Thilo Muth

ProteoDUDes improves taxonomic profiling in metaproteomics by reducing false positives, achieving higher accuracy in identifying active organisms in complex samples.

The paper introduces yHydra, a foundation model for mass spectrometry-based proteomics that enables open and error-tolerant peptide and protein identification with improved performance.

MegaPX is a fast and space-efficient tool for classifying de novo peptide sequences against large protein databases using k-mer-based methods and Bloom filters.

The paper introduces Peptonizer2000, a tool that improves taxonomic identification in metaproteomics by combining peptide scores and taxon links with statistical models, showing higher specificity and confidence.

This study benchmarks eight de novo peptide sequencing models on proteomics datasets to evaluate their performance and identify limitations in resolving sequence ambiguity.

Fiora predicts compound mass spectra from single fragmentation events using graph neural networks, outperforming existing methods and enabling faster spectral library expansion.

MS2Rescore improves peptide identification and taxonomic specificity in metaproteomics by using machine learning-based rescoring, achieving lower false discovery rates with minimal sensitivity loss.

MultiStageSearch improves viral strain identification by combining proteomic and genomic databases, outperforming existing methods without relying on NCBI taxonomy.

gNOMO2 is an updated pipeline for analyzing microbiome multi-omics data, offering enhanced features for integration and visualization across various omics types.

Mistle improves metaproteomics by efficiently searching predicted spectral libraries, outperforming existing tools in speed and memory usage.

PepGM infers viral taxonomy from proteomic data with confidence scores, showing strain-level resolution and accurate species-level assignments in some cases.

This study evaluates de novo sequencing tools for monoclonal antibodies, finding that deep learning methods improve recall but challenges remain in heavy chain sequencing.

AHLF is a deep learning model trained on mass spectra to detect phosphorylated and cross-linked peptides with improved performance and interpretability.

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.

TaxIt is an iterative pipeline for strain-level classification using MS/MS spectra, improving accuracy over non-iterative methods by incorporating strain data and abundance weighting.