PoGo

PoGo – Fast Mapping of Peptides to Genomic Coordinates for Proteogenomic Analyses

In proteogenomic analyses it is essential to know the loci giving rise to peptides in order to improve genomic annotation and the functional characterization of protein products in their biological context. With next-generation sequencing of DNA and RNA for each sample studied by proteomic mass spectrometry integration and visualisation in a common coordinate system, i.e. the genome, is vital for systems biology. Advances in technology in mass spectrometry now allow almost complete quantification of the sample proteome. With research moving to protein quantitative trait loci (pQTL) to identify genomic alterations with functional effects on the proteome and the high complexity of combinations thereof integration and visualisation of protein and peptide quantification on genomic loci is paramount for this type of analysis. Furthermore, moving towards more personal multi-omics studies comparative visualisation of proteomic data on a genome has been lacking. Not only genomic variation affecting proteins have come into focus of functional integration studies but also post-translational modifications (PTM), the effect of single nucleotide variants and other alterations on PTMs and alternative modification loci, and the effects of alternative PTMs on protein abundance have become more a centre of attention for researchers. To facilitate this type of integration not only the genomic locations of modified peptides but specifically the genomic loci of associated with these modifications is required. Here, we provide a mapping tool, PoGo, to quickly and efficiently identify genomic loci of peptides and post-translational modifications and couple these mappings with associated quantitative values over multiple samples. Using reference gene annotation and an associated transcript translations our tool identifies the genomic loci of peptides given as input and generates output in different formats borrowed from genomics and transcriptomics which can be loaded in various genome browsers such as UCSC Genome Browser, Ensembl Genome Browser, BioDalliance, and the Integrative Genomics Viewer.

Further information

PoGo uses transcript translations and reference gene annotations to identify the genomic loci of peptides and post-translational modifications. Multiple occurrences of peptides in the input data resulting in the same genomic loci will be collapsed as a single occurrence in the output.

Input format

The input format required by PoGo is a tab delimited file with four columns. Allowed file extensions for this format are *.pogo, *.txt, and *.tsv.

Column Column header Description
1 Sample Name of sample or experiment
2 Peptide Peptide sequence with PSI-MS nodification names in round brackets following the mpdified amino acid, e.g. PEPT(Phopsho)IDE for a phosphorylated threonine
3 PSMs Number of peptide-spectrum matches (PSMs) for the given peptide
4 Quant Quantitative value for the given peptide in the given sample

Output formats

BED

This format contains the genomic loci for peptides, the exon-structure, the peptide sequence, as well as a colour code for uniqueness of peptides within the genome.

Colour Description
Peptide is unique to single gene AND single transcript
Peptide is unique to single gene BUT shared between multiple transcripts
Peptide is shared between multiple genes

PTMBED

Two files will be generates. Similar to BED the first file contains the location of the post-translational modification on the genome. Thick parts of the peptide blocks indicate the position of the post-translational modification on a single amino acid (short thick block) while longer blocks indicate the occurrence of the first and last post-translational modification and residues in between. In the PTMBED the colour code is changed to indicate the type of modification. The second file contains peptides from the input file that were not found without any post-translational modifications.

Colour Post-translational Modification
Phosphorylation (phospho)
Acetylation (acetyl)
Amidation (amidated)
Oxidation (oxidation)
Methylation (methyl)
Ubiquitinylation (glygly; gg)
Sulfation (sulfo)
Palmitoylation (palmitoyl)
Formylation (formyl)
Deamidation (deamidated)
Any other post-translational modification

GTF

This output format contains besides the genomic loci the annotated information for the genes giving rise to each peptide sequence including status and biotype. For each mapped peptide the sample, number of peptide-spectrum matches and associated quantitative value as tags.

GCT

In this format the peptide sequences are combines with the Ensembl gene identifier. It contains the genomic loci for each peptide as well as the quantitative values for each peptide in different samples as a matrix.

