ProTN

Version: 0.3.3.1

ProTN is now a Shiny app that provides an integrated pipeline for the complete downstream analysis of proteomics, phospho-proteomics, and interactomics data following peptide quantification. The app features four distinct workflows—proTN, phosproTN, phosproTN with proteome background, and interacTN—each accessible through a separate tab in the application menu.

ProTN is compatible with MS-based proteomic experiments processed using Proteome Discoverer and MaxQuant, two of the most widely used platforms for extracting peptide abundances from raw MS spectra. For proteome and interactomics analyses, it also supports input files generated by FragPipe and Spectronaut.

Designed to be user-friendly, fast, and comprehensive, ProTN delivers high-quality visualizations and tables to facilitate the biological interpretation of results across a range of experimental setups.

All required information can be found on the info page of the web app.

Before starting

Dependencies

ProTN requires R version >= 4.1 and RStudio.

The app should automatically start installing all required packages. Otherwise, manual installation can be done via the INSTALL.R script. Before executing the Shiny app, verify the correct installation.

How to execute the app

1. Open the file app.R.
2. Click Run App

Getting help

The menu is on the left of the web page and contains guide pages for all workflows and tabs for executing the pipelines. The last tab contains contact information.

The two info tabs contain information about the workflow and detailed information about input and output files.

Case study example

Note: The case study presented is the comparison of polysome-associated proteins with total proteins in human MCF7 cells. (PRIDE: PXD009417) (Clamer M, Tebaldi T, Lauria F, et al. Active Ribosome Profiling with RiboLace. Cell Rep. 2018;25(4):1097-1108.e5.)

Workflow description

ProTN

ProTN is an integrative pipeline that analyzes DDA proteomics data obtained from MS. It performs a complete analysis of raw files from different software, including their biological interpretation with enrichment and network analysis. ProTN executes a dual-level analysis at the protein and peptide levels.

Set settings for the execution and read the raw data from loaded files {#set-settings-for-the-execution-and-read-the-raw-data-from-loaded-files}

ProTN analyzes the results of Proteome Discoverer and MaxQuant. The essential parameters and files to run ProTN are: (additional details on the input can be found in the ProTN info tab)

• Software Analyzer: Determines which software was used to identify peptides and proteins.
  • PD: Proteome Discoverer
  • MQ: MaxQuant
  • SP: Spectronaut
  • FP: FragPipe

Files required for Proteome Discoverer

• Annotation file: This file provides metadata for the samples analyzed. It must be an Excel file with the following required columns:

  Column Name	Description
  File ID	Identifier used in column headers of the peptide file.
  Condition	Experimental group name. Used for comparisons.
  Sample	Clean sample name used downstream.
  color	(Optional) Plot color. Defaults are applied if missing.
  batch	(Optional) Batch ID for batch effect correction.

• Peptides file: Excel table with annotated peptides and abundance values.

  Column Name	Description
  Master Protein Accessions	Maps peptide to protein; only the first ID is kept.
  Annotated Sequence	Amino acid sequence including PTM annotations.
  Modifications	Post-translational modifications.
  Positions in Master Proteins	Position of peptide in the protein sequence.
  Abundance: <File ID>	Intensity/abundance for each sample. One column per sample.

• Proteins file: Excel table containing descriptive and accession information for proteins.

  Column Name	Description
  Accession	Unique protein identifier, used to join with peptide file.
  Description	Descriptive string, e.g., from UniProt.

Files required for MaxQuant

• Annotation file: This file provides metadata for the samples analyzed. It must be an Excel file with the following required columns:

  Column Name	Description
  Condition	Experimental condition (e.g. Control, Treated). Used for group comparison.
  Sample	Sample identifier. Must match sample names in the peptide file.
  color	(Optional) Color associated with the condition. If not present, default colors are assigned.
  batch	(Optional) Batch ID for batch effect correction. Required if batch correction is enabled.

