Generating high quality libraries for DIA MS with empirically corrected peptide predictions
Data-independent acquisition-mass spectrometry (MS) typically requires many preparatory MS runs to produce experiment-specific spectral libraries. Here, the authors show that empirical correction of in silico predicted spectral libraries enables efficient generation of high-quality experiment-specif...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2020-03-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-020-15346-1 |
| Summary: | Data-independent acquisition-mass spectrometry (MS) typically requires many preparatory MS runs to produce experiment-specific spectral libraries. Here, the authors show that empirical correction of in silico predicted spectral libraries enables efficient generation of high-quality experiment-specific libraries. |
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| ISSN: | 2041-1723 |