Biology subjects

Jagtap, P. D.

Publications and source records attributed to Jagtap, P. D..

9 recordsLinked to original sources

bioRxiv2026

Comparative Evaluation of DDA and DIA Based Proteomic Workflows in Beryllium Related Lung Disease

We compared traditional data-dependent acquisition mass spectrometry (DDA-MS) with the increasingly adopted data-independent acquisition (DIA-MS) to evaluate their relative utility for large-scale quantitative biofluid proteomics of lung compartments, specifically paired bronchoalveolar lavage (BAL) cells and bronchoalveolar lavage fluid (BALF). Using beryllium-related granulomatous lung disease as a focused model, we analyzed BALF and BAL cells from beryllium-sensitized (BeS) individuals using both acquisition strategies to assess proteome depth, quantitative completeness, and analytical robustness. In BAL cells, 5,640 proteins were identified by DDA-MS and 5,227 by DIA-MS; however, DIA-MS yielded markedly improved quantitative completeness, with 5,178 proteins ([~]99%) quantified across all samples compared with 3,539 ([~]63%) quantified by DDA-MS. While 3,397 proteins were quantified by both methods, DIA-MS uniquely quantified 1,781 lower-abundance proteins. Proteins identified by both DIA and DDA-MS approaches revealed pathways associated with granulomatous inflammation, including Toll-like receptor, clathrin-mediated endocytosis, sirtuin, and C-type lectin receptor signaling, whereas DIA-MS resolved additional pathways, such as the complement cascade, coagulation system, and JAK/IL-6-type cytokine signaling. In BALF, although more proteins were identified by DDA-MS than by DIA-MS (2,069 vs 1,742), DIA-MS achieved greater quantitative completeness, with 1,695 proteins quantified across all samples compared with 1,050 using DDA-MS, underscoring its suitability for biomarker-oriented analyses in lung fluid compartments. Together, these results support DIA-MS as a robust and sensitive platform for quantitative lung proteomics and discovery of disease-relevant protein signatures.

systems biology↗

bioRxiv2025

Evaluation of Parallel Accumulation-Serial Fragmentation methods for metaproteomics using a model microbiome

Mass spectrometry-based metaproteomics allows for the identification and quantification of thousands of proteins from clinical and environmental samples and is rapidly gaining importance in microbiome sciences. Metaproteomics researchers can measure taxonomic and functional abundances of microbiomes, shedding light on mechanistic details of microbiome interactions with their environment. However, metaproteomic analysis suffers from limited depth of coverage due to the presence of millions of peptides at lower abundance levels. Recent advances in data-independent acquisition mass spectrometry coupled with Parallel Accumulation-Serial Fragmentation (PASEF) technology offer improved depth of coverage. PASEF technology enables simultaneous accumulation of ions from multiple co-eluting peptides by combining ion mobility separation with dynamic quadrupole isolation, allowing efficient and selective fragmentation in a single scan. This boosts ion sampling efficiency and resolves overlapping signals with high sensitivity. In this study, we assessed proteome coverage, quantitative precision, and accuracy of Data-dependent acquisition (DDA) and Data-independent acquisition (DIA) methods coupled with the PASEF method. For this, we used a ground-truth mock community containing 28 species (30 strains) from all three domains of life and bacteriophages with a 400-fold dynamic range of organism abundance. Our results showed that diaPASEF demonstrated superior performance, identifying 168% more peptide precursors, 155% more peptides, and 66% more protein groups compared to ddaPASEF. Quantitative measurements showed improved precision with diaPASEF, with 26 out of 28 organisms exhibiting coefficient of variation values below 20%, compared to 24 organisms with ddaPASEF. Both ddaPASEF and diaPASEF methods accurately quantified the 22 most abundant organisms, while measurements of low-abundance bacteriophages showed significant deviation from expected values. Our findings demonstrate that diaPASEF provides enhanced depth of coverage and quantitative reliability for metaproteomics analysis, particularly beneficial for clinical and environmental microbiome studies where deeper functional characterization is essential. This study provides valuable benchmark data to facilitate the development of advanced bioinformatic methods for quantitative metaproteomics.

