Search

Your search keyword '"Gessulat, Siegfried"' showing total 20 results
Start Over Author "Gessulat, Siegfried"
20 results on '"Gessulat, Siegfried"'

1. Unifying the analysis of bottom-up proteomics data with CHIMERYS

Author

Frejno, Martin, primary, Berger, Michelle T, additional, Tueshaus, Johanna, additional, Hogrebe, Alexander, additional, Seefried, Florian, additional, Graber, Michael, additional, Samaras, Patroklos, additional, Fredj, Samia Ben, additional, Sukumar, Vishal, additional, Eljagh, Layla, additional, Bronshtein, Igor, additional, Mamisashvili, Lizi, additional, Schneider, Markus, additional, Gessulat, Siegfried, additional, Schmidt, Tobias, additional, Kuster, Bernhard, additional, Zolg, Daniel P, additional, and Wilhelm, Mathias, additional

Published
2024
Full Text
View/download PDF

2. Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics

Author

Wilhelm, Mathias, Zolg, Daniel P., Graber, Michael, Gessulat, Siegfried, Schmidt, Tobias, Schnatbaum, Karsten, Schwencke-Westphal, Celina, Seifert, Philipp, de Andrade Krätzig, Niklas, Zerweck, Johannes, Knaute, Tobias, Bräunlein, Eva, Samaras, Patroklos, Lautenbacher, Ludwig, Klaeger, Susan, Wenschuh, Holger, Rad, Roland, Delanghe, Bernard, Huhmer, Andreas, Carr, Steven A., Clauser, Karl R., Krackhardt, Angela M., Reimer, Ulf, and Kuster, Bernhard

Published
2021
Full Text
View/download PDF

3. Author Correction: Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics

Author

Wilhelm, Mathias, Zolg, Daniel P., Graber, Michael, Gessulat, Siegfried, Schmidt, Tobias, Schnatbaum, Karsten, Schwencke-Westphal, Celina, Seifert, Philipp, de Andrade Krätzig, Niklas, Zerweck, Johannes, Knaute, Tobias, Bräunlein, Eva, Samaras, Patroklos, Lautenbacher, Ludwig, Klaeger, Susan, Wenschuh, Holger, Rad, Roland, Delanghe, Bernard, Huhmer, Andreas, Carr, Steven A., Clauser, Karl R., Krackhardt, Angela M., Reimer, Ulf, and Kuster, Bernhard

Published
2021
Full Text
View/download PDF

6. Toward an Integrated Machine Learning Model of a Proteomics Experiment

Author

Neely, Benjamin A., primary, Dorfer, Viktoria, additional, Martens, Lennart, additional, Bludau, Isabell, additional, Bouwmeester, Robbin, additional, Degroeve, Sven, additional, Deutsch, Eric W., additional, Gessulat, Siegfried, additional, Käll, Lukas, additional, Palczynski, Pawel, additional, Payne, Samuel H., additional, Rehfeldt, Tobias Greisager, additional, Schmidt, Tobias, additional, Schwämmle, Veit, additional, Uszkoreit, Julian, additional, Vizcaíno, Juan Antonio, additional, Wilhelm, Mathias, additional, and Palmblad, Magnus, additional

Published
2023
Full Text
View/download PDF

9. Mass-spectrometry-based draft of the human proteome

Author

Wilhelm, Mathias, Schlegl, Judith, Hahne, Hannes, Gholami, Amin Moghaddas, Lieberenz, Marcus, Savitski, Mikhail M., Ziegler, Emanuel, Butzmann, Lars, Gessulat, Siegfried, Marx, Harald, Mathieson, Toby, Lemeer, Simone, Schnatbaum, Karsten, Reimer, Ulf, Wenschuh, Holger, Mollenhauer, Martin, and Slotta-Huspenina, Julia

