# Metaproteomics tutorial

### Overview

question Questions
• How can I match metaproteomic mass spectrometry data to peptide sequences derived from shotgun metagenomic data?

• How can I perform taxonomy analysis and visualize metaproteomics data?

• How can I perform functional analysis on this metaproteomics data?

objectives Objectives
• A taxonomy and functional analysis of metaproteomic mass spectrometry data.

requirements Requirements

time Time estimation: 2 hours

Supporting Materials

last_modification Last modification: Mar 12, 2021

# Introduction

In this metaproteomics tutorial we will identify expressed proteins from a complex bacterial community sample. For this MS/MS data will be matched to peptide sequences provided through a FASTA file.

Metaproteomics is the large-scale characterization of the entire protein complement of environmental microbiota at a given point in time. It has the potential to unravel the mechanistic details of microbial interactions with the host / environment by analyzing the functional dynamics of the microbiome.

In this tutorial, we will analyze a sample of sea water that was collected in August of 2013 from the Bering Strait chlorophyll maximum layer (7m depth, 65° 43.44″ N, 168° 57.42″ W). The data were originally published in May et al., 2016.

### Agenda

In this tutorial, we will deal with:

1. Pretreatments
2. Analysis

# Pretreatments

• Import from the data library if your instance provides the files

In this tutorial, we will get the data from Zenodo: .

### hands_on Hands-on: Data upload and organization

1. Create a new history and name it something meaningful (e.g. Metaproteomics tutorial)

### Tip: Creating a new history

Click the new-history icon at the top of the history panelIf the new-history is missing:

1. Click on the galaxy-gear icon (History options) on the top of the history panel
2. Select the option Create New from the menu

### Tip: Renaming a history

1. Click on Unnamed history (or the current name of the history) (Click to rename history) at the top of your history panel
2. Type the new name
3. Press Enter
2. Import the three MGF MS/MS files and the FASTA sequence file from Zenodo.

• Open the Galaxy Upload Manager (galaxy-upload on the top-right of the tool panel)
• Select Paste/Fetch Data
• Paste the link into the text field
• Press Start
• Close the windowBy default, Galaxy uses the URL as the name, so rename the files with a more useful name.

As default, Galaxy takes the link as name.

• Rename the datasets to a more descriptive name
3. Build a Dataset list for the three MGF files

### Tip: Creating a dataset collection

• Click on Operations on multiple datasets (check box icon) at the top of the history panel
• Check all the datasets in your history you would like to include
• Click For all selected.. and choose Build dataset list
• Enter a name for your collection
• Click Create List to build your collection
• Click on the checkmark icon at the top of your history again

# Analysis

## Match peptide sequences

The search database labelled FASTA_Bering_Strait_Trimmed_metapeptides_cRAP.FASTA is the input database that will be used to match MS/MS to peptide sequences via a sequence database search. It is a small excerpt of the original database, which was constructed based on a metagenomic screening of the sea water samples (see May et al. (2016)). The full original database can be accessed from here. The contaminant database (cRAP) was merged with the original database.

For this, the sequence database-searching program called SearchGUI will be used. The created dataset collection of the three MGF files in the history is used as the MS/MS input.

### hands_on Hands-on: SearchGUI

1. SearchGUI tool: Run SearchGUI with:
• Protein Database: FASTA_Bering_Strait_Trimmed_metapeptides_cRAP.FASTA(or however you named the FASTA file)
• Input Peak lists (mgf): MGF files dataset collection.

### tip 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:

• Search Engines: X!Tandem

### comment Comment

The section Search Engine Options contains a selection of sequence database searching algorithms 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 Precursor Options:

• Fragment Tolerance Units: Daltons
• Fragment Tolerance: 0.2- this is high resolution MS/MS data
• Maximum Charge: 6

Section Protein Modification Options:

• Fixed Modifications: Carbamidomethylation of C
• Variable modifications: Oxidation of M

### tip Tip: Search for options

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

• X!Tandem Options: Advanced
• X!Tandem: Quick Acetyl: No
• X!Tandem: Quick Pyrolidone: No
• X!Tandem: Protein stP Bias: No
• X!Tandem: Maximum Valid Expectation Value: 100

• leave everything else as default
2. Click Execute.

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.

### comment Comment

Note that sequence databases used for metaproteomics 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 metaproteomics, choosing the optimal database is a crucial step of your workflow, for further reading see Timmins-Schiffman et al (2017).

To learn more about database construction in general, like integrating contaminant databases or using a decoy strategy for FDR searching, please consult our tutorial on Database Handling.