biochemistry↗

bioRxiv2025

A modular, Galaxy-based immunopeptidogenomic (iPepGen) analysis pipeline for discovery, verification, and prioritization of candidate cancer neoantigen peptides

Characterizing tumor-specific neoantigen peptides, derived from genomic or transcriptomic aberrations and presented to the immune system, is critical for immuno-oncology studies. To this end, the modular iPepGen immunopeptidogenomics pipeline provides these functions: (1) Neoantigen prediction and protein database generation from genomic or transcriptomic sequencing data; (2) Peptide identification (3) Verification from immunopeptidomic mass spectral data; (4) Neoantigen classification and visualization; (5) Candidate prioritization for further study. Easy access via a publicly available, scalable cloud-based gateway coupled with online, interactive training materials streamlines the adoption by cancer researchers who require immunopeptidogenomic analysis tools but lack advanced computational expertise and resources.

bioinformatics↗

bioRxiv2024

Comprehensive protease specificity profiling

Protease enzymes are of great importance in medicine, industry, and as research tools. Despite the crucial need for detailed knowledge of their proteolytic cleavage specificity, many proteases are poorly characterized. We present a method for fully characterizing the cleavage specificity of proteases through the comprehensive profiling of all possible permutations of octamer peptide substrates in a single experiment. The powerful combination of in vitro selection with high-throughput sequencing, mass spectrometry, and automated motif mining enabled the screening of mixtures of >1012 peptides. We developed freely available software that easily integrates the massive amounts of cleavage data into user-friendly specificity information. We applied this method to three different proteases that had either narrow (factor Xa) or broad specificity (ADAM17 and streptopain). The resulting specificity maps revealed motifs that corroborate canonical known cleavage sites, yet step further into extended spectrum preferences and yield insights into the function of broad specificity proteases.

biochemistry↗

bioRxiv2022

Galaxy Training: A Powerful Framework for Teaching!

There is an ongoing explosion of scientific datasets being generated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analysis and stewardship are still rarely taught in life science educational programs [1], resulting in a skills gap in many of the researchers tasked with analysing these big datasets. In order to address this skills gap and empower researchers to perform their own data analyses, the Galaxy Training Network (GTN) has previously developed the Galaxy Training Platform (https://training.galaxyproject.org); an open access, community-driven framework for the collection of FAIR training materials for data analysis utilizing the user-friendly Galaxy framework as its primary data analysis platform [2]. Since its inception, this training platform has thrived, with the number of tutorials and contributors growing rapidly, and the range of topics extending beyond life sciences to include topics such as climatology, cheminformatics and machine learning. While initially aimed at supporting researchers directly, the GTN framework has proven to be an invaluable resource for educators as well. We have focused our efforts in recent years on adding increased support for this growing community of instructors. New features have been added to facilitate the use of the materials in a classroom setting, simplifying the contribution flow for new materials, and have added a set of train-the-trainer lessons. Here, we present the latest developments in the GTN project, aimed at facilitating the use of the Galaxy Training materials by educators, and its usage in different learning environments.

bioinformatics↗

bioRxiv2020

2019 Association of Biomolecular Resource Facilities Multi-Laboratory Data-Independent Acquisition Study

Despite the advantages of fewer missing values by collecting fragment ion data on all analytes in the sample, as well as the potential for deeper coverage, the adoption of data-independent acquisition (DIA) in core facility settings has been slow. The Association of Biomolecular Resource Facilities conducted a large interlaboratory study to evaluate DIA performance in laboratories with various instrumentation. Participants were supplied with generic methods and a uniform set of test samples. The resulting 49 DIA datasets act as benchmarks and have utility in education and tool development. The sample set consisted of a tryptic HeLa digest spiked with high or low levels of four exogenous proteins. Data are available in MassIVE MSV000086479. Additionally, we demonstrate how the data can be analysed by focusing on two datasets using different library approaches and show the utility of select summary statistics. These data can be used by DIA newcomers, software developers, or DIA experts evaluating performance with different platforms, acquisition settings and skill levels.

bioinformatics↗

bioRxiv2019

Multi-omics Visualization Platform: An extensible Galaxy plug-In for multi-omics data visualization and exploration

