Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
August 19th, 2025
Mass spectrometry-based proteomic data is available in open databases and accessible using free tools. Given the complexity of database searches and descriptions, many biologists lack the knowledge to utilize these datasets. Here, we provide a guide on using free tools for basic proteomic data searches.
Our work provides a guide for biologists to analyze complex proteomic data using free tools. We aim to empower them to validate findings and explore public datasets. Many biologists cannot use public proteome data due to a lack of clear guide.
Our work bridges this, enabling validation without any new wildlife experiments. Our work utilizes free state-of-the-art software that is vendor-agnostic. These tools are faster, more sensitive, and provide more precise proteome discovery than many traditional tools.
To begin, download the reference proteome FASTA file from the UniProt database. Click on the add FASTA button to load the reference proteome file into the DIA-NN software. Select the two options, FASTA digest for library free search library generation, and deep-learning based spectra, RTs, and IMs prediction under the precursor ion generation section.
Then, click on the run button to generate a predicted spectral library. Unselect the two options under the precursor ion generation section. Click on the type button that corresponds to the file format under the input section to load the DIA data.
Now, set both mass accuracy and MS1 accuracy to zero parts per million under the algorithm section. Adjust the precursor and fragment mass range settings under the precursor ion generation section according to the experimental setup. Keep the other software settings unchanged.
Next, click on the run button. Wait until finished is displayed on the operation interface, indicating the analysis is complete. Click on the frag pipe icon located in bin folder after installation.
Navigate to the config tab to view all dependent settings. Check whether MSFragger, IonQuant, diaTracer, DIA-NN, and Python modules are available on your system. If any modules are missing, click on download update or download to retrieve them.
Now, switch to the workflow tab. Select default from the workflow dropdown and click on load workflow. Then, click on add files to input the file paths.
Assign experiment name and biological replicate number under assign files or leave it blank. Next, click on the database tab to switch to it. Load a FASTA file from disk or download one that corresponds to the sample species.
During download, select the options reviewed sequences only, add decoys, and add common contaminants for a simple run. Click on the MSFragger tab to change the view. Select closed search default config and click on load.
Under peak matching settings, retain all default values. For both calibration and optimization, select none to reduce processing time. For protein digestion, adjust the parameters based on your experiment's requirements and maintain the remaining default settings.
Now, switch to the validation tab. Uncheck predict RT and predict spectra, as these options are intended for data-independent acquisition workflows. Click on the quant MS1 tab.
Select run MS1 quant and then click on load quant defaults. Choose ion quant and leave all other settings at default values. Finally, click on the run tab to proceed.
Select the desired output directory and click on run to begin analyzing the data. In patients with pancreatic ductal adenocarcinoma, or PDAC, SERPINA5 and HPSE showed significantly reduced expression, while FGB displayed increased expression in serum compared to normal individuals. In hepatocellular carcinoma tumor samples, ENO3, PLS3, MTAP, SERPINB9, and ITPR2 showed reduced expression relative to paired tissues, whereas ME1, CYP27A1, RPS16, and ATP5PF were significantly increased.
Heat map visualization revealed consistently elevated protein expression in the serum of PDAC patients compared to normal individuals. Gene ontology enrichment analysis of PDAC serum revealed significant upregulation of processes related to coagulation and hemostasis. GO analysis of hepatocellular carcinoma tumors identified enrichment in nucleotide and metabolic processes, including purine nucleotide metabolism and NAD metabolic pathways.
KEGG pathway enrichment analysis of PDAC serum revealed significant activation of the complement and coagulation cascades pathway, along with glycosaminoglycan degradation. KEG analysis in hepatocellular carcinoma tumor samples identified enrichment in PPAR signaling, carbon metabolism, and neurodegenerative disease pathways, though with lower statistical significance. The protein-protein interaction network for upregulated PDAC serum proteins revealed a central cluster involving coagulation factor 11, fibrinogen beta chain, and plasma serine protease inhibitor, as well as several isolated proteins, including HPSE, CD5 antigen-like, and CRISP3.
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Overview
This study provides a comprehensive guide for biologists to analyze complex proteomic data using free, state-of-the-art tools.
Key Study Components
Research Area
- Proteomics
- Data analysis
- Biological validation
Background
- Challenges faced by biologists in using public proteome data due to complexity.
- The necessity for straightforward guidelines to empower data validation and exploration.
- Importance of utilizing vendor-agnostic, efficient software tools for proteomic analysis.
Methods Used
- Mass spectrometry-based proteomic data analysis.
- Use of software such as DIA-NN and MSFragger for data processing.
- Guidelines for loading and analyzing datasets, including settings for spectral library generation.
Main Results
- Identification of significant gene expression changes in pancreatic ductal adenocarcinoma and hepatocellular carcinoma.
- Heat map visualizations reveal distinct protein expression profiles.
- GO and KEGG analyses highlight pathways activated in the context of cancers studied.
Conclusions
- The study demonstrates the effectiveness of free software in advancing proteomic analysis.
- Findings are relevant for ongoing research in cancer biology and proteomics.
Frequently Asked Questions
Executive Industry Relevance
Mass spectrometry-based proteomics is increasingly central to early discovery and translational research, yet data complexity and lack of accessible workflows limit its broader adoption in biopharma R&D. This article demonstrates how free, vendor-agnostic computational tools enable robust interrogation and validation of public proteomic datasets, supporting hypothesis-driven research without new experimental burden. Streamlined data analysis workflows enhance predictive confidence and facilitate cross-study comparisons, directly impacting target validation and biomarker discovery pipelines.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Enables interrogation of proteomic datasets for functional target validation and pathway analysis.
- Supports biological de-risking by revealing protein expression changes in disease-relevant samples.
- Facilitates portfolio triage through quantitative identification of dysregulated proteins.
Screening & Assay Development
- Prepares validated protein lists and quantitative outputs for downstream assay development.
- Standardizes data processing using reproducible, open-source computational workflows.
- Enables scalable screening readiness by leveraging public datasets and automated analysis pipelines.
Translational & Preclinical Research
- Aligns proteomic findings with disease biology and pathway enrichment for translational biomarker exploration.
- Supports continuity from discovery to preclinical validation by integrating public and proprietary datasets.
- Provides mechanistic de-risking through pathway and network analysis of dysregulated proteins.
Pipeline & Workflow Integration
This workflow positions mass spectrometry data analysis from early discovery through lead identification and translational research, leveraging public repositories and free computational tools.
- Discovery Biology: Empowers hypothesis testing and pathway clarification using curated proteomic datasets.
- Screening: Delivers reproducible, quantitative protein expression outputs for assay development.
- Analytics: Generates volcano plots, heat maps, and pathway enrichment statistics for comparative analysis.
- Translational Research: Connects protein-level changes to disease mechanisms and biomarker candidates.
- Enterprise Reuse: Establishes a reusable, scalable workflow for ongoing proteomic data mining and validation.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence and reduces mechanistic ambiguity in target and biomarker validation.
- Operational Value: Promotes standardization, reproducibility, and scalability across R&D teams.
- Strategic Value: Enables informed go/no-go decisions and capital-efficient portfolio advancement.
- Portfolio Impact: Supports risk-adjusted prioritization and cross-functional data integration.
Implementation Considerations
- Requires expertise in proteomics data analysis and familiarity with open-source computational tools.
- Needs access to appropriate computational infrastructure and software dependencies.
- Demands cross-team standardization of data formats and analysis parameters.
- Adaptation may be needed for different species or experimental designs via appropriate FASTA files.
- Dependent on quality and completeness of public or in-house proteomic datasets.