• Evidence pipeline:

  • evidence.txt: This is a TSV/CSV file containing peptide-level quantification data. Required columns:

    Column Name	Description
    Sequence	Amino acid sequence of the peptide.
    Modifications	PTMs of the peptide.
    Gene names	Gene symbol associated with the peptide.
    Protein names	Protein description. If missing, will be merged from annotation file.
    Leading razor protein	UniProt accession. Used for annotation enrichment.
    Raw file	File/sample ID. Must match entries in the annotation file.
    Intensity	Peptide intensity value. Used for quantification.
    Leading proteins	Used for filtering out contaminants (e.g. "CON_").

• Peptide and ProteinGroups pipeline:

  • peptides.txt: Tab-delimited file with peptide-level quantification. Required columns:

    Column Name	Description
    Sequence	Amino acid sequence of the peptide.
    Gene names	If missing, inferred from Leading razor protein.
    Protein names	Protein description. If missing, will be merged from annotation file.
    Leading razor protein	UniProt accession. Used for annotation enrichment.
    Intensity <Sample>	Intensity values for each sample (e.g. Intensity Sample1).

  • proteinGroups.txt: Tab-delimited file providing protein-level information. Required columns:

    Column Name	Description
    Majority protein IDs	Used to extract the Leading razor protein.
    Fasta headers	Used for protein description.

Files required for Spectronaut

• Annotation file: (Optional) If provided, this file should contain metadata for each sample. If not provided, the pipeline will extract sample annotations directly from the peptide file.

  Column Name	Description
  Condition	Experimental group label. Used for comparison between conditions.
  Sample	Sample identifier. Must match entries in the peptide file.
  color	(Optional) Color for visualization. Default colors will be assigned if missing.
  batch	(Optional) Batch ID for batch correction. Required if batch correction is enabled.

• Spectronaut report: This is a TSV/CSV file containing peptide-level data. Required columns:

  Column Name	Description
  PG.ProteinAccessions	Protein group accessions.
  PEP.StrippedSequence	Peptide sequence without modifications.
  EG.ModifiedSequence	Peptide sequence with modifications.
  PEP.Quantity	Peptide quantification value.
  R.FileName	Sample identifier (column used is defined by sample_col).
  R.Condition	Condition identifier (column used if annotation file not provided).

Files required for FragPipe

• Annotation file: This file provides metadata for the samples analyzed. It must be an Excel file with the following required columns:

  Column Name	Description
  Condition	Experimental condition (e.g. Control, Treated). Used for group comparison.
  Sample	Sample identifier. Must match sample names in the peptide file.
  color	(Optional) Color associated with the condition. If not present, default colors are assigned.
  batch	(Optional) Batch ID for batch effect correction. Required if batch correction is enabled.

• combined_modified_peptide.tsv: The peptide quantification file must contain raw or normalized intensity values for each sample and peptide. Required columns:

  Column Name	Description
  Protein ID	Protein accession or identifier.
  Protein Description	Descriptive name of the protein.
  Gene	Gene symbol.
  Peptide Sequence	Amino acid sequence of the peptide.
  Assigned Modifications	Sequence with nucleotide modifications.
  Prev AA	Used to determine tryptic condition.
  Next AA	Used to determine tryptic condition.
  <Sample> Intensity	One column per sample named like <Sample> Intensity.

Normalization and imputation of the intensities

The intensities are log2 transformed and normalized with DEqMS (Zhu 2022). Two methods are applied because double normalization is required—one for peptides and one for proteins. At the peptide level, normalization is done by the function equalMedianNormalization, which normalizes intensity distributions in samples so that they have a median equal to 0.

At the protein level, this operation is executed by the function medianSweeping. It applies the same median normalization used for peptides, and also summarizes the peptide intensities into protein relative abundance by the median sweeping method.

