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bioRxiv · 10.1101/2023.06.02.543441

Benchmarking DIA data analysis workflows

Staes, A.·Maia, T. M.·Dufour, S.·Bouwmeester, R.·Gabriels, R.·Martens, L.·Impens, F.·Devos, S.
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Abstract

Data independent acquisition (DIA) has become a well-established method in LC-MS driven proteomics. Nonetheless, there are still a lot of possibilities at the data analysis level. By benchmarking different DIA analysis workflows using a ground-truth sample, consisting of a differential spike-in of UPS2 in a constant yeast background, we provide a roadmap for DIA data analysis of shotgun samples based on whether sensitivity, precision or accuracy is of the essence. Three different commonly used DIA software tools (DIA-NN, EncyclopeDIA and SpectronautTM) were tested in both spectral library mode and spectral library-free mode. In spectral library mode we used the independent spectral library prediction tools PROSIT and MS2PIP together with DeepLC, next to the classical DDA-based spectral libraries. In total we benchmarked 12 DIA workflows. DIA-NN in library-free mode or using in silico predicted libraries, together with Spectronaut in library-free mode, shows the highest sensitivity maintaining a high reproducibility and accuracy. In general, DIA-NN shows the best reproducibility, while the accuracy is comparable for all DIA workflows.

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Staes, A., Maia, T. M., Dufour, S., Bouwmeester, R., Gabriels, R., Martens, L., Impens, F., Devos, S.. 2023-06-06. Benchmarking DIA data analysis workflows. https://doi.org/10.1101/2023.06.02.543441

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