tutorial.md

layout	title	zenodo_link	level	questions	objectives	time_estimation	key_points	contributors	follow_up_training	requirements
tutorial_hands_on	metaQuantome 2: Function	https://doi.org/10.5281/zenodo.4110725	Intermediate	How do I look at differentially expressed proteins? How do I get top 5 or 10 function present in the sample? How can I visualize my results?	A functional and quantitational analysis of metaproteomic mass spectrometry data.	1h	All the inputs should have one column in common i.e. peptides. Functional annotation can be performed using GO terms and EC numbers Having basic knowledge regarding tools that can perform functional annotation can come in handy	subinamehta timothygriffin pratikdjagtap emmaleith mariecrane pravs3683	type topic_name tutorials internal proteomics metaproteomics type topic_name tutorials internal proteomics metaquantome-taxonomy	type topic_name tutorials internal proteomics metaquantome-data-creation

Introduction

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metaQuantome software suite {% cite Easterly2019 %} was developed by the {% cite Galaxy-P %} for quantitative and statistical analysis of metaproteomics data. For taxonomic and functional expression analysis within the microbial community, metaQuantome leverages peptide-level quantitative information to generate visual outputs for data interpretation. It also generates outputs that help in understanding the taxonomic contribution to a selected function as well as functions expressed by selected taxonomic group.

In this tutorial, we will learn specifically about the metaQuantome Function workflow. In particular, we will learn about how peptide-level quantitation and associated functional information can be used to generate bar plots (for functional information such as molecular function, cellular components and biological processes), volcano plots (to detect differentially expressed function) and heatmap cluster analysis. To demonstrate the use of this workflow, we have used a thermophilic biogas reactor dataset wherein municipal food waste and manure is digested to generate methane gas ({% cite Delogu2020 %}). After one round in the reactor, the microbial community was simplified and enriched via serial dilution. This inoculum was then transferred to a solution of cellulose from Norwegian Spruce and incubated at 65°C. Triplicate samples were taken in a time series from 0 to 43 hours after inoculation and mass spectrometry data was acquired on a Q-Exactive (Thermo) mass spectrometer. For this training, we have chosen three time points (8 hours, 18 hours and 33 hours) from this dataset.

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Agenda

In this tutorial, we will cover:

1. TOC {:toc}

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Pretreatments

The first step in this tutorial is to get the data from the Zenodo link provided and make sure that it is in the correct format.

Get data

{% icon hands_on %} Hands-on: Data upload

1. Create a new history for this tutorial and give it a meaningful name

   {% snippet faqs/galaxy/histories_create_new.md %} {% snippet faqs/galaxy/histories_rename.md %}

2. Import the files from [Zenodo]({{ page.zenodo_link }}): a Functional File and an Intensity file.

   https://zenodo.org/record/4110725/files/Function-File.tabular
   https://zenodo.org/record/4110725/files/Intensity-File.tabular
   
   

   {% snippet faqs/galaxy/datasets_import_via_link.md %}

   Alternatively, import the files from the shared data library (GTN - Material -> {{ page.topic_name }} -> {{ page.title }})

   {% snippet faqs/galaxy/datasets_import_from_data_library.md %}

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Download metaQuantome Databases

{% icon hands_on %} Hands-on: Run metaQuantome databases

1. {% tool metaQuantome: databases %} with the following parameters:

   • {% icon param-select %} "Databases to Download": Select All (NCBI Taxonomy Database, Gene Ontology Database and Enzyme Commission (EC) database)

   {% icon comment %} Comment

   metaQuantome uses freely available bioinformatic databases to expand your set of direct annotations.

   The databases are:

   1. NCBI taxonomy database. consists of a list of all currently identified taxa and the relationships between them.

   2. Gene Ontology (GO) term database. metaQuantome uses the go-basic.obo file and the metagenomics slim GO (subset of the full GO). More details are available at http://geneontology.org/docs/download-ontology/

   3. ENZYME database with Enzyme Classification (EC) numbers. This database classifies enzymes and organizes the relationships between them.

