Beta-DIA: Integrating learning-based and function-based feature scores to optimize the proteome profiling of single-shot diaPASEF mass spectrometry data

Song, J.; Liu, H.; Shen, C.; Wu, X.

doi:10.1101/2024.11.19.624419

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bioRxiv · 10.1101/2024.11.19.624419

Beta-DIA: Integrating learning-based and function-based feature scores to optimize the proteome profiling of single-shot diaPASEF mass spectrometry data

Song, J.·Liu, H.·Shen, C.·Wu, X.

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Abstract

We present a freely available diaPASEF data analysis software, Beta-DIA, that utilizes deep learning methods to score coelution consistency in retention time-ion mobility dimensions and spectrum similarity. Beta-DIA integrates these learning-based scores with traditional function-based scores, enhancing the qualitative analysis performance. In some low detection datasets, Beta-DIA identifies twice as many protein groups as DIA-NN. The success of Beta-DIA has paved another way for the application of deep learning in fundamental proteome profiling.

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Song, J., Liu, H., Shen, C., Wu, X.. 2024-11-21. Beta-DIA: Integrating learning-based and function-based feature scores to optimize the proteome profiling of single-shot diaPASEF mass spectrometry data. https://doi.org/10.1101/2024.11.19.624419

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