Optimization of Spectral Library Size Improves DIA-MS Proteome Coverage

Ge, W.; Liang, X.; Zhang, F.; Xu, L.; Xiang, N.; Sun, R.; Liu, W.; Xue, Z.; Yi, X.; Wang, B.; Zhu, J.; Lu, C.; Zhan, X.; Chen, L.; Wu, Y.; Zheng, Z.; Gong, W.; Wu, Q.; Yu, J.; Ye, Z.; Teng, X.; Huang, S.; Zheng, S.; Liu, T.; Yuan, C.; Guo, T.

doi:10.1101/2020.11.24.395426

bioRxiv · 10.1101/2020.11.24.395426

Optimization of Spectral Library Size Improves DIA-MS Proteome Coverage

Ge, W.·Liang, X.·Zhang, F.·Xu, L.·Xiang, N.·Sun, R.·Liu, W.·Xue, Z.·Yi, X.·Wang, B.·Zhu, J.·Lu, C.·Zhan, X.·Chen, L.·Wu, Y.·Zheng, Z.·Gong, W.·Wu, Q.·Yu, J.·Ye, Z.·Teng, X.·Huang, S.·Zheng, S.·Liu, T.·Yuan, C.·Guo, T.

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Abstract

Efficient peptide and protein identification from data-independent acquisition mass spectrometric (DIA-MS) data typically rely on an experiment-specific spectral library with a suitable size. Here, we report a computational strategy for optimizing the spectral library for a specific DIA dataset based on a comprehensive spectral library, which is accomplished by a priori analysis of the DIA dataset. This strategy achieved up to 44.7% increase in peptide identification and 38.1% increase in protein identification in the test dataset of six colorectal tumor samples compared with the comprehensive pan-human library strategy. We further applied this strategy to 389 carcinoma samples from 15 tumor datasets and observed up to 39.2% increase in peptide identification and 19.0% increase in protein identification. In summary, we present a computational strategy for spectral library size optimization to achieve deeper proteome coverage of DIA-MS data.

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Ge, W., Liang, X., Zhang, F., Xu, L., Xiang, N., Sun, R., Liu, W., Xue, Z., Yi, X., Wang, B., Zhu, J., Lu, C., Zhan, X., Chen, L., Wu, Y., Zheng, Z., Gong, W., Wu, Q., Yu, J., Ye, Z., Teng, X., Huang, S., Zheng, S., Liu, T., Yuan, C., Guo, T.. 2020-11-25. Optimization of Spectral Library Size Improves DIA-MS Proteome Coverage. https://doi.org/10.1101/2020.11.24.395426

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