In this tutorial, we perform proteogenomic database searching using the Mass Spectrometry data. The inputs for performing the proteogenomic database searching are the peaklist MGF files and the FASTA database file. The FASTA database is obtained by running the first workflow “Uniprot_cRAP_SAV_indel_translatedbed.FASTA”. The second workflow focuses on performing database search of the peak list files (MGFs).

Agenda

In this tutorial, we will deal with:

1. Pretreatments
2. Match peptide sequences
   1. SearchGUI
   2. PeptideShaker
3. Create a SQLite database for peptide, protein and genomic annotation visualization
   1. Query Tabular
4. Getting data Blast-P ready
5. Conclusion

Pretreatments

Hands-on: data upload and organization

1. Create a new history and name it something meaningful (e.g. Proteogenomics DB search)
2. Import the four MGF MS/MS files and the Trimmed_ref_5000_uniprot_cRAP.FASTA sequence file from Zenodo.

   https://zenodo.org/record/1489208/files/Mo_Tai_Trimmed_mgfs__Mo_Tai_iTRAQ_f4.mgf
   https://zenodo.org/record/1489208/files/Mo_Tai_Trimmed_mgfs__Mo_Tai_iTRAQ_f5.mgf
   https://zenodo.org/record/1489208/files/Mo_Tai_Trimmed_mgfs__Mo_Tai_iTRAQ_f8.mgf
   https://zenodo.org/record/1489208/files/Mo_Tai_Trimmed_mgfs__Mo_Tai_iTRAQ_f9.mgf
   https://zenodo.org/records/13270741/files/Uniprot_cRAP_SAV_indel_translatedbed.FASTA
   https://zenodo.org/records/13270741/files/Reference_Protein_Accessions.tabular
   
   

   Tip: Importing via links

   • Copy the link location

   • Click galaxy-upload Upload Data at the top of the tool panel

   • Select galaxy-wf-edit Paste/Fetch Data

   • Paste the link(s) into the text field

   • Press Start

   • Close the window

3. Rename the datasets to something more recognizable (strip the URL prefix)

4. Build a Dataset list for the three MGF files, name it as “Mo_Tai_MGFs”

   Tip: Creating a dataset collection

   • Click on galaxy-selector Select Items at the top of the history panel
   • Check all the datasets in your history you would like to include

   • Click n of N selected and choose Build Dataset List

     

   • Enter a name for your collection
   • Click Create collection to build your collection
   • Click on the checkmark icon at the top of your history again

Match peptide sequences

The search database labeled Uniprot_cRAP_SAV_indel_translatedbed.FASTA is the input database that will be used to match MS/MS to peptide sequences via a sequence database search.

For this, the sequence database-searching program called SearchGUI will be used.The generated dataset collection of the three MGF files in the history is used as the MS/MS input. We will walk through a number of these settings in order to utilize SearchGUI on these example MGF files.

Comment: Tool Versions

The tools are subjected to changes while being upgraded. Here we have used an older version of SearchGUI (v3.3.10.1)-Peptide Shaker (v1.16.36.3). The new versions are available in Galaxy.

SearchGUI

Hands-on: SearchGUI

1. Search GUI ( Galaxy version 3.3.10.1) with the following parameters:

   • param-file “Protein Database”: Uniprot_cRAP_SAV_indel_translatedbed.FASTA (Or however you named the FASTA file)

     Comment

     The “Uniprot_cRAP_SAV_indel_translatedbed” FASTA database is obtained when you run the first proteogenomics workflow. Please make sure to run the 1st workflow.

   • param-files “Input Peak lists (mgf)”: MGF files dataset collection.

     Tip: Select dataset collections as input

     • Click the Dataset collection icon on the left of the input field:

     • Select the appropriate dataset collection from the list

   • Section Search Engine Options:

     • param-check “DB-Search Engines”: X!Tandem

       Comment

       The section Search Engine Options contains a selection of sequence database searching programs that are available in SearchGUI. Any combination of these programs can be used for generating PSMs from MS/MS data. For the purpose of this tutorial, X!Tandem we will be used.

