More and more proteomics datasets are becoming available in public repositories. The knowledge embedded in these datasets can be used to improve peptide identification workflows. Spectral library searching provides a straightforward method to boost identification rates using previously identified spectra. Alternatively, machine learning methods can learn from these spectra to accurately predict the behavior of peptides in a liquid chromatography-mass spectrometry system. At the basis of both approaches are spectral libraries: Unified collections of previously identified spectra. Organizations and projects such as the National Institute of Standards and Technology (NIST), the Global Proteome Machine, PeptideAtlas, PRIDE Archive and MassIVE ...
Introduction There exists a rich ecosystem of open-source MS software. Compared to vendor software ...
Proteomics is being transformed by deep learning methods that predict peptide fragmentation spectra....
Controlled vocabularies (CVs), i.e. a collection of predefined terms describing a modeling domain, u...
The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion...
The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion...
The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion...
The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion...
The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion...
Spectral library searching is an emerging approach in peptide identifications from tandem mass spect...
Spectral library searching is an emerging approach in peptide identification from tandem mass (MS/MS...
Spectral library searching is an emerging approach in peptide identifications from tandem mass spect...
Spectral library searching is a new approach in proteomic data analysis that promises to address som...
A notable inefficiency of shotgun proteomics experiments is the repeated rediscovery of the same ide...
Spectral library searching has been recently proposed as an alternative to sequence database searchi...
In contemporary peptide-centric or non-gel proteome studies, vast amounts of peptide fragmentation d...
Introduction There exists a rich ecosystem of open-source MS software. Compared to vendor software ...
Proteomics is being transformed by deep learning methods that predict peptide fragmentation spectra....
Controlled vocabularies (CVs), i.e. a collection of predefined terms describing a modeling domain, u...
The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion...
The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion...
The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion...
The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion...
The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion...
Spectral library searching is an emerging approach in peptide identifications from tandem mass spect...
Spectral library searching is an emerging approach in peptide identification from tandem mass (MS/MS...
Spectral library searching is an emerging approach in peptide identifications from tandem mass spect...
Spectral library searching is a new approach in proteomic data analysis that promises to address som...
A notable inefficiency of shotgun proteomics experiments is the repeated rediscovery of the same ide...
Spectral library searching has been recently proposed as an alternative to sequence database searchi...
In contemporary peptide-centric or non-gel proteome studies, vast amounts of peptide fragmentation d...
Introduction There exists a rich ecosystem of open-source MS software. Compared to vendor software ...
Proteomics is being transformed by deep learning methods that predict peptide fragmentation spectra....
Controlled vocabularies (CVs), i.e. a collection of predefined terms describing a modeling domain, u...