BackgroundProteogenomics integrates genomics, transcriptomics and mass spectrometry (MS)-based proteomics data to identify novel protein sequences arising from gene and transcript sequence variants. Proteogenomic data analysis requires integration of disparate omic software tools, as well as customized tools to view and interpret results. The flexible Galaxy platform has proven valuable for proteogenomic data analysis. Here, we describe a novel Multi-omics Visualization Platform (MVP) for organizing, visualizing and exploring proteogenomic results, adding a critically needed tool for data exploration and interpretation. FindingsMVP is built as an HTML Galaxy plugin, primarily based on JavaScript. Via the Galaxy API, MVP uses SQLite databases as input -- a custom datatype (mzSQLite) containing MS-based peptide identification information, a variant annotation table, and a coding sequence table. Users can interactively filter identified peptides based on sequence and data quality metrics, view annotated peptide MS data, and visualize protein-level information, along with genomic coordinates. Peptides that pass the user-defined thresholds can be sent back to Galaxy via the API for further analysis; processed data and visualizations can also be saved and shared. MVP leverages the Integrated Genomics Viewer JavaScript (IGVjs) framework, enabling interactive visualization of peptides and corresponding transcript and genomic coding information within the MVP interface. ConclusionsMVP provides a powerful, extensible platform for automated, interactive visualization of proteogenomic results within the Galaxy environment, adding a unique and critically needed tool for empowering exploration and interpretation of results. The platform is extensible, providing a basis for further development of new functionalities for proteogenomic data visualization.

bioinformatics↗

bioRxiv2019

A sectioning and database enrichment approach for improved peptide spectrum matching in large, genome-guided protein sequence databases

Multi-omics approaches focused on mass-spectrometry (MS)-based data, such as metaproteomics, utilize genomic and/or transcriptomic sequencing data to generate a comprehensive protein sequence database. These databases can be very large, containing millions of sequences, which reduces the sensitivity of matching tandem mass spectrometry (MS/MS) data to sequences to generate peptide spectrum matches (PSMs). Here, we describe a sectioning method for generating an enriched database for those protein sequences that are most likely present in the sample. Our evaluation demonstrates how this method helps to increase the sensitivity of PSMs while maintaining acceptable false discovery rate statistics. We demonstrate increased true positive PSM identifications using the sectioning method when compared to the traditional large database searching method, whereas it helped in reducing the false PSM identifications when compared to a previously described two-step method for reducing database size. The sectioning method for large sequence databases enables generation of an enriched protein sequence database and promotes increased sensitivity in identifying PSMs, while maintaining acceptable and manageable FDR. Furthermore, implementation in the Galaxy platform provides access to a usable and automated workflow for carrying out the method. Our results show the utility of this methodology for a wide-range of applications where genome-guided, large sequence databases are required for MS-based proteomics data analysis.

bioinformatics↗

bioRxiv2019

Challenges in Peptide-Spectrum Matching: a Robust and Reproducible Statistical Framework for Removing Low-Accuracy, High-Scoring Hits

Workflows for large-scale (MS)-based shotgun proteomics can potentially lead to costly errors in the form of incorrect peptide spectrum matches (PSMs). To improve robustness of these workflows, we have investigated the use of the precursor mass discrepancy (PMD) to detect and filter potentially false PSMs that have, nonetheless, a high confidence score. We identified and addressed three cases of unexpected bias in PMD results: time of acquisition within a LC-MS run, decoy PSMs, and length of peptide. We created a post-analysis Bayesian confidence measure based on score and PMD, called PMD-FDR. We tested PMD-FDR on four datasets across three types of MS-based proteomics projects: standard (single organism; reference database), proteogenomics (single organism; customized genomic-based database plus reference), and metaproteomics (microorganism community; customized conglomerate database). On a ground truth dataset and other representative data, PMD-FDR was able to detect 60-80% of likely incorrect PSMs (false-hits) while losing only 5% of correct PSMs (true-hits). PMD-FDR can also be used to evaluate data quality for results generated within different experimental PSM-generating workflows, assisting in method development. Going forward, PMD-FDR should provide detection of high-scoring but likely false-hits, aiding applications which rely heavily on accurate PSMs, such as proteogenomics and metaproteomics.

bioinformatics↗