   • Section Protein Digestion Options:
     • param-select “Enzyme”: Trypsin
     • param-text “Maximum Missed Cleavages”: 2
   • Section Precursor options:
     • param-select “Precursor Ion Tolerance Units”: Parts per million (ppm)
     • param-text “Precursor Ion Tolerance”: 10
     • param-text “Fragment Tolerance (Daltons)”: 0.05 (this is high resolution MS/MS data)
     • param-text “Minimum charge”:2
     • param-text “Maximum charge”:6
     • param-select “Forward Ion”: b
     • param-select “Reverse Ion”: y
     • param-text “Minimum Precursor Isotope” :0
     • param-text “Maximum Precursor Isotope” :1
   • Section Protein Modification Options:
     • param-select “Fixed Modifications”: Carbamidomethylation of C, ITRAQ-4Plex of K, ITRAQ-4Plex of Ntermini

     • param-select “Variable modifications”: Oxidation of M, ITRAQ-4Plex of Y

       Tip: Search for options

       For selection lists, typing the first few letters in the window will filter the available options.

   • Section Advanced Options:
     • param-select “X!Tandem Options”: Advanced
       • param-check “X!Tandem: Quick Acetyl”: No
       • param-check “X!Tandem: Quick Pyrolidone”: No
       • param-check “X!Tandem: Protein stP Bias”: No
       • param-text “X!Tandem: Maximum Valid Expectation Value”: 100

Once the database search is completed, the SearchGUI tool will output a file (called a SearchGUI archive file) that will serve as an input for the next section, PeptideShaker. Rename the output as “Compressed SearchGUI results”

Comment

Note that sequence databases used for proteogenomics are usually much larger than the excerpt used in this tutorial. When using large databases, the peptide identification step can take much more time for computation. In proteogenomics, choosing the optimal database is a crucial step of your workflow.

PeptideShaker

PeptideShaker is a post-processing software tool that processes data from the SearchGUI software tool. It serves to organize the Peptide-Spectral Matches (PSMs) generated from SearchGUI processing and is contained in the SearchGUI archive. It provides an assessment of confidence of the data, inferring proteins identified from the matched peptide sequences and generates outputs that can be visualized by users to interpret results. PeptideShaker has been wrapped in Galaxy to work in combination with SearchGUI outputs.

Comment: File Formats

There are a number of choices for different data files that can be generated using PeptideShaker. A compressed file can be made containing all information needed to view them results in the standalone PeptideShaker viewer. A mzidentML file can be generated that contains all peptide sequence matching information and can be utilized by compatible downstream software. Other outputs are focused on the inferred proteins identified from the PSMs, as well as phosphorylation reports, relevant if a phosphoproteomics experiment has been undertaken.

Hands-on: PeptideShaker

1. Peptide Shaker ( Galaxy version 1.16.36.3) with the following parameters:
   • param-file “Compressed SearchGUI results”: The SearchGUI archive file
   • param-select “Specify Advanced PeptideShaker Processing Options”: Default Processing Options
   • param-select “Specify Advanced Filtering Options”: Default Filtering Options
   • param-check “Include the protein sequences in mzIdentML”: No
   • param-check “Output options”: Select the PSM Report (Peptide-Spectral Match) and the Certificate of Analysis

   Comment: Certificate of Analysis

   The “Certificate of Analysis” provides details on all the parameters used by both SearchGUI and PeptideShaker in the analysis. This can be downloaded from the Galaxy instance to your local computer in a text file if desired.

2. Inspect galaxy-eye the resulting files

A number of new items will appear in your History, each corresponding to the outputs selected in the PeptideShaker parameters. The Peptide Shaker’s PSM report is used as an input for the BlastP analysis. Before performing BlastP analysis. The Query Tabular tool and few test manipulation tools are used to remove spectra that belongs to the reference proteins. The output tabular file “Peptides_for_Blast-P_analysis” will contain only those spectra that did not belong to any known proteins.

