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https://doi.org/10.1016/j.dib.2022.107919
Title: | Proteomic datasets of HeLa and SiHa cell lines acquired by DDA-PASEF and diaPASEF | Authors: | Huang, Zelu Kong, Weijia Wong, Bertrand Jernhan Gao, Huanhuan Guo, Tiannan Liu, Xianming Du, Xiaoxian Wong, Limsoon Bin Goh, Wilson Wen |
Keywords: | Science & Technology Multidisciplinary Sciences Science & Technology - Other Topics DDA DIA PASEF HeLa SiHa |
Issue Date: | Apr-2022 | Publisher: | ELSEVIER | Citation: | Huang, Zelu, Kong, Weijia, Wong, Bertrand Jernhan, Gao, Huanhuan, Guo, Tiannan, Liu, Xianming, Du, Xiaoxian, Wong, Limsoon, Bin Goh, Wilson Wen (2022-04). Proteomic datasets of HeLa and SiHa cell lines acquired by DDA-PASEF and diaPASEF. DATA IN BRIEF 41. ScholarBank@NUS Repository. https://doi.org/10.1016/j.dib.2022.107919 | Abstract: | We present four datasets on proteomics profiling of HeLa and SiHa cell lines associated with the research described in the paper “PROTREC: A probability-based approach for recovering missing proteins based on biological networks” [1]. Proteins in each cell line were acquired by two different data acquisition methods. The first was Data