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Articles by Thilo Muth
Limitations of de novo peptide sequencing in resolving sequence ambiguity
Abstract De novo peptide sequencing enables peptide identification from fragmentation spectra without relying on sequence databases. However, incomplete spectra create ambiguity, making unambiguous identification challenging. Recent deep learning advances have produced numerous de novo models that predict sequences and refine peptide-spectrum matches under such conditions. Yet, their relative strengths, weaknesses and ability to handle spectrum ambiguity remain unclear.
MultiStageSearch: An Iterative Workflow for Unbiased Taxonomic Analysis of Pathogens Using Proteogenomics
Introduction Click to copy section linkSection link copied! Viral and bacterial pathogens represent significant threats to public health, (1) as evidenced by the recent SARS-CoV-2 pandemic. In suspected cases of outbreaks, the availability of fast and accurate detection and identification methods for these pathogens is essential to facilitate timely intervention and containment measures.
MultiStageSearch: An Iterative Workflow for Unbiased Taxonomic Analysis of Pathogens Using Proteogenomics
Introduction Click to copy section linkSection link copied! Viral and bacterial pathogens represent significant threats to public health, (1) as evidenced by the recent SARS-CoV-2 pandemic. In suspected cases of outbreaks, the availability of fast and accurate detection and identification methods for these pathogens is essential to facilitate timely intervention and containment measures.
FIORA: Local neighborhood-based prediction of compound mass spectra from single fragmentation events - Nature Communications
Abstract Non-targeted metabolomics holds great promise for advancing precision medicine and biomarker discovery. However, identifying compounds from tandem mass spectra remains a challenging task due to the incomplete nature of spectral reference libraries. Augmenting these libraries with simulated mass spectra can provide the necessary references to resolve unmatched spectra, but generating high-quality data is difficult.
The Peptonizer2000: graphical model based taxonomic identifications of metaproteomic samples
Abstract Metaproteomics, the large-scale study of proteins from microbial communities, presents complex challenges in taxonomic inference due to sequence homologies between proteins within and across taxa. Commonly, taxonomic inference relies on heuristics, and few more advanced methods are available. We introduce the Peptonizer2000, a novel graphical model-based workflow designed to provide high-resolution taxonomic identifications of metaproteomic samples with associated confidence scores.
The Peptonizer2000: graphical model based taxonomic identifications of metaprotoemic samples
Abstract Metaproteomics, the large-scale study of proteins from microbial communities, presents complex challenges in taxonomic inference due to sequence homologies between proteins within and across taxa. Commonly, taxonomic inference relies on heuristics, and few more advanced methods are available. We introduce the Peptonizer2000, a novel graphical model-based workflow designed to provide high-resolution taxonomic identifications of metaproteomic samples with associated confidence scores.
MultiStageSearch: a multi-step proteogenomic workflow for taxonomic identification of viral proteome samples adressing database bias
Abstract The recent years, with the global SARS-Cov-2 pandemic, have shown the importance of strain level identification of viral pathogens. While the gold-standard approach for unknown viral sample identification remains genomics, studies have shown the necessity and advantages of orthogonal experimental approaches such as proteomics, based on proteomic database search methods.
gNOMO2: a comprehensive and modular pipeline for integrated multi-omics analyses of microbiomes
Abstract Background: Over the past few years, the rise of omics technologies has offered an exceptional chance to gain a deeper insight into the structural and functional characteristics of microbial communities. As a result, there is a growing demand for user friendly, reproducible, and versatile bioinformatic tools that can effectively harness multi-omics data to offer a holistic understanding of microbiomes.
Mistle: bringing spectral library predictions to metaproteomics with an efficient search index
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yHydra: Deep Learning enables an Ultra Fast Open Search by Jointly Embedding MS/MS Spectra and Peptides of Mass Spectrometry-based Proteomics
Abstract Mass spectrometry-based proteomics allows to study all proteins of a sample on a molecular level. The ever increasing complexity and amount of proteomics MS-data requires powerful and yet efficient computational and statistical analysis.
yHydra: Deep Learning enables an Ultra Fast Open Search by Jointly Embedding MS/MS Spectra and Peptides of Mass Spectrometry-based Proteomics
Abstract Mass spectrometry-based proteomics allows to study all proteins of a sample on a molecular level. The ever increasing complexity and amount of proteomics MS-data requires powerful and yet efficient computational and statistical analysis.
Editorial for Special Issue: Metaproteomics
Open AccessFeature PaperEditorial Jana Seifert 1,* and Thilo Muth 2,* 1 Institute of Animal Science, University of Hohenheim, 70599 Stuttgart, Germany 2 Bioinformatics Unit (MF 1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, 13353 Berlin, Germany * Authors to whom correspondence should be addressed.
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