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{% icon question %} Questions

1. Why is it necessary to download metaQuantome databases?
2. Can these databases be downloaded from other sources?

{% icon solution %} Solution

1. metaQuantome databases help the metaQuantome:expand tool to co-relate information your have provided in your input files according to the GO term, taxa or EC databases and its relationship .
2. For metaQuantome, we use freely available databases. This module downloads the most recent releases of the specified databases and stores them in a single file, which can then be accessed by the rest of the metaQuantome modules. For reference, the taxonomy database is the largest (~500 Mb), while the GO and EC databases are smaller: ~34 Mb and ~10Mb, respectively. Also, note that the databases will be stored in the history so that the date of download can be referenced later. Thus, the databases will not be modified, except for the NCBI database.

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Create metaQuantome sample file

The create samples file module is used to generate the samples file input file for the metaQuantome workflow. This input file is used to specify the column names used for each experimental group. These column names are referenced when handling the input data and performing statistical analysis.

{% icon hands_on %} Hands-on: Create an experimental Design file for sorting samples

1. {% tool metaQuantome: create samples file %} with the following parameters:
   • {% icon param-select %} "Sample file creation method": Manually specify experimental conditions and samples
   • In "Insert Samples":
     • "1.Samples":
       • "Group Name": T2
         • "Column": T2_A1,T2_B1
     • "2.Samples":
       • "Group Name": T4
         • "Column": T4A_1,T4B_1
     • "3.Samples":
       • "Group Name": T7
         • "Column": T7A_1,T7B_1

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Run metaQuantome

metaQuantome: expand

The expand module is the first analysis step in the metaQuantome analysis workflow, and can be run to analyze differently expressed functions in the samples. In function mode, the following information is required apart from metaQuantome databases and samples file:

• The name of the peptide column in the functional annotation file (peptide)
• The name of the functional annotation column in the functional annotation file (go_term)
• The name of the peptide column in the Intensity file (peptide)

{% icon hands_on %} Hands-on: Run metaQuantome expand

1. {% tool metaQuantome: expand %} with the following parameters:

   • {% icon param-file %} "Database Archive File": metaQuantome databases (output of metaQuantome: database {% icon tool %})
   • {% icon param-file %} "Samples file": metaQuantome: create samples_file (output of metaQuantome: create samples file {% icon tool %})
   • "Mode": Functional analysis
     • {% icon param-file %} "Functional file": Function File (Input dataset)
     • "Functional file: peptide column name": peptide
     • "Ontology": Gene Ontology (GO terms)
     • "Functional column name": go_term
   • {% icon param-file %} "Intensity file": Intensity File (Input dataset)
     • "Intensity file: peptide column name": peptide

2. Rename {% icon galaxy-pencil %} the output file to metaQuantome expand.

   {% icon comment %} Comment

   The structure of the output file depends on the analysis mode and the experimental design, but the columns generally look like this, with one row for each term:

term id	info about term. (one or more columns)	mean term intensity (by sample group)	term intensity (by sample)	number of unique peptides (by sample)	number of sample children in each sample
term1	name, rank, etc.	note that this is the log2 of the mean intensity	this is the log2 of term intensity in each sample. Missing data is coded as NA.	integer. 0 is coded as NA	integer. 0 is coded as NA

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metaQuantome: filter

The filter module is the second step in the metaQuantome workflow. The filter module filters the expanded terms to those that are representative of the data according to the sample parameters the user has specified.