Usage for command line

PoGo is a readily compiled command line tool written in C++, basic usage to generate map peptides to corresponding genomic loci with default parameters:

Windows:	PoGo.exe -fasta annotation.translation.fasta -gtf annotation.gtf -in example.pogo
Linux/Unix/Mac:	PoGo -fasta annotation.translation.fasta -gtf annotation.gtf -in example.pogo

Full usage:

PoGo/PoGo.exe -fasta TRANSL -gtf ANNO -in *.pogo[,*.pogo] [-format OUTF] [-merge TRUE/FALSE] [-source SRC] [-mm NUM] [-mmmode TRUE/FALSE] [-species SPECIES]

Required arguments:

-fasta TRANSL
Filepath for file containing protein sequences in FASTA format
-gtf ANNO
Gene annotation with coding sequences (CDS) in GTF format
-in *.pogo
Path to single input file or comma separated list of paths to input files containing peptides to be mapped with associated number of peptide to spectrum matches, sample name and quantitative value (see input file format)

Optional arguments:

-format OUTF
Set output format GTF, GCT, BED, PTMBED or ALL. Comma separated combination possible. Default = ALL
-merge TRUE/FALSE
Set TRUE to merge output of multiple input files (output will be named after last input file *_merged). Default = FALSE
-source SRC
Set the source name for the peptides. This is only used in the GTF output. Default = PoGo
-mm NUM
Number of mismatches allowed in mapping (0, 1 or 2). DEFAULT = 0
-mmmode TRUE/FALSE
Set TRUE to restrict number of mismatch in kmer to 1. DEFAULT = FALSE
-species SPECIES
Set species using common or scientific name or taxonomy ID. Default is Human (Homo sapiens, 9606).

Table of supported species:

Common name Scientific name Taxon ID
C.intestinalis Ciona intestinalis 7719
Cat Felis catus 9685
Chicken Gallus gallus 9031
Chimpanzee Pan troglodytes 9598
Cow Bos taurus 9913
Dog Canis lupus familiaris 9615
Gorilla Gorilla gorilla gorilla 9595
Horse Equus caballus 9796
Human Homo sapiens 9606
Macaque Macaca mulatta 9544
Marmoset Callithrix jacchus 9483
Medaka Oryzias latipes 8090
Mouse Mus musculus 10090
Olive baboon Papio anubis 9555
Opossum Monodelphis domestica 13616
Orangutan Pongo abelii 9601
Pig Sus scrofa 9823
Platypus Ornithorhynchus anatinus 9258
Rabbit Oryctolagus cuniculus 9986
Rat Rattus norvegicus 10116
Sheep Ovis aries 9940
Tetraodon Tetraodon nigroviridis 99883
Turkey Meleagris gallopavo 9103
Vervet-AGM Chlorocebus sabaeus 60711
Zebra Finch Taeniopygia guttata 59729

Step by step

  1. Download annotation and translated sequences for human from GENCODE, e.g. release 25. Go to www.gencodegenes.org/release/25.html and download the GTF file containing ‘Comprehensive gene annotation’ and the ‘Protein-coding transcript translation sequences’ as Fasta file. Store and unzip both files into a folder, e.g. ${POGO_DIR}/input/
  2. Navigate to the folder that contains the PoGo executable (cd ${POGO})
  3. Execute the following command to generate gtf, gct, bed and ptmbed output for of the your input file referred to as ${Peptides.txt}
    Linux/Unix: ./PoGo –fasta ./input/gencode.v25.pc_translations.fa –gtf ./input/gencode.v25.annotation.gtf –in /PATH/TO/${Peptides.txt}
    Windows: .\PoGo.exe –fasta .\input\gencode.v25.pc_translations.fa –gtf .\input\gencode.v25.annotation.gtf –in \PATH\TO\${Peptides.txt}
  4. You can load the generated BED and/or GTF files into a genome browser or create an web accessible track hub for your data through TrackHub Generator. Here the example is shown for visualisation in the UCSC genome browser.
    To load the data into the browser please follow these steps:

    1. Go to https://genome.ucsc.edu and navigate to ‘My Data’ -> ‘Custom Tracks’.
    2. After clicking ‘Choose File’ select the file you want to upload and submit via the ‘Submit’ button.
    3. You will be redirected to the ‘Manage Custom Tracks’ webpage.
    4. Proceed from the ‘Manage Custom Tracks’ page by selecting ‘Genome Browser’ and confirm (‘go’).
    5. Now you can browse the peptides mapped to their genomic loci on the reference genome.

