbioportal mutation data file. An example is given in cbioportal-downloader showing how to obtain the mutations file for a particular study. The CDS sequence for all genes input file -fa (--input_genes) can be obtained using the ENSEMBL CDS files, see this example. The output of the tool is a protein fasta file and it is written in the following path -out (--output_db)

Note

The cBioportal mutations are aligned to the hg19 assembly, make sure that the correct genome assembly is selected for the download.

Examples

  • translate mutations from Bladder samples in studyID: blca_mskcc_solit_2014 (use cbioportal-downloader to download the study, then extract the content of the downloaded file):

    python pypgatk_cli.py cbioportal-to-proteindb --config_file config/cbioportal_config.yaml --input_cds human_hg19_cds.fa  --input_mutation data_mutations_mskcc.txt --clinical_sample_file data_clinical_sample.txt --output_db bladder_proteindb.fa

Variants (VCF) to Protein Sequences

Variant Calling Format (VCFv4.1) is a text file representing genomic variants.

The vcf_to_proteindb COMMAND takes a VCF file and a GTF (Gene annotations) file to translates the genomic variants in the VCF that affect protein-coding transcripts.

Command Options

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$: python pypgatk_cli.py vcf-to-proteindb -h
   Usage: pypgatk_cli.py vcf-to-proteindb [OPTIONS]

   Required parameters:
     -c, --config_file TEXT      Configuration for VCF conversion parameters
     -v, --vcf                               VCF file containing the genomic variants
     -g, --gene_annotations_gtf  Gene models in the GTF format that will be used to extract protein-coding transcripts
     -f, --input_fasta               Fasta sequences for the transripts in the GTF file used to annotated the VCF
     -o, --output_proteindb      Output file to write the resulting variant protein sequences

   Options:
     --translation_table INTEGER     Translation table (Default 1). Please see <https://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi> for identifiers of translation tables.
     --mito_translation_table INTEGER        Mito_trans_table (default 2), also from <https://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi>
     --var_prefix TEXT       String to add as prefix for the variant peptides
     --report_ref_seq        In addition to variant peptides, also report the reference peptide from the transcripts overlapping the variant
     --annotation_field_name TEXT    Annotation Field name found in the INFO column, e.g CSQ or vep, set to empty if the VCF is not annotated (default is CSQ)
     --af_field TEXT Field name in the VCF INFO column that shows the variant allele frequency (VAF, default is none).
     --af_threshold FLOAT      Minium allele frequency threshold for considering the variants
     --transcript_index INTEGER      Index of transcript ID in the annotated columns in the VCF INFO field that is when the VCF file is alerady annotated, affected by --annotation_field_name (separated by |) (default is 3)
     --consequence_index INTEGER     Index of consequence in the annotated columns in the VCF INFO field that is when the VCF file is alerady annotated, affected by --annotation_field_name (separated by |) (default is 1)
     --include_consequences TEXT     Consider variants that have one of these consequences, affected by --annotation_field_name  (default is all) (for the list of consequences see: https://www.ensembl.org/info/genome/variation/prediction/predicted_data.html.
     --exclude_consequences TEXT     Variants with these consequences will not be considered for translation, affected by --annotation_field_name  (default: downstream_gene_variant, upstream_gene_variant, intergenic_variant, intron_variant, synonymous_variant)
     --skip_including_all_cds        By default any affected transcript that has a defined CDS will be translated, this option disables this features instead it only depends on the specified biotypes
     --ignore_filters        Enabling this option causes all variants to be parsed. By default only variants that have not failed any filters will be processed (FILTER field is PASS, None, .) or if the filters are subset of the accepted_filters (default is False)
     --accepted_filters TEXT Accepted filters for variant parsing
     -h, --helP              Show this message and exit.

The file input of the tool --vcf is a VCF file that can be provided by the user or obtained from ENSEMBL using ensembl_downloader, see an example here. The gene_annotations_gtf file can also be obtained with the ensembl_downloader.

The --input_fasta file contains the CDS and DNA sequences for all genes present in the GTF file. This file can be generated from the GTF file using the gffread tool as follows:

$: gffread -F -w input_fasta.fa -g genome.fa gene_annotations_gtf

The output of the tool is a protein fasta file and is written in the following path --output_proteindb.