#### 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 Comment

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 the results in the standalone PeptideShaker viewer. A mzidentML file can be created 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. More detailed information on peptide inference using SearchGUI and PeptideShaker can be found in our tutorial on Peptide and Protein ID.

### hands_on Hands-on: PeptideShaker

1. PeptideShaker tool: Run PeptideShaker with:
• Compressed SearchGUI results: The SearchGUI archive file
• Specify Advanced PeptideShaker Processing Options: Default Processing Options
• Specify Advanced Filtering Options: Advanced Filtering Options
• Maximum Precursor Error Type: Daltons
• Specify Contact Information for mzIdendML: You can leave the default dummy options for now, but feel free to enter custom contact information.
• Include the protein sequences in mzIdentML: No
• Output options: Select the PSM Report (Peptide-Spectral Match) and the Certificate of Analysis

### comment Comment

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. Click Execute and inspect the resulting files after they turned green with the View data icon:

A number of new items will appear in your history, each corresponding to the outputs selected in the PeptideShaker parameters. Most relevant for this tutorial is the PSM report:

Scrolling towards left will show the sequence for the PSM that matched to these metapeptide entries. Column 3 is the sequence matched for each PSM entry. Every identified PSM is a new row in the tabular output.

In the following steps of this tutorial, selected portions of this output will be extracted and used for analysis of the taxonomic make-up of the sample as well as the biochemical functions represented by the proteins identified.

## Taxonomy analysis

In the previous section, the genome sequencing and mass spectrometry data from processing of biological samples was used to identify peptides present in those samples. Now those peptides are used as evidence to infer which organisms are represented in the sample, and what biological functions those peptides and associated proteins suggest are occurring.

The UniProt organization collects and annotates all known proteins for organisms. A UniProt entry includes the protein amino acid sequence, the NCBI taxonomy, and any annotations about structure and function of the protein. The UniPept web resource developed by Ghent University will be used to match the sample peptides to proteins. UniPept indexes all Uniprot proteins and provides a fast matching algorithm for peptides.

### comment Unipept

Users can access UniPept via a web page and paste peptide sequences into the search form to retrieve protein information. But we’ll use the Galaxy Unipept tool to automate the process. The Unipept tool sends the peptide list to the UniPept REST API service, then transforms the results into datasets that can be further analyzed or operated on within Galaxy.

#### Recieving the list of peptides: Query Tabular

In order to use Unipept, a list containing the peptide sequences has to be generated. The tool Query Tabular can load tabular data (the PSM report in this case) into a SQLite data base. As a tabular file is being read, line filters may be applied and an SQL query can be performed.

### hands_on Hands-on: Query Tabular

1. Query Tabular tool: Run Query Tabular with:

• Database Table: Click on + Insert Database Table:
• Tabular Dataset for Table: The PSM report

Section Filter Dataset Input:

• Filter Tabular Input Lines: Click on + Insert Filter Tabular Input Lines:
• Filter By: Select by regex expression matching
• regex pattern: ^\d
• action for regex match: include line on pattern match

Section Table Options:

• Specify Name for Table: psm
• Specify Column Names (comma-separated list): id,,sequence,,,,,,,,,,,,,,,,,,,,confidence,validation

### comment 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.

• Only load the columns you have named into database: Yes

• Save the sqlite database in your history: Yes

### comment Querying SQLite Databases

• Query Tabular can also use an existing SQLite database. Activating Save the sqlite database in your history will store the created database in the history, allowing to reuse it directly.
• SQL Query to generate tabular output:

SELECT distinct sequence

FROM psm

WHERE confidence >= 95

ORDER BY sequence


### question Questions

The SQL query might look confusing at first, but having a closer look should clarify a lot.

1. What does FROM psm mean?
2. What need to be changed if we only want peptides with a confidence higher then 98%?

### solution Solution

1. We want to read from table “psm”. We defined the name before in the “Specify Name for Table” option.
2. We need to change the value in line 3: “WHERE validation IS NOT ‘Confident’ AND confidence >= 98”
• include query result column headers: No
2. Click Execute and inspect the query results file after it turned green. If everything went well, it should look similiar:

While we can proceed with this list of peptides, let’s practice using the created SQLite database for further queries. We might not only be interested in all the distinct peptides, but also on how many PSMs a single peptide had. Therefore we can search the database for the peptides and count the occurrence without configuring the tables and columns again:

### hands_on Hands-on: SQLite to tabular

1. SQLite to tabular tool: Run SQLite to tabular with:

• SQL Query:

SELECT sequence as "peptide", count(id) as "PSMs"

FROM psm

WHERE confidence >= 95

GROUP BY sequence

ORDER BY sequence

2. Click Execute. The resulting file should have two columns, one with the distinct peptides, the other with the count number of PSMs.