Runtime and Memory Estimations

Runtime and required memory (RAM) for PoGo execution across different settings for inclusion of mismatches and depending on number of peptides in the input file.

Mismatch Parameter Setting ~1,500 peptides in Input ~250,000 peptides in Input
-mm 0
< 5 min / < 4 GB <5 min / min 10 GB
-mm 1
~5 min / < 4 GB ~5 min / min 16 GB
-mm 2 -mmode true
< 15 min / min 6 GB < 20 min / min 32 GB
-mm 2 -mmode false
~ 1.5 h / min 64 GB ~ 2 h / min 160 GB

PoGo GUI

PoGoGUI uses transcript translations and reference gene annotations as well as peptide identification files in four column tab separated format, mzid, mzIdentML, and mzTab formats to pass on to the specified PoGo executable. Additional parameters to select outputformats, enable mapping with amino acid substitutions or merging output of multiple peptide identification files can be selected through checkboxes and buttons. Additional information about parameters, PoGo input and output formats can be found at the top of the ‘Learn and Support’ section.

Starting GUI

PoGoGUI has to be started through the command line. Please navitage to the folder containing the PoGoGUI.jar file via cd PATH/TO/PoGoGUI/ and type in the following command:

java -jar PoGoGUI.jar

The user interface will start. Mandatory parameters, i.e. all required files, can be chosen through the Select and Add buttons on the right half of the interface while optional parameters can be selected on the left side.

Step by Step

  1. Download annotation and translated sequences for human from GENCODE, e.g. release 25. Go to www.gencodegenes.org/release/25.html and download the GTF file containing ‘Comprehensive gene annotation’ and the ‘Protein-coding transcript translation sequences’ as Fasta file. Store and unzip both files into a folder, e.g. ${POGO_DIR}/input/
  2. Navigate to the folder that contains the PoGoGUI JAR (cd ${POGOGUI}) and execute the JAR using
    java -jar ${POGOGUI}/PoGoGUI.jar
  3. In the user interface select the PoGo executable via the selection button.
  4. Select the translated protein coding sequence file (FASTA) and the transcript annotation file (GTF) through the resprective Select buttons.
  5. Add the input files with peptides for mapping in tsv, txt, mzid, mzIdentML, mzTab formats either through the Add button or through drag and drop into the list field. The GUI will create intermediate files in PoGo input format from mzid, mzIdentML and mzTab files if required.
  6. Through clicking the Start button the FileConverter, if required, is started followed by PoGo.
  7. You can load the generated BED and/or GTF files into a genome browser or create an web accessible track hub for your data through TrackHub Generator. Here the example is shown for visualisation in the UCSC genome browser.
    To load the data into the browser please follow these steps:

    1. Go to https://genome.ucsc.edu and navigate to ‘My Data’ -> ‘Custom Tracks’.
    2. After clicking ‘Choose File’ select the file you want to upload and submit via the ‘Submit’ button.
    3. You will be redirected to the ‘Manage Custom Tracks’ webpage.
    4. Proceed from the ‘Manage Custom Tracks’ page by selecting ‘Genome Browser’ and confirm (‘go’).
    5. Now you can browse the peptides mapped to their genomic loci on the reference genome.

Test Examples

Test examples, requirement specifications and time estimations are available here.

Contact

If you need help or have any queries, please contact us using the details below.

Christoph Schlaffner (christoph.schlaffner@sanger.ac.uk)


Sanger Institute Contributors

Previous contributors

Photo of Jyoti Choudhary

Jyoti Choudhary

Former Head of Mass Spectrometry