Subina Mehta

The paper introduces the Microbiology Galaxy Lab, a community-driven platform for reproducible and FAIR microbial data analysis, offering tools for various omics data types and use cases.

This study compares DDA-MS and DIA-MS for proteomic analysis of lung compartments in beryllium-related disease, finding DIA-MS provides better quantitative completeness and detects more low-abundance proteins.

The paper introduces a modular pipeline for neoantigen discovery, verification, and prioritization in cancer research using immunopeptidogenomic data.

This study compares Scribe and database search engines in metaproteomic analysis of a ground-truth microbiome dataset, finding Scribe detects more proteins and quantifies microbial composition more accurately.

The paper introduces a clinical metaproteomics workflow that enables analysis of host-microbe dynamics in disease by integrating mass spectrometry and bioinformatics tools.

The paper introduces Galaxy Training Network (GTN) as a framework for creating and sharing FAIR training materials for data analysis, with recent improvements to support educators and expand its use in various learning environments.

This paper evaluates six metaproteomics tools for functional annotation using GO terms, finding differences in sensitivity and specificity, and integrates two into Galaxy for broader accessibility.

The paper evaluates and implements two label-free quantification tools in the Galaxy platform for proteomics and multi-omics analysis.

The paper introduces a sectioning method to improve peptide spectrum matching in large genome-guided protein sequence databases by increasing sensitivity while maintaining acceptable false discovery rates.

The paper introduces a Galaxy plugin for proteogenomic data visualization and exploration, enabling interactive analysis and sharing of results.

The paper introduces PMD-FDR, a Bayesian confidence measure to filter high-scoring but likely false peptide-spectrum matches in proteomics, improving accuracy in standard, proteogenomics, and metaproteomics datasets.