{% icon hands_on %} Hands-on: Filtering the expanded data

1. {% tool metaQuantome: filter %} with the following parameters:

   • "Mode": Functional analysis
     • "Ontology": Gene Ontology (GO terms)
   • {% icon param-file %} "Samples file": metaQuantome:create samples_file (output of metaQuantome: create samples file {% icon tool %})
   • {% icon param-file %} "metaquantome expand file": metaQuantome:expand (output of metaQuantome: expand {% icon tool %})
   • "min_peptides": 1
   • "min_pep_nsamp": 1
   • "min_children_non_leaf": 2
   • "min_child_nsamp": 1
   • "qthreshold": 2

2. Rename {% icon galaxy-pencil %} the output file to metaQuantome:Filter.

   {% icon comment %} Comment

   To learn more about the filter module please read the metaQuantome paper published by Galaxy-P {% cite Easterly2019 %}. {: .comment}

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{% icon question %} Questions

1. Can we change the filter parameters?

{% icon solution %} Solution

1. The parameters can be changed according to the experimental design or depending on the data.

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metaQuantome: stat

{% icon hands_on %} Hands-on: Statistical analysis of the filtered data on multiple conditions.

1. {% tool metaQuantome: stat %} with the following parameters:

   • {% icon param-file %} "Samples file": metaQuantome:create samples_file (output of metaQuantome: create samples file {% icon tool %})
   • {% icon param-file %} "metaquantome expanded or filtered file": metaQuantome:Filter (output of metaQuantome: filter {% icon tool %})
   • "Mode": Functional analysis
     • "Ontology": Gene Ontology (GO terms)
   • "Name of the control sample group of control (used as denominator for fold change evaluation).": T2
   • "Statistical test to perform": standard t-test (parametric)
   • "Perform paired tests": Yes

2. Rename {% icon galaxy-pencil %} the output file to metaQuantome:stat.

   {% icon comment %} Comment

   In this tutorial, we have used the sample T2 as the control group for fold change evaluation. The users have the ability to choose their own control sample group. The initial version of the metaQuantome stat module could only process 2 conditions at a time. However, the new update of metaQuantome has the ability to perform statistical analysis across multiple conditions. {: .comment}

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Visualize your Data

The outputs of the visualization module of metaQuantome are high-quality, publication-ready visualizations: barplots for the analysis of a single sample or experimental condition and differential abundance analysis, volcano plots, heatmaps, and principal components analysis for comparisons between two or more experimental conditions. Here were are showing 2 visualizations: Barplot and Volcano Plot. The Heatmap and PCA plot for multiple conditions are under development. There are two outputs of the visualization tool : an HTML file (figure) and a tabular output containing the plot data.

metaQuantome: visualize Bar Chart

{% icon hands_on %} Hands-on: Bar chart visualization of Functions in T2 sample.

1. {% tool metaQuantome: visualize %} with the following parameters:

   • {% icon param-file %} "Tabular file from metaQuantome stats or metaQuantome filter": metaQuantome:stat (output of metaQuantome: stat {% icon tool %})
   • {% icon param-file %} "Samples file": metaQuantome:create samples_file (output of metaQuantome: stat {% icon tool %})
   • "Mode": Functional analysis
     • "Plot type": Bar Chart (bar)
       • "Mean intensity column name": T2_mean
       • "GO category to restrict to": Molecular Function
       • "Color for the bar fill": Orange

   {% icon comment %} Comment

   The user has a choice to run Bar plot for T2, T4 and T7 to know the top 5 or 10 most differently expressed functions. Here we have restricted the GO category to Molecular Function but the user can choose Biological Processes or Cellular component as their category.

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{% icon question %} Questions

1. Can we select any color for the bar chart?
2. Can we increase the number of terms to display?

{% icon solution %} Solution

1. Yes, the available colors are blue, orange, yellow, violet, black and red.
2. Yes, the user can choose their own number, however, the larger the display numbers are the dimensions of the image has to be adjusted accordingly.

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{% icon hands_on %} Hands-on: Bar chart visualization of Functions in T4 sample.

1. {% tool metaQuantome: visualize %} with the following parameters:
   • {% icon param-file %} "Tabular file from metaQuantome stats or metaQuantome filter": metaQuantome:stat (output of metaQuantome: stat {% icon tool %})
   • {% icon param-file %} "Samples file": metaQuantome:create samples_file (output of metaQuantome: create samples file {% icon tool %})
   • "Mode": Functional analysis
     • "Plot type": Bar Chart (bar)
       • "Mean intensity column name": T4_mean
       • "GO category to restrict to": Molecular Function
       • "Color for the bar fill": Orange

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{% icon hands_on %} Hands-on: Bar chart visualization of Functions in T7 sample.