Subjects
Proteomics -- Methods, Mass spectrometry -- Usage, Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Proteomes are characterized by large protein-abundance differences, cell-type- and time-dependent expression patterns and post-translational modifications, all of which carry biological information that is not accessible by genomics or transcriptomics. Here we present a mass-spectrometry-based draft of the human proteome and a public, high-performance, in-memory database for real-time analysis of terabytes of big data, called ProteomicsDB. The information assembled from human tissues, cell lines and body fluids enabled estimation of the size of the protein-coding genome, and identified organ-specific proteins and a large number of translated lincRNAs (long intergenic non-coding RNAs). Analysis of messenger RNA and protein-expression profiles of human tissues revealed conserved control of protein abundance, and integration of drug-sensitivity data enabled the identification of proteins predicting resistance or sensitivity. The proteome profiles also hold considerable promise for analysing the composition and stoichiometry of protein complexes. ProteomicsDB thus enables navigation of proteomes, provides biological insight and fosters the development of proteomic technology. A mass-spectrometry-based draft of the human proteome and a public database for analysis of proteome data are presented; assembled information is used to estimate the size of the protein-coding genome, to identify organ-specific proteins, proteins predicting drug resistance or sensitivity, and many translated long intergenic non-coding RNAs, and to reveal conserved control of protein abundance. Mapping the human proteome More than a decade after publication of the draft human genome sequence, there is no direct equivalent for the human proteome. But in this issue of Nature two groups present mass spectrometry-based analysis of human tissues, body fluids and cells mapping the large majority of the human proteome. Akhilesh Pandey and colleagues identified 17,294 protein-coding genes and provide evidence of tissue- and cell-restricted proteins through expression profiling. They highlight the importance of proteogenomic analysis by identifying translated proteins from annotated pseudogenes, non-coding RNAs and untranslated regions. The data set is available on http://www.humanproteomemap.org. Bernhard Kuster and colleagues have assembled protein evidence for 18,097 genes in ProteomicsDB (available on https://www.proteomicsdb.org) and highlight the utility of the data, for example the identification of hundreds of translated lincRNAs, drug-sensitivity markers and discovering the quantitative relationship between mRNA and protein levels in tissues. Elsewhere in this issue, Vivien Marx reports on a third major proteomics project, the antibody-based Human Protein Atlas programme (http://www.proteinatlas.org/)., Author(s): Mathias Wilhelm [sup.1] [sup.2] , Judith Schlegl [sup.2] , Hannes Hahne [sup.1] , Amin Moghaddas Gholami [sup.1] , Marcus Lieberenz [sup.2] , Mikhail M. Savitski [sup.3] , Emanuel Ziegler [...]

Published
2014
Full Text
View/download PDF

10. INFERYS rescoring: Boosting peptide identifications and scoring confidence of database search results

Author

Zolg, Daniel P., primary, Gessulat, Siegfried, additional, Paschke, Carmen, additional, Graber, Michael, additional, Rathke‐Kuhnert, Magnus, additional, Seefried, Florian, additional, Fitzemeier, Kai, additional, Berg, Frank, additional, Lopez‐Ferrer, Daniel, additional, Horn, David, additional, Henrich, Christoph, additional, Huhmer, Andreas, additional, Delanghe, Bernard, additional, and Frejno, Martin, additional

Published
2021
Full Text
View/download PDF

11. Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics

Author

Wilhelm, Mathias, Zolg, Daniel P., Graber, Michael, Gessulat, Siegfried, Schmidt, Tobias, Schnatbaum, Karsten, Schwencke-Westphal, Celina, Seifert, Philipp, de Andrade Krätzig, Niklas, Zerweck, Johannes, Knaute, Tobias, Bräunlein, Eva, Samaras, Patroklos, Lautenbacher, Ludwig, Klaeger, Susan, Wenschuh, Holger, Rad, Roland, Delanghe, Bernard, Huhmer, Andreas, Carr, Steven A., Clauser, Karl R., Krackhardt, Angela M., Reimer, Ulf, and Kuster, Bernhard

Subjects
Article, Machine learning, Mass spectrometry, MHC, Molecular medicine, Proteomics, ddc
Published
2020

13. Ein Deep-Learning-Modell zur proteomweiten Vorhersage von Peptid-Tandem-Massenspektren

Author

Gessulat, Siegfried, Küster, Bernhard (Prof. Dr.), and Käll, Lukas (Prof., Ph.D.)