#### Retrieve taxonomy for peptides: Unipept

The generated list of peptides can now be used to search via Unipept. We do a taxonomy analysis using the UniPept pept2lca function to return the taxonomic lowest common ancestor for each peptide:

### hands_on Hands-on: Unipept

1. Unipept tool: Run Unipept with:

• Unipept application: pept2lca: lowest common ancestor
• Peptides input format: tabular
• Tabular Input Containing Peptide column: The query results file.
• Select column with peptides: Column 1
• Choose outputs: Select tabular and JSON taxonomy tree
2. Click Execute. The history should grow by two files. View each to see the difference.

### comment Comment

The JSON (JavaScript Object Notation) file contains the same information as the tabular file but is not comfortably human readable. Instead, we can use it to use JavaScript libraries to visualize this data.

3. Visualize the data:

• Click on the JSON output file from the Unipept tool to expand it. Click on the Visualize button and select Unipept Tree viewer:

• A new window should appear with a visualization of the taxonomy tree of your data. Use the mouse wheel to scroll in and out and click on nodes to expand or collapse them:

## Genus taxonomy level summary

The tabular Unipept output lists the taxonomy assignments for each peptide. To create a meaningful summary, the Query Tabular tool is once again used, aggregating the number of peptides and PSMs for each genus level taxonomy assignment:

### hands_on Hands-on: Query Tabular

1. Query Tabular tool: Run Query Tabular with:

• Database Table: Click on + Insert Database Table
• Tabular Dataset for Table: The PSM report

Section Filter Dataset Input:

• Filter Tabular Input Lines: Click on + Insert Filter Tabular Input Lines:
• Filter By: Select by regex expression matching
• regex pattern: ^\d
• action for regex match: include line on pattern match

Section Table Options:

• Specify Name for Table: psm
• Specify Column Names (comma-separated list): ,,sequence,,,,,,,,,,,,,,,,,,,,confidence,validation

• Only load the columns you have named into database: Yes
2. Repeat this step to have a second Database Table:

• Database Table: Click on + Insert Database Table
• Tabular Dataset for Table: The Unipept tabular/tsv output

Section Filter Dataset Input:

• Filter Tabular Input Lines: Click on + Insert Filter Tabular Input Lines:
• Filter By: Select by regex expression matching
• regex pattern: #peptide
• action for regex match: exclude line on pattern match

Section Table Options:

• Specify Name for Table: lca
• Specify Column Names (comma-separated list): peptide,,,,,,,,,,,,,,,,,,,,,genus

• Only load the columns you have named into database: Yes

• Save the sqlite database in your history: Yes

• SQL Query to generate tabular output:

SELECT lca.genus,count(psm.sequence) as "PSMs",count(distinct psm.sequence) as "DISTINCT PEPTIDES"

FROM psm LEFT JOIN lca ON psm.sequence = lca.peptide

WHERE confidence >= 95

GROUP BY lca.genus

ORDER BY PSMs desc, 'DISTINCT PEPTIDES' desc

3. Click Execute and inspect the query results file after it turned green:

## Functional Analysis

Recent advances in microbiome research indicate that functional characterization via metaproteomics analysis has the potential to accurately measure the microbial response to perturbations. In particular, metaproteomics enables the estimation of the function of the microbial community based on expressed microbial proteome.

In the following chapter, a functional analysis will be performed using the UniPept application pept2prot in order to match the list of peptides with the correlated Gene Ontology terms. This allows to get an insight of the biological process, the molecular function and the cellular component related to the sample data.

### comment Gene Ontology (GO) Consortium

The Gene Ontology Consortium provides with its Ontology a framework for the model of biology. The GO defines concepts/classes used to describe gene function, and relationships between these concepts. It classifies functions along three aspects:

• molecular function

• molecular activities of gene products
• cellular component

• where gene products are active
• biological process

• pathways and larger processes made up of the activities of multiple gene products.

For this tutorial, a tabular file containing the relevant GO terms has been created. It contains the GO aspect, the ID and the name. It is available at Zenodo: .

1. Import the file Gene_Ontology_Terms.tabular from Zenodo.

In the upload window of Galaxy you can set the filetype and related genome of the file you’re uploading in the corresponding columns beforehand. This might be handy if the automatic detection of the filetype didn’t work out perfectly or if you want to avoid setting the genome later on, especially for multiple files.