1. {% tool metaQuantome: visualize %} with the following parameters:
   • {% icon param-file %} "Tabular file from metaQuantome stats or metaQuantome filter": metaQuantome:stat (output of metaQuantome: stat {% icon tool %})
   • {% icon param-file %} "Samples file": metaQuantome:create samples_file (output of metaQuantome: create samples file {% icon tool %})
   • "Mode": Functional analysis
     • "Plot type": Bar Chart (bar)
       • "Mean intensity column name": T7_mean
       • "GO category to restrict to": Molecular Function
       • "Color for the bar fill": Orange

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metaQuantome: visualize Volcano Plots

{% icon hands_on %} Hands-on: Volcano Plot visualization of the data T7 and T2.

1. {% tool metaQuantome: visualize %} with the following parameters:

   • {% icon param-file %} "Tabular file from metaQuantome stats or metaQuantome filter": metaQuantome:stat (output of metaQuantome: stat {% icon tool %})
   • {% icon param-file %} "Samples file": metaQuantome:create samples_file (output of metaQuantome: create samples file {% icon tool %})
   • "Mode": Functional analysis
     • "Plot type": Volcano Plot (volcano)
       • "Name of the fold change column in the stat dataframe": log2fc_T7_over_T2
       • "Name of the Corrected p-value column in the stat dataframe": corrected_p_T7_over_T2
       • "Name of the annotation column": id
     • {% icon param-select %} "Flip the fold change (i.e., multiply log fold change by -1)": No
     • {% icon param-select %} "Make one plot for each of BiologicalProcess, CellularComponent, and MolecularFunction GO term categories": Yes

   {% icon comment %} Comment

   Here, we show metaQuantome's Functional differential abundance volcano plot. The user may select the significance threshold (0.05 by default), and terms with statistically significant fold changes are colored green and labeled. However, we can currently only compare two conditions at a time. Here we have compared T7 and T2 time points. {: .comment}

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{% icon question %} Questions

1. Can we choose to Flip the fold change?
2. Could we create a single volcano plot for all the functions than splitting into different categories?

{% icon solution %} Solution

1. Certainly, the user has the ability to flip the fold changes.
2. Yes, the Go term that passes the threshold value will be represented in green. The user can then find out from the tabular output which category the GO term belonged to.

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{% icon hands_on %} Hands-on: Volcano Plot visualization of the data T4 and T2.

1. {% tool metaQuantome: visualize %} with the following parameters:
   • {% icon param-file %} "Tabular file from metaQuantome stats or metaQuantome filter": metaQuantome:stat (output of metaQuantome: stat {% icon tool %})
   • {% icon param-file %} "Samples file": metaQuantome:create samples_file (output of metaQuantome: create samples file {% icon tool %})
   • "Mode": Functional analysis
     • "Plot type": Volcano Plot (volcano)
       • "Name of the fold change column in the stat dataframe": log2fc_T4_over_T2
       • "Name of the Corrected p-value column in the stat dataframe": corrected_p_T4_over_T2
       • "Name of the annotation column": id
     • {% icon param-select %} "Flip the fold change (i.e., multiply log fold change by -1)": No
     • {% icon param-select %} "Make one plot for each of BiologicalProcess, CellularComponent, and MolecularFunction GO term categories": Yes

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Conclusion

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This completes the walkthrough of the metaQuantome function workflow. This tutorial is a guide to run the metaQuantome modules and can be used for metaproteomics research. Here, we have incorporated only two visualization modules in this workflow but we will make the heatmap and PCA plot also available. Researchers can use this workflow with their data also, please note that the tool parameters and the workflow will be needed to be modified accordingly. Also, refer to the metaQuantome data creation workflow to understand how to make files metaQuantome compatible.

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

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