Subjects
Biowissenschaften, Biologie, ddc:570, ddc:540, Chemie, proteomics, machine learning, deep learning, Proteomik, Maschinelles Learnen, Deep learning
Abstract

Mass spectrometry-based proteomics is the leading technology to identify peptides. The identification strongly relies on software with database searching, and spectral library matching being two successful approaches. Both benefit from accurate predictions. This work presents a deep learning model whose predictions exceed experimental spectra from synthetic peptides in quality. It can be calibrated to different conditions and generalizes to various proteases. The utility of the model is shown in the context of both: database searching and spectral library matching. Massenspektrometriebasierte Proteomik ist die führende Technologie zur Identifizierung von Peptiden und stützt sich wesentlich auf Software. Die Suche in Sequenzdatenbanken und der Abgleich mit Spektralbibliotheken sind zwei Identifikationsansätze, die von akkuraten Vorhersagen von Spektren profitieren. Diese Arbeit stellt ein Deep Learning-basiertes Modell vor, dessen Vorhersagen die Qualität experimenteller Spektren von synthetischen Peptiden übertreffen. Es lässt sich für unterschiedliche Bedingungen kalibrieren und generalisiert für mehrere Proteasen. Prosits Nutzen wird im Kontext beiden Identifikationsansätzen gezeigt.

Published
2020

14. ProteomicsDB: a multi-omics and multi-organism resource for life science research

Author

Samaras, Patroklos, primary, Schmidt, Tobias, additional, Frejno, Martin, additional, Gessulat, Siegfried, additional, Reinecke, Maria, additional, Jarzab, Anna, additional, Zecha, Jana, additional, Mergner, Julia, additional, Giansanti, Piero, additional, Ehrlich, Hans-Christian, additional, Aiche, Stephan, additional, Rank, Johannes, additional, Kienegger, Harald, additional, Krcmar, Helmut, additional, Kuster, Bernhard, additional, and Wilhelm, Mathias, additional

Published
2019
Full Text
View/download PDF

18. ProteomicsDB

Author

Schmidt, Tobias, primary, Samaras, Patroklos, additional, Frejno, Martin, additional, Gessulat, Siegfried, additional, Barnert, Maximilian, additional, Kienegger, Harald, additional, Krcmar, Helmut, additional, Schlegl, Judith, additional, Ehrlich, Hans-Christian, additional, Aiche, Stephan, additional, Kuster, Bernhard, additional, and Wilhelm, Mathias, additional

Published
2017
Full Text
View/download PDF

19. Building ProteomeTools based on a complete synthetic human proteome

Author

Zolg, Daniel P, primary, Wilhelm, Mathias, additional, Schnatbaum, Karsten, additional, Zerweck, Johannes, additional, Knaute, Tobias, additional, Delanghe, Bernard, additional, Bailey, Derek J, additional, Gessulat, Siegfried, additional, Ehrlich, Hans-Christian, additional, Weininger, Maximilian, additional, Yu, Peng, additional, Schlegl, Judith, additional, Kramer, Karl, additional, Schmidt, Tobias, additional, Kusebauch, Ulrike, additional, Deutsch, Eric W, additional, Aebersold, Ruedi, additional, Moritz, Robert L, additional, Wenschuh, Holger, additional, Moehring, Thomas, additional, Aiche, Stephan, additional, Huhmer, Andreas, additional, Reimer, Ulf, additional, and Kuster, Bernhard, additional

Published
2017
Full Text
View/download PDF

20. ProteomicsDB.

Author

Schmidt, Tobias, Samaras, Patroklos, Frejno, Martin, Gessulat, Siegfried, Barnert, Maximilian, Kienegger, Harald, Krcmar, Helmut, Schlegl, Judith, Ehrlich, Hans-Christian, and Aiche, Stephan

Published
2018
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results