Examples

  • Translate human missense variants from ENSEMBL VCFs that have a minimum AF 5%:

    python pypgatk_cli.py vcf-to-proteindb
    --vcf homo_sapiens_incl_consequences.vcf
    --input_fasta transcripts.fa
    --gene_annotations_gtf genes.gtf
    --include_consequences missense_variant
    --af_field MAF
    --af_threshold 0.05
    --output_proteindb var_peptides.fa

Note

  • Translate human missense variants or inframe_insertion from gnoMAD VCFs that have a minmum 1% allele frquency in control samples:

    python pypgatk_cli.py vcf-to-proteindb
   --vcf gnmad_genome.vcf
   --input_fasta gencode.fa
   --gene_annotations_gtf gencode.gtf
   --include_consequences missense_variant,frameshift_insert
   --annotation_field_name vep
   --af_threshold 0.01
   --af_field control_af
   --transcript_index 6

Hint

  • vcf-to-proteindb considers transcript that have a coding sequence which includes all protein_coding transcripts.
  • The provided VCF file has some specific properties: the annotation field is specified with the string vep hence the --annotation_field_name parameter, the transcriptat the sixth position in the annotation field, and since gnomAD collects variants from many sources it provides allele frequencies across many many sub-populations and sub-groups, in this case the goal is to use only variants that are common within control samples therefroe the --af_field is set to control_af.
  • Since gnomAD uses GENCODE gene annotations for annotation the variants we need to change the default biotype_str from transcript_biotype to transcript_type (as written in the GTF file).

Note

As shown in the two examples above, when ENSEMBL data is used, the default options should work. However, for using other data sources such as variants from gnomAD, GTF from GENOCODE and others one or more of the following parameters need to be changed:

–af_field (from the VCF INFO field)

—annotation_field_name (from the VCF INFO field)

–transcript_index (from the annotation field in the VCF INFO field)

—consequence_index (from the annotation field in the VCF INFO field)

  • Translate human variants from a custom VCF that is obtained from sequencing of a sample:

    python pypgatk_cli.py vcf-to-proteindb
   --vcf sample.vcf
   --input_fasta transcripts.fa
   --gene_annotations_gtf genes.gtf
   --annotation_field_name ''
   --output_proteindb var_peptides.fa

Transcripts (DNA) to Protein Sequences

DNA sequences given in a fasta format can be translated using the dnaseq-to-proteindb tool. This tool allows for translation of all kinds of transcripts (coding and noncoding) by specifying the desired biotypes. The most suited --input_fasta file can be generated from a given GTF file using the gffread commad as follows:

$: gffread -F -w transcript_sequences.fa -g genome.fa gene_annotations_gtf

The fasta file that is generated from the GTF file would contain DNA sequences for all transcripts regardless of their biotypes. Also, it specifies the CDS positions for the protein coding transcripts. The dnaseq-to-proteindb command recognizes the features such as biotype and expression values in the fasta header that are taken from the GTF INFO filed (if available). However, it is not required to have those information in the fasta header but their presence enables the user to filter by biotype and expression values during the translation step.

Command Options

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$: python pypgatk.py dnaseq-to-proteindb -h
   Usage: pypgatk.py dnaseq-to-proteindb [OPTIONS]

   Required parameters:
     -c, --config_file TEXT      Configuration for VCF conversion parameters
     --input_fasta         Fasta sequences for the transripts in the GTF file used to annotated the VCF
     --output_proteindb          Output file to write the resulting variant protein sequences

   Optional parameters:
      --translation_table INTEGER    Translation Table (default 1)
      --num_orfs INTEGER             Number of ORFs (default 0)
      --num_orfs_complement INTEGER  Number of ORFs from the reverse side (default 0)
      --skip_including_all_cds       By default any transcript that has a defined CDS will be translated, this option disables this features instead it only depends on the biotypes
      --include_biotypes TEXT        Translate sequences with the spcified biotypes. Multiple biotypes can be given separated by comma. To translate all sequences in the input_fasta file set this option to ``all`` (default protein coding genes).
      --exclude_biotypes TEXT        Skip sequences with unwanted biotypes (affected by --include_biotypes) (default None).
      --biotype_str TEXT             String used to identify gene/transcript biotype in the fasta file (default transcript_biotype).
      --expression_str TEXT          String to be used for extracting expression value (TPM, FPKM, etc) (default None).
      --expression_thresh FLOAT      Threshold used to filter transcripts based on their expression values (default 5, affected by --expression_str)
      --var_prefix TEXT              Prefix to be added to fasta headers (default none)
      -h, --help                     Show this message and exit

Examples

  • Generate the canonical protein database, i.e. translate all protein_coding transcripts:

    python pypgatk.py dnaseq-to-proteindb
    --config_file config/ensembl_config.yaml
    --input_fasta testdata/transcript_sequences.fa
    --output_proteindb testdata/proteindb_from_CDSs_DNAseq.fa