As default, Galaxy takes the link as name.

• Rename the datasets to a more descriptive name, e.g. Gene Ontology Terms

### details Creating your own Gene Ontology list

The latest Gene Ontology can be downloaded here as a text file in the OBO format. OBO files are human-readable (in addition to machine-readable) and can be opened in any text editor. They contain more information than just the name and aspect.

In order to receive a file like we use in the tutorial for your own analysis, different tools are available to extract information from OBO files, one of them being ONTO-PERL. An example file with all GO terms from 08.07.2017 named Gene_Ontology_Terms_full_07.08.2017.tabular can be found on the Zenodo repository of this tutorial as well.

#### Retrieve GO IDs for peptides: Unipept

The UniPept application pept2prot can be used to return the list of proteins containing each peptide. The option retrieve extra information option is set to yes so that we retrieve Gene Ontology assignments (go_references) for each protein.

### hands_on Hands-on: Unipept

1. Unipept tool: Run Unipept with:

• Unipept application: pept2prot: UniProt entries containing a given tryptic peptide
• retrieve extra information: Yes
• Peptides input format: tabular
• Tabular Input Containing Peptide column: The first query results file.
• Select column with peptides: Column 1
• Choose outputs: Select tabular
2. Click Execute.

3. inspect the result:

• The output should be a tabular file containing a column labeled go_references. This is what we’re looking for.

#### Combine all information to quantify the GO results

As a final step we will use Query Tabular in a more sophisticated way to combine all information to quantify the GO analysis. The three used file and the extracted information are:

• Gene Ontology Terms:
• go_id to match with Normalized UniPept output
• The GO aspect to group the results in three separate files
• The GO description to annotate the results
• Normalized UniPept output:
• peptide to match with PSM Report and to count distinct peptides per GO term
• go_reference to match with Gene Ontology Terms
• PSM Report:
• sequence to match with Normalized UniPept output
• id to count distinct PSM’s per GO term

### hands_on Hands-on: Query Tabular

1. Query Tabular tool: Run Query Tabular with:

• Database Table: Click on + Insert Database Table
• Tabular Dataset for Table: The Gene Ontology Terms file

Section Filter Dataset Input:

• Filter Tabular Input Lines: Click on + Insert Filter Tabular Input Lines:
• Filter By: Select skip leading lines
• Skip lines: 1

Section Table Options:

• Specify Name for Table: go
• Specify Column Names (comma-separated list): aspect,go_id,description
• Table Index: Click on + Insert Table Index:
• This is a unique index: No
• Index on Columns: aspect,go_id
2. Repeat this step to have a second Database Table:

• Database Table: Click on + Insert Database Table
• Tabular Dataset for Table: The Unipept tabluar/tsv output

Section Filter Dataset Input:

• Filter Tabular Input Lines: Click on + Insert Filter Tabular Input Lines:
• Filter By: Select skip leading lines
• Skip lines: 1
• Add another Filter: Click on + Insert Filter Tabular Input Lines:
• Filter By: Select prepend a line number column

Section Table Options:

• Specify Name for Table: bering_prot
• Specify Column Names (comma-separated list): id,peptide,uniprot_id,taxon_id,taxon_name,ec_references,go_references,refseq_ids,refseq_protein_ids,insdc_ids,insdc_protein_ids
• Table Index: Click on + Insert Table Index:
• This is a unique index: No
• Index on Columns: id,peptide
3. Repeat this step to have another Database Table:

• Database Table: Click on + Insert Database Table
• Tabular Dataset for Table: The same Unipept tabluar/tsv output

Section Filter Dataset Input:

• Filter Tabular Input Lines: Click on + Insert Filter Tabular Input Lines:
• Filter By: Select skip leading lines
• Skip lines: 1
• Add another Filter: Click on + Insert Filter Tabular Input Lines:
• Filter By: Select prepend a line number column
• Add another Filter: Click on + Insert Filter Tabular Input Lines:
• Filter By: Select select columns
• enter column numbers to keep: 1,7
• Add another Filter: Click on + Insert Filter Tabular Input Lines:
• Filter By: Select normalize list columns, replicates row for each item in list
• enter column numbers to normalize: 2
• List item delimiter in column:   (a single blank character)

• The UniPept result file can contain multiple GO IDs in a single row. In order to create a normalized table of this data, these rows will be split so each record contains only one GO ID.

