SWATH/DIA mass spectrometry enables generation of proteomics data sets at increasing pace,leading to a series of computational challenges,which are currently partially relieved by improving the computational pipelines developed initially for shotgun and SRM/MRM data sets.On the other hand,computer science is rapidly progressing.Data science and machine learning are advancing social,astronomical,and biomedical sciences.Data sets including microscopic images and recently genomic data are being analyzed by advanced computational approaches.To date,application of advanced computer science technologies to proteomics data analysis is still sparse,if any.Here,we try to build a bridge that translates the ten most pressing computational challenges of SWATH/DIA-based proteomics into computer science language and provide benchmark data sets to promote the advance of both fields.