Imputation:

• PhosR: Imputation is performed on peptide and protein abundances with the Bioconductor package PhosR. Round imputation is performed in the absence of replicates. ProTN uses two functions of PhosR for imputation: one imputes the missing values for a peptide across replicates within a single condition, and another applies a tail-based imputation approach as implemented in Perseus.
• Gaussian Estimation: Imputation is performed on peptide and protein abundances using Gaussian estimation, where missing values are sampled from a normal distribution defined by the mean and standard deviation of observed intensities. This preserves data variance and reduces bias from missingness within conditions.
• missForest: Imputation is performed on peptide and protein abundances using the missForest R package, which applies a non-parametric random forest algorithm to predict missing values. This approach captures nonlinear relationships between features, preserving complex data structures.
• pcaMethods: Imputation is performed on peptide and protein abundances using the pcaMethods R package with the svdImpute function, which estimates missing values by reconstructing the data matrix from its leading singular vectors. This approach leverages global correlation structures to provide consistent estimates.

Method	Package	Main Idea	Typical Use
PhosR imputation	PhosR (Bioconductor)	Designed for phosphoproteomics; implements round-robin (iterative imputation without replicates) and paired tail-based imputation (using replicate structure).	Phosphoproteomics with sparse coverage or missingness tied to peptide abundance.
Gaussian estimation imputation		Draws values from a Gaussian distribution defined by low-intensity tail of observed data (shifted mean, reduced SD).	When MNAR (Missing Not At Random) is likely due to detection limit.
missForest	missForest (R)	Non-parametric iterative imputation using random forests. Predicts missing entries using nonlinear relationships between features.	General proteomics where missingness relates to multiple covariates, or when structure is complex.
pcaMethods svdImpute	pcaMethods (Bioconductor)	Uses Singular Value Decomposition to reconstruct missing values from lower-rank structure in the data.	Well-replicated datasets with high correlation between samples.

In this step, two MDSs and two PCAs (proteins and peptides) are generated.

Statistical differential analysis

The workflow continues with differential analysis. This phase is applied to proteins and peptides to obtain the significant proteins and peptides. Two slightly different methodologies are applied: for proteins, the DEqMS package (Zhu 2022) is used, while for peptides, the Limma package (Ritchie et al. 2015) is used. DEqMS is developed on top of Limma, but the method estimates different prior variances for proteins quantified by different numbers of PSMs/peptides per protein, thereby achieving better accuracy.

Limma and DEqMS calculate DEPs for each comparison in the design file parameter. Each peptide or protein has different parameters: the log2 Fold Change, the P.Value, the adjusted P.Value, and the log2 expression. In this pipeline, a protein/peptide is significant if it passes three thresholds. A protein/peptide for each comparison can be up-regulated or down-regulated. It is up-regulated if:

• the log2 FC is higher than the Fold Change threshold (FC > Log2 FC thr),
• the P.Value is lower than the threshold (P.Value < P.Value thr).

It is down-regulated if:

• the log2 FC is lower than the Fold Change threshold (FC < -Log2 FC thr),
• the P.Value is lower than the threshold (P.Value < P.Value thr).

In the output, for each comparison, this distinction is reported in the "class" column, which assumes the value "+" if up-regulated, "-" if down-regulated, and "=" if not significant.

Various figures are generated, first a bar plot that graphically represents the DEPs identified, followed by comparison-specific volcano plots.

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Report creation and download of the results

The results are summarized in an HTML web-page report. Furthermore, the experiment is described by a large number of files; a description of each generated file can be found in section 4. Details on the output files. All files are grouped in a zip file and downloaded.

ADDITIONAL STEPS:

B1. Batch Effect correction

If required by the experiment, a batch correction step can be applied using proBatch (Cuklina et al. 2018). The batches need to be defined in the Sample_Annotation file, where the column MS_batch is required.

E1. Enrichment analysis of the Differentially Expressed Proteins

The biological interpretation of the Differentially Expressed Proteins starts with the enrichment step. To execute this analysis, ProTN uses EnrichR (Jawaid 2022), a widely used tool that searches a large number of datasets to obtain information about many categories. EnrichR organizes its hundreds of datasets into 8 sections: Transcription, Pathways, Ontologies, Diseases/Drugs, Cell Types, Misc, Legacy, and Crowd.