Section Table Options:

• Specify Name for Table: bering_prot_go
• Specify Column Names (comma-separated list): id,go_reference
• Table Index: Click on + Insert Table Index:
• This is a unique index: No
• Index on Columns: go_reference,id
4. Repeat this step to have another Database Table:

• Database Table: Click on + Insert Database Table
• Tabular Dataset for Table: The PSM Report

Section Filter Dataset Input:

• Filter Tabular Input Lines: Click on + Insert Filter Tabular Input Lines:
• Filter By: Select by regex expression matching
• regex pattern: ^\d
• action for regex match: include line on pattern match
• Add another Filter: Click on + Insert Filter Tabular Input Lines:
• Filter By: Select select columns
• enter column numbers to keep: 1,3,23,24

Section Table Options:

• Specify Name for Table: bering_psms
• Specify Column Names (comma-separated list): id,sequence,confidence,validation
• Only load the columns you have named into database: Yes
• Table Index: Click on + Insert Table Index:
• This is a unique index: No
• Index on Columns: sequence,id
• Save the sqlite database in your history: Yes

• SQL Query to generate tabular output:

SELECT sequence as "peptide", count(id) as "PSMs"

FROM bering_psms

WHERE confidence >= 95

GROUP BY sequence

ORDER BY sequence

5. Click Execute.

With this we have combined all the data into a single database which we can now query to extract the desired information with SQLite to tabular:

### hands_on Hands-on: SQLite to tabular

1. SQLite to tabular tool: Run SQLite to tabular with:

• SQLite Database: The created SQLite database from the former step
• SQL Query:

SELECT go.description,

count(distinct bering_psms.sequence) as "bering_peptides", count(distinct bering_psms.id) as "bering_psms"

FROM go JOIN bering_prot_go ON go.go_id = bering_prot_go.go_reference JOIN bering_prot on bering_prot_go.id = bering_prot.id JOIN

bering_psms ON bering_prot.peptide = bering_psms.sequence

WHERE go.aspect = 'molecular_function'

GROUP BY go.description

ORDER BY  bering_peptides desc,bering_psms desc

2. Click Execute.
3. Repeat these steps two times by replacing molecular_function in the fifth row of the SQL query by biological_process and cellular_component.

With these three output files the functional analysis of this tutorial is finished. Each record contains the name of a GO term, the amount of peptides related to it and the amount of PSMs for these peptides.

### keypoints Key points

• Use dataset collections

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

• Learning the basics of SQL queries can pay off

# 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.

# Citing this Tutorial

1. Timothy J. Griffin, Pratik Jagtap, James Johnson, Clemens Blank, Subina Mehta, 2021 Metaproteomics tutorial (Galaxy Training Materials). /training-material/topics/proteomics/tutorials/metaproteomics/tutorial.html Online; accessed TODAY
2. Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012

### details BibTeX

@misc{proteomics-metaproteomics,
author = "Timothy J. Griffin and Pratik Jagtap and James Johnson and Clemens Blank and Subina Mehta",
title = "Metaproteomics tutorial (Galaxy Training Materials)",
year = "2021",
month = "03",
day = "12"
url = "\url{/training-material/topics/proteomics/tutorials/metaproteomics/tutorial.html}",
note = "[Online; accessed TODAY]"
}
@article{Batut_2018,
doi = {10.1016/j.cels.2018.05.012},
url = {https://doi.org/10.1016%2Fj.cels.2018.05.012},
year = 2018,
month = {jun},
publisher = {Elsevier {BV}},
volume = {6},
number = {6},
pages = {752--758.e1},
author = {B{\'{e}}r{\'{e}}nice Batut and Saskia Hiltemann and Andrea Bagnacani and Dannon Baker and Vivek Bhardwaj and Clemens Blank and Anthony Bretaudeau and Loraine Brillet-Gu{\'{e}}guen and Martin {\v{C}}ech and John Chilton and Dave Clements and Olivia Doppelt-Azeroual and Anika Erxleben and Mallory Ann Freeberg and Simon Gladman and Youri Hoogstrate and Hans-Rudolf Hotz and Torsten Houwaart and Pratik Jagtap and Delphine Larivi{\{e}}re and Gildas Le Corguill{\'{e}} and Thomas Manke and Fabien Mareuil and Fidel Ram{\'{\i}}rez and Devon Ryan and Florian Christoph Sigloch and Nicola Soranzo and Joachim Wolff and Pavankumar Videm and Markus Wolfien and Aisanjiang Wubuli and Dilmurat Yusuf and James Taylor and Rolf Backofen and Anton Nekrutenko and Björn Grüning},
title = {Community-Driven Data Analysis Training for Biology},
journal = {Cell Systems}
}
`