Create a SQLite database for peptide, protein and genomic annotation visualization

The mzidentml output from the Peptide shaker is converted into an sqlite database file by using the mz to sqlite tool. This sqlite output is used to open the Multi-omics visualization platform, wherein you can view the spectra of the peptides using Lorikeet parameters. To open the MVP viewer, click on the “Visualize in MVP Application” icon ( this will pop-open the interactive multi-omics viewer in a new window/tab)

Hands-on: mz to sqlite

This tool extracts mzidentml and its associated proteomics datasets into a sqlite db

1. mz to sqlite ( Galaxy version 2.0.4+galaxy1) with the following parameters:
   • param-file “Proteomics identification files”: PeptideShaker_mzidentml
   • param-file “Proteomics Spectrum files”: Mo_Tai_MGFs
   • param-file “Proteomics Search Database Fasta”: Uniprot_cRAP_SAV_indel_translatedbed.FASTA

   

The next step is to remove known peptides from the list of PSMs that we acquired from the Peptide Shaker results. For that, we need to perform Query tabular to extract the list of known peptides from the UniProt and cRAP database.

Query Tabular

Hands-on: Remove Reference proteins

1. Query Tabular tool with the following parameters:

• param-repeat Insert Database Table (b): PSM report
  • Section Filter Dataset Input:
    • param-repeat Insert Filter Tabular Input Lines
      • “Filter by”: skip leading lines
      • “Skip lines”: 1
  • Section Table Options:
    • “Specify Name for Table”: psms
    • “Use first line as column names” : No
    • “Specify Column Names (comma-separated list)”:id,Proteins,Sequence
    • “Only load the columns you have named into database”: Yes
    • param-repeat Table Index
      • “Table Index”: No
      • “Index on Columns”: id

• param-repeat Insert Database Table (c):PSM report

  • Section Filter Dataset Input
    • param-repeat Insert Filter Tabular Input Lines
      • “Filter by”: skip leading lines
      • “Skip lines”: 1
    • param-repeat Insert Filter Tabular Input Lines
      • “Filter by”: select columns
      • “Enter column numbers to keep”: 1,2
    • param-repeat Insert Filter Tabular Input Lines
      • “Filter by”: normalize list columns,replicate rows for each item in the list
      • “Enter column numbers to normalize”: 2
      • “List item delimiter in column”: ,
  • Section Table Options:
    • “Specify Name for Table”: prots
    • “Use first line as column names” : No
    • “Specify Column Names (comma-separated list)”:id,prot
    • “Only load the columns you have named into database”: Yes
    • param-repeat Insert Table Index:
      • “Table Index”: No
      • “Index on Columns”: prot,id
• param-repeat Insert Database Table (a): Reference_Protein_Acessions
  • Section Table Options:
    • “Tabular Dataset for Table”: Uniprot
    • “Use first line as column names” : No
    • “Specify Column Names (comma-separated list)”:prot
    • param-repeat Insert Table Index:
      • “Table Index”: No
      • “Index on Columns”: prot

    Comment

    By default, table columns will be named: c1,c2,c3,…,cn (column names for a table must be unique). You can override the default names by entering a comma separated list of names, e.g. ,name1,,,name2 would rename the second and fifth columns. Check your input file to find the settings which best fits your needs.

  • “Save the sqlite database in your history”: No

    Comment: Querying an SQLite Database

    Query Tabular can also use an existing SQLite database. Activating Save the sqlite database in your history will store the generated database in the history, allowing to reuse it directly.

  • “SQL Query to generate tabular output”:

    SELECT psms.*
    FROM psms
    WHERE psms.id NOT IN
    (SELECT distinct prots.id
    FROM prots JOIN uniprot ON prots.prot = uniprot.prot)
    ORDER BY psms.id
    

  • “include query result column headers”: Yes
• Click Run Tool and inspect the query results file after it turns green.

The output from this step is that the resultant peptides would be those which doesn’t belong in the Uniprot or cRAP database. The query tabular tool is used again to create a tabular output containing peptides ready for Blast P analysis.