Since the analysis that a user wants to perform can be different, each comparison has 3 sets of proteins: the up-regulated (called Up), the down-regulated (called Down), and the set (called all) obtained by merging up- and down-regulated proteins. EnrichR provides much information for each term. It returns statistical parameters, such as P.Value, fdr, odds ratio, and combined score. At the same time, it gives the overlap size, the number of genes in the term, and the genes of the input list found in the term.

The data downloaded from EnrichR are exported in two formats. The tool creates an RData file of the complete data frame, so the user can easily import it into R to perform further analysis and plots. Additionally, it generates an Excel file with only the significant terms. The process uses the filters defined in the options file to differentiate relevant terms from the overall dataset.

A term is significant if it has: - a P.Value lower than P.Value thr for enrichment (P.Value < P.Value thr for enrichment), - an Overlap Size higher than Overlap size thr for enrichment (Overlap Size > Overlap size thr for enrichment).

Besides that, the pipeline plots the enrichment results in different figures. The 4 plots can be split into 2 categories. The first two are filtered on the datasets, showing only the datasets written in the column DB to analyze of the options file. The other 2 are filtered on words to search in the term description, using the list of words in column Terms to search.

E1.1. Enrichment analysis of the whole set of proteins discovered by the experiment

In some cases, it can be useful to analyze the enrichment of the whole proteome discovered by the experiment. For example, it can be used as a negative control for the differentially expressed proteins. So, the entire proteome is analyzed with EnrichR and saved in an RData and an Excel file. Also, as before, 4 plots can be generated; in this case, the negative control provided by the whole proteome is added as the last column.

N1. Protein-Protein Interaction network analysis of Differentially Expressed Proteins

The last analytical step is the Protein-Protein Interaction (PPI) network analysis, since PPIs are essential in almost all cellular processes and crucial for understanding cell physiology in different states. For each comparison, ProTN analyzes the interaction between the DEPs using STRING (Szklarczyk et al. 2021).

PhosProTN

PhosProTN is an integrative pipeline for phosphoproteomic analysis of DDA experimental data obtained from MS. It performs a complete analysis of raw files from Proteome Discoverer (PD) or MaxQuant (MQ), including their biological interpretation, enrichment, and network analysis. PhosProTN analyzes phosphoproteomic data at the peptide level.

The phospho-workflow is similar to the one described previously; below are reported only the different steps.

Set settings for the execution and read the raw phospho data from loaded files {#set-settings-for-the-execution-and-read-the-raw-phospho-data-from-loaded-files}

ProTN analyzes the results of Proteome Discoverer and MaxQuant. The essential parameters and files to run ProTN are: (additional details on the input can be found in the ProTN info tab)

• Software Analyzer: Determines which software was used to identify peptides and proteins.

  • PD: Proteome Discoverer
  • MQ: MaxQuant

Files required for Proteome Discoverer

• Annotation file: This file provides metadata for the samples analyzed. It must be an Excel file with the following required columns:

  Column Name	Description
  File ID	Identifier used in column headers of the peptide file.
  Condition	Experimental group name. Used for comparisons.
  Sample	Clean sample name used downstream.
  color	(Optional) Plot color. Defaults are applied if missing.
  batch	(Optional) Batch ID for batch effect correction.

• Peptides file: Excel table with annotated peptides and abundance values.

  Column Name	Description
  Master Protein Accessions	Maps peptide to protein; only the first ID is kept.
  Annotated Sequence	Amino acid sequence including PTM annotations.
  Modifications	Post-translational modifications.
  Positions in Master Proteins	Position of peptide in the protein sequence.
  Abundance: <File ID>	Intensity/abundance for each sample. One column per sample.