  • Generate a protein database from lincRNA and canonical proteins:

    python pypgatk.py dnaseq-to-proteindb
    --config_file config/ensembl_config.yaml
    --input_fasta testdata/transcript_sequences.fa
    --output_proteindb testdata/proteindb_from_lincRNA_canonical_sequences.fa
    --var_prefix lincRNA_
    --include_biotypes lincRNA

  • Generate a protein database from processed pseudogene:

    python pypgatk.py dnaseq-to-proteindb
    --config_file config/ensembl_config.yaml
    --input_fasta testdata/transcript_sequences.fa
    --output_proteindb testdata/proteindb_from_processed_pseudogene.fa
    --var_prefix pseudogene_
    --include_biotypes processed_pseudogene,transcribed_processed_pseudogene,translated_processed_pseudogene
    --skip_including_all_cds

  • Generate alternative ORFs from canonical sequences:

    python pypgatk.py dnaseq-to-proteindb
    --config_file config/ensembl_config.yaml
    --input_fasta testdata/transcript_sequences.fa
    --output_proteindb testdata/proteindb_from_altORFs.fa
    --var_prefix altorf_
    --include_biotypes altORFs
    --skip_including_all_cds

  • Generate protein sequences (six-frame translation) from a Genome assembly:

    python pypgatk.py dnaseq-to-proteindb
    --config_file config/ensembl_config.yaml
    --input_fasta testdata/genome.fa
    --output_proteindb testdata/proteindb_genome.fa
    --biotype_str ''
    --num_orfs 3
    --num_orfs_complement 3

Generate Decoy Database

generate-decoy command enables generation of decoy databases for any given protein sequence database. Decoy databases are need to evaluate significance of spectra-sequence matching scores in proteomics mass spectrometry experiments.

DecoyPYrat is integrated into py-pgatk as the standard method for generating decoy sequences. In addition to reversing the target sequences, the tool replaces the cleavage with preceding amino acids. Also, it checks for the presence of the reversed sequence in the target sequences and if found, DecoyPYrat shuffles the sequences to avoid target-decoy sequence matches. For more information please read the DecoyPYrat manual available at: https://www.sanger.ac.uk/science/tools/decoypyrat.

Command Options

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$: python pypgatk.py dnaseq-to-proteindb -h
   Usage: pypgatk.py dnaseq-to-proteindb [OPTIONS]

   Required parameters:
     -c, --config_file TEXT          Configuration file for the protein database decoy generation
     -o, --output TEXT               Output file for decoy database
     -i, --input TEXT                FASTA file of target protein sequences for
                                     which to create decoys (*.fasta|*.fa)
   Optional parameters:
     -s, --cleavage_sites TEXT       A list of amino acids at which to cleave
                                     during digestion. Default = KR
     -a, --anti_cleavage_sites TEXT  A list of amino acids at which not to cleave
                                     if following cleavage site ie. Proline.
                                     Default = none
     -p, --cleavage_position TEXT    Set cleavage to be c or n terminal of
                                     specified cleavage sites. Options [c, n],
                                     Default = c
     -l, --min_peptide_length INTEGER
                                     Set minimum length of peptides to compare
                                     between target and decoy. Default = 5
     -n, --max_iterations INTEGER    Set maximum number of times to shuffle a
                                     peptide to make it non-target before
                                     failing. Default=100
     -x, --do_not_shuffle TEXT       Turn OFF shuffling of decoy peptides that
                                     are in the target database. Default=false
     -w, --do_not_switch TEXT        Turn OFF switching of cleavage site with
                                     preceding amino acid. Default=false
     -d, --decoy_prefix TEXT         Set accession prefix for decoy proteins in
                                     output. Default=DECOY_
     -t, --temp_file TEXT            Set temporary file to write decoys prior to
                                     shuffling. Default=protein-decoy.fa
     -b, --no_isobaric TEXT          Do not make decoy peptides isobaric.
                                     Default=false
     -m, --memory_save TEXT          Slower but uses less memory (does not store
                                     decoy peptide list). Default=false
     -h, --help                      Show this message and exit.

Examples

  • Generate decoy sequences for proteindb_from_lincRNA_canonical_sequences.fa that was generate using dnaseq-to-proteindb:

    python pypgatk_cli.py generate-decoy -c config/protein_decoy.yaml --input proteindb_from_lincRNA_canonical_sequences.fa --output decoy_proteindb.fa

Contributions