Thilo Muth

2.8k total citations
54 papers, 1.5k citations indexed

About

Thilo Muth is a scholar working on Molecular Biology, Spectroscopy and Ecology. According to data from OpenAlex, Thilo Muth has authored 54 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 46 papers in Molecular Biology, 35 papers in Spectroscopy and 8 papers in Ecology. Recurrent topics in Thilo Muth's work include Advanced Proteomics Techniques and Applications (34 papers), Genomics and Phylogenetic Studies (24 papers) and Metabolomics and Mass Spectrometry Studies (18 papers). Thilo Muth is often cited by papers focused on Advanced Proteomics Techniques and Applications (34 papers), Genomics and Phylogenetic Studies (24 papers) and Metabolomics and Mass Spectrometry Studies (18 papers). Thilo Muth collaborates with scholars based in Germany, Belgium and Norway. Thilo Muth's co-authors include Lennart Martens, Bernhard Y. Renard, Erdmann Rapp, Udo Reichl, Dirk Benndorf, Harald Barsnes, Marc Vaudel, Robert Heyer, Fabian Kohrs and Alexander Behne and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and Bioinformatics.

In The Last Decade

Thilo Muth

53 papers receiving 1.4k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Thilo Muth Germany 22 1.1k 630 235 92 80 54 1.5k
Roberto Vera Alvarez United States 16 1.0k 0.9× 87 0.1× 297 1.3× 29 0.3× 163 2.0× 34 1.6k
Natalie Tasman United States 6 1.3k 1.1× 957 1.5× 60 0.3× 35 0.4× 22 0.3× 7 1.7k
Yuqi Wang United States 4 984 0.9× 37 0.1× 161 0.7× 42 0.5× 49 0.6× 7 1.3k
Christine Hoogland Switzerland 21 1.0k 0.9× 601 1.0× 62 0.3× 81 0.9× 26 0.3× 48 1.5k
Jessica Chen United States 12 540 0.5× 49 0.1× 100 0.4× 27 0.3× 266 3.3× 26 1.1k
Amit Kumar Yadav India 20 573 0.5× 290 0.5× 49 0.2× 32 0.3× 46 0.6× 92 1.1k
Ib Søndergaard Denmark 17 444 0.4× 122 0.2× 28 0.1× 37 0.4× 132 1.6× 52 1.3k
Ignacio Ortea Spain 21 872 0.8× 105 0.2× 207 0.9× 38 0.4× 176 2.2× 37 1.3k
Sara Carillo Ireland 19 679 0.6× 169 0.3× 101 0.4× 18 0.2× 42 0.5× 56 1.0k

Countries citing papers authored by Thilo Muth

Since Specialization
Citations

This map shows the geographic impact of Thilo Muth's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Thilo Muth with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thilo Muth more than expected).

Fields of papers citing papers by Thilo Muth

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Thilo Muth. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Thilo Muth. The network helps show where Thilo Muth may publish in the future.