• Proteins file: Excel table containing descriptive and accession information for proteins.

  Column Name	Description
  Accession	Unique protein identifier, used to join with peptide file.
  Description	Descriptive string, e.g., from UniProt.

• PSM file

  Column Name	Description
  ptmRS: Best Site Probabilities	Used to resolve phosphosite ambiguity.
  Precursor Abundance	Abundance value used to filter invalid entries.
  Master Protein Accessions	Matches protein IDs for mapping.
  Annotated Sequence	Used to resolve conflicting PTM assignments.

Files required for MaxQuant

• Annotation file: This file provides metadata for the samples analyzed. It must be an Excel file with the following required columns:

  Column Name	Description
  Condition	Experimental condition (e.g. Control, Treated). Used for group comparison.
  Sample	Sample identifier. Must match sample names in the peptide file.
  color	(Optional) Color associated with the condition. If not present, default colors are assigned.
  batch	(Optional) Batch ID for batch effect correction. Required if batch correction is enabled.

• evidence.txt: This is a TSV/CSV file containing peptide-level quantification data. Required columns:

  Column Name	Description
  Modifications	PTMs of the peptide.
  Gene names	Gene symbol associated with the peptide.
  Protein names	Protein description. If missing, will be merged from annotation file.
  Leading razor protein	UniProt accession. Used for annotation enrichment.
  Raw file	File/sample ID. Must match entries in the annotation file.
  Intensity	Peptide intensity value. Used for quantification.
  Phospho (STY) Probabilities	Used to filter for confident phosphorylation.

ADDITIONAL STEPS:

K1. Activity kinase tree analysis of the Differentially Expressed Phosphosite

The last analytical step is the kinase tree analysis. In phospho-proteomics, it is extremely useful to study the activation status of kinases based on differentially expressed substrates identified by differential analysis. For each comparison, PhosProTN predicts the activation state of kinases using PhosR (Kim et al. 2021). PhosR provides a kinase-substrate relationship score and, based on that, prioritizes potential kinases that could be responsible for the phosphorylation change of phosphosites based on kinase recognition motifs and phosphoproteomic dynamics.

The activity score provided by PhosR is used to generate a graphical version of the human kinome tree using CORAL (Metz K.S. et al. 2018), a web Shiny app for visualizing both quantitative and qualitative data. It generates high-resolution scalable vector graphic files suitable for publication without the need for refinement in graphic editing software.

PhosProTN with proteome background

PhosProTN with proteome background is an integrative pipeline for phosphoproteomic analysis of DDA experimental data obtained from MS. It performs a complete analysis of raw files from Proteome Discoverer (PD) or MaxQuant (MQ), including their biological interpretation, enrichment, and network analysis. PhosProTN analyzes phosphoproteomic data at the peptide level using the proteome analysis of the same conditions as background.

The phospho-workflow is similar to the one described previously; below are reported only the different steps.

Set settings for the execution and read the raw data from loaded files

This workflow requires both files for phosphoproteomics, as described previously, and the proteome file, as described here.

InteracTN

InteracTN is an integrated pipeline for the analysis of interactomics data derived from DDA mass spectrometry experiments. It supports input from multiple widely used proteomics platforms, including Proteome Discoverer (PD), MaxQuant (MQ), Spectronaut (SP), and FragPipe (FP). The pipeline also includes network reconstruction and visualization, helping users interpret protein–protein interactions in the context of biological pathways and complexes. Designed to be robust and user-friendly, InteracTN enables researchers to efficiently explore and interpret interactome data for a deeper understanding of cellular mechanisms and protein functions.

Getting help

Bugs and errors can be reported on the issues page on GitHub. Before filing new issues, please read the documentation and take a look at currently open and already closed discussions.

Contacts

Gabriele Tomè, Developer: gtome@fbk.eu{.email}; gabriele.tome@unitn.it{.email}

Dr. Toma Tebaldi, PI: toma.tebaldi@unitn.it{.email}