DIAFree enables untargeted open-search identification for Data-Independent Acquisition data
Xu, I.
Show abstract
As a reliable and high-throughput proteomics strategy, data-independent acquisition (DIA) has shown great potential for protein analysis. However, DIA also imposes stress on the data processing algorithm by generating complex multiplexed spectra. Traditionally, DIA data is processed using spectral libraries refined from experiment histories, which requires stable experiment conditions and additional runs. Furthermore, scientists still need to use library-free tools to generate spectral libraries from additional runs. To lessen those burdens, here we present DIAFree(https://github.com/xuesu/DIAFree), a library-free, tag-index-based software suite that enables both restrict search and open search on DIA data using the information of MS1 scans in a precursor-centric and spectrum-centric style. We validate the quality of detection by publicly available data. We further evaluate the quality of spectral libraries produced by DIAFree.
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- MealTime-MS: A Machine Learning-Guided Real-Time Mass SpectrometryAnalysis for Protein Identification and Efficient DynamicExclusion 96%
- IS-PRM-based peptide targeting informed by long-read sequencing for alternative proteome detection 96%
- Machine learning strategies to tackle data challenges in mass spectrometry-based proteomics 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.