Hands-on: Query Tabular

Query Tabular tool: with the following parameters:

• Section Filter Dataset Input
  • param-repeat “Insert Filter Tabular Input Lines”
    • “Filter by”: skip leading lines
    • “Skip lines”: 1
• Section Table Options:
  • “Specify Name for Table”: psm
  • “Use first line as column names” : No
  • “Specify Column Names (comma-separated list)”:id,Proteins,Sequence
  • “Only load the columns you have named into database”: Yes
• “SQL Query to generate tabular output”:

  SELECT Sequence || ' PSM=' || group_concat(id,',') || ' length='
  || length(Sequence) as "ID",Sequence
  FROM  psm
  WHERE length(Sequence) >6
  AND length(Sequence) <= 30
  GROUP BY Sequence
  ORDER BY length(Sequence),Sequence
  

• “include query result column headers”: Yes

• Click Run Tool and inspect the query results file after it turns green.

Getting data Blast-P ready

BlastP search is carried out with the PSM report (output from PeptideShaker). Before, BlastP analysis the “Peptides_for_Blast-P_analysis” is first converted from Tabular format to FASTA file format which can be easily read by the BlastP algorithm. This is done with the help of “Tabular to FASTA” conversion tool. The short BlastP uses parameters for short peptide sequences (8-30 aas). Please use the rerun option to look at the parameters used.

Hands-on: Tabular to FASTA (version 1.1.1)

1. Tabular-to-FASTA tool: with the following parameters:
   • “Title column”: 1
   • “Sequence Column”:2

The output FASTA file is going to be subjected to BLAST-P analysis.

Comment: Tool Versions

The tools are subjected to changes while being upgraded. Thus, running the workflow provided with the tutorial, the user might need to make sure they are using the latest version including updating the parameters.

Conclusion

This completes the walkthrough of the proteogenomics database search workflow. This tutorial is a guide to performing database searching with mass spectrometry files and having peptides ready for Blast-P analysis, you can perform follow-up analysis using the next GTN “Proteogenomics Novel Peptide Analysis”. Researchers can use this workflow with their data also, please note that the tool parameters, reference genomes, and the workflow will need to be modified accordingly.

This workflow was developed by the Galaxy-P team at the University of Minnesota. For more information about Galaxy-P or our ongoing work, please visit us at galaxyp.org

You've Finished the Tutorial

Please also consider filling out the Feedback Form as well!

Key points

• With SearchGUI and PeptideShaker you can gain access to multiple search engines

Frequently Asked Questions

Useful literature

Further information, including links to documentation and original publications, regarding the tools, analysis techniques and the interpretation of results described in this tutorial can be found here.

Feedback

Did you use this material as an instructor? Feel free to give us feedback on how it went.
Did you use this material as a learner or student? Click the form below to leave feedback.

Citing this Tutorial

1. Subina Mehta, Timothy J. Griffin, Pratik Jagtap, Ray Sajulga, James Johnson, Praveen Kumar, Proteogenomics 2: Database Search (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/proteomics/tutorials/proteogenomics-dbsearch/tutorial.html Online; accessed TODAY
2. Hiltemann, Saskia, Rasche, Helena et al., 2023 Galaxy Training: A Powerful Framework for Teaching! PLOS Computational Biology 10.1371/journal.pcbi.1010752
3. Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012

BibTeX

@misc{proteomics-proteogenomics-dbsearch,
author = "Subina Mehta and Timothy J. Griffin and Pratik Jagtap and Ray Sajulga and James Johnson and Praveen Kumar",
	title = "Proteogenomics 2: Database Search (Galaxy Training Materials)",
	year = "",
	month = "",
	day = ""
	url = "\url{https://training.galaxyproject.org/training-material/topics/proteomics/tutorials/proteogenomics-dbsearch/tutorial.html}",
	note = "[Online; accessed TODAY]"
}
@article{Hiltemann_2023,
	doi = {10.1371/journal.pcbi.1010752},
	url = {https://doi.org/10.1371%2Fjournal.pcbi.1010752},
	year = 2023,
	month = {jan},
	publisher = {Public Library of Science ({PLoS})},
	volume = {19},
	number = {1},
	pages = {e1010752},
	author = {Saskia Hiltemann and Helena Rasche and Simon Gladman and Hans-Rudolf Hotz and Delphine Larivi{\`{e}}re and Daniel Blankenberg and Pratik D. Jagtap and Thomas Wollmann and Anthony Bretaudeau and Nadia Gou{\'{e}} and Timothy J. Griffin and Coline Royaux and Yvan Le Bras and Subina Mehta and Anna Syme and Frederik Coppens and Bert Droesbeke and Nicola Soranzo and Wendi Bacon and Fotis Psomopoulos and Crist{\'{o}}bal Gallardo-Alba and John Davis and Melanie Christine Föll and Matthias Fahrner and Maria A. Doyle and Beatriz Serrano-Solano and Anne Claire Fouilloux and Peter van Heusden and Wolfgang Maier and Dave Clements and Florian Heyl and Björn Grüning and B{\'{e}}r{\'{e}}nice Batut and},
	editor = {Francis Ouellette},
	title = {Galaxy Training: A powerful framework for teaching!},
	journal = {PLoS Comput Biol} Computational Biology}
}

                   

Go Further

Do you want to extend your knowledge? Follow one of our recommended follow-up trainings:

• tutorial Hands-on: Proteogenomics 3: Novel peptide analysis

Galaxy Administrators: Install the missing tools

You can use Ephemeris's shed-tools install command to install the tools used in this tutorial.

shed-tools install [-g GALAXY] [-a API_KEY] -t <(curl https://training.galaxyproject.org/training-material/api/topics/proteomics/tutorials/proteogenomics-dbsearch/tutorial.json | jq .admin_install_yaml -r)

Alternatively you can copy and paste the following YAML

---
install_tool_dependencies: true
install_repository_dependencies: true
install_resolver_dependencies: true
tools:
- name: tabular_to_fasta
  owner: devteam
  revisions: 0a7799698fe5
  tool_panel_section_label: FASTA/FASTQ
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: mz_to_sqlite
  owner: galaxyp
  revisions: f5c632894818
  tool_panel_section_label: Convert Formats
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: peptideshaker
  owner: galaxyp
  revisions: 7fdd9119cc4f
  tool_panel_section_label: Proteomics
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: peptideshaker
  owner: galaxyp
  revisions: 7fdd9119cc4f
  tool_panel_section_label: Proteomics
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: query_tabular
  owner: iuc
  revisions: 33d61c89fb8d
  tool_panel_section_label: Text Manipulation
  tool_shed_url: https://toolshed.g2.bx.psu.edu/

Feedback

5 stars	4
4 stars	1
3 stars	1

October 2023

• 3 stars: Disliked: I went through the first tutorial, Proteogenomics 1: Database Creation. This resulted in creation of the fasta protein database file, Uniprot_cRAP_SAV_indel_translatedbed.FASTA which is used by this tutorial. When I ran the SearchGUI against this database file and the same downloaded mfg files, an empty SearchGUI archive file was generated. So I could not move onto the next steps of the tutorial. I suppose it is possible that I somehow did something wrong in the first tutorial to create the fasta file although just by looking at it it seemed reasonable and had a fair amount of data in it. I was wondering if anyone has gone through the first tutorial and the second tutorial to verify that they work. Also, it would be helpful if there was a sample input protein database file available on zenodo to complete this tutorial without having to be dependent upon the output of the first tutorial.

March 2022

• 5 stars: Liked: very useful pipeline
• 5 stars: Liked: very complex biological/ bioinfo problem solved thanks to this great workflow. available and accessible to the whole of the scientific community. Great! Thanks for all contributors and participants. Disliked: n/a

February 2021

• 5 stars: Liked: The presentation was great: first the explanantion what we will do and why, and then tutorial.
• 5 stars: Liked: clear and concise descriptions of each step Disliked: provide all necessary input files from tutorial 1 on zenodo