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

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.