Co-authorship network of co-authors of Thilo Muth

This figure shows the co-authorship network connecting the top 25 collaborators of Thilo Muth. A scholar is included among the top collaborators of Thilo Muth based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Thilo Muth. Thilo Muth is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Muth, Thilo, Roger Karlsson, Yi‐Kuo Yu, & Gelio Alves. (2025). From Identification to Insight: Making Full Use of the Diagnostic Potential of MS/MS Proteotyping in Clinical Microbiology Using Efficient Bioinformatics. Journal of Proteome Research. 24(11). 5336–5351.
2.
Bossche, Tim Van Den, et al.. (2025). Metaproteomics Beyond Databases: Addressing the Challenges and Potentials of De Novo Sequencing. PROTEOMICS. 25(17-18). 51–61. 4 indexed citations
3.
Martens, Lennart, et al.. (2025). MultiStageSearch: An Iterative Workflow for Unbiased Taxonomic Analysis of Pathogens Using Proteogenomics. Journal of Proteome Research. 24(6). 2643–2656. 1 indexed citations
4.
Lisec, Jan, et al.. (2025). FIORA: Local neighborhood-based prediction of compound mass spectra from single fragmentation events. Nature Communications. 16(1). 2298–2298. 2 indexed citations
5.
Arıkan, Muzaffer & Thilo Muth. (2024). gNOMO2: a comprehensive and modular pipeline for integrated multi-omics analyses of microbiomes. GigaScience. 13. 2 indexed citations
6.
Arıkan, Muzaffer & Thilo Muth. (2023). Integrated multi-omics analyses of microbial communities: a review of the current state and future directions. Molecular Omics. 19(8). 607–623. 33 indexed citations
7.
El‐Athman, Rukeia, et al.. (2023). BAM Data Store. 1. 1 indexed citations
8.
Renard, Bernhard Y., et al.. (2022). Comprehensive evaluation of peptide de novo sequencing tools for monoclonal antibody assembly. Briefings in Bioinformatics. 24(1). 19 indexed citations
9.
Giese, Sven H., et al.. (2022). Ad hoc learning of peptide fragmentation from mass spectra enables an interpretable detection of phosphorylated and cross-linked peptides. Nature Machine Intelligence. 4(4). 378–388. 10 indexed citations
10.
Rehberg, Markus, Dirk Benndorf, Yvonne Genzel, et al.. (2021). Tracking changes in adaptation to suspension growth for MDCK cells: cell growth correlates with levels of metabolites, enzymes and proteins. Applied Microbiology and Biotechnology. 105(5). 1861–1874. 3 indexed citations
11.
Riffle, Michael, Bart Mesuere, Thilo Muth, et al.. (2020). Survey of metaproteomics software tools for functional microbiome analysis. PLoS ONE. 15(11). e0241503–e0241503. 30 indexed citations
12.
Doellinger, Joerg, et al.. (2020). TaxIt: An Iterative Computational Pipeline for Untargeted Strain-Level Identification Using MS/MS Spectra from Pathogenic Single-Organism Samples. Journal of Proteome Research. 19(6). 2501–2510. 15 indexed citations
13.
Bossche, Tim Van Den, Pieter Verschaffelt, Kay Schallert, et al.. (2020). Connecting MetaProteomeAnalyzer and PeptideShaker to Unipept for Seamless End-to-End Metaproteomics Data Analysis. Journal of Proteome Research. 19(8). 3562–3566. 7 indexed citations
14.
Grossegesse, Marica, Felix Hartkopf, Andreas Nitsche, et al.. (2020). Perspective on Proteomics for Virus Detection in Clinical Samples. Journal of Proteome Research. 19(11). 4380–4388. 27 indexed citations
15.
Hartkopf, Felix, Tim Van Den Bossche, Vitor C. Piro, et al.. (2020). gNOMO: a multi-omics pipeline for integrated host and microbiome analysis of non-model organisms. NAR Genomics and Bioinformatics. 2(4). 3 indexed citations
16.
Schiebenhoefer, Henning, Tim Van Den Bossche, Stephan Fuchs, et al.. (2019). Challenges and promise at the interface of metaproteomics and genomics: an overview of recent progress in metaproteogenomic data analysis. Expert Review of Proteomics. 16(5). 375–390. 58 indexed citations
17.
Muth, Thilo, Felix Hartkopf, Marc Vaudel, & Bernhard Y. Renard. (2018). A Potential Golden Age to Come—Current Tools, Recent Use Cases, and Future Avenues for De Novo Sequencing in Proteomics. PROTEOMICS. 18(18). e1700150–e1700150. 41 indexed citations
18.
Muth, Thilo, Fabian Kohrs, Robert Heyer, et al.. (2017). MPA Portable: A Stand-Alone Software Package for Analyzing Metaproteome Samples on the Go. Analytical Chemistry. 90(1). 685–689. 39 indexed citations
19.
Muth, Thilo, Dirk Benndorf, Udo Reichl, Erdmann Rapp, & Lennart Martens. (2012). Searching for a needle in a stack of needles: challenges in metaproteomics data analysis. Molecular BioSystems. 9(4). 578–585. 67 indexed citations
20.
Muth, Thilo, Marc Vaudel, Harald Barsnes, Lennart Martens, & Albert Sickmann. (2010). XTandem Parser: An open‐source library to parse and analyse X!Tandem MS/MS search results. PROTEOMICS. 10(7). 1522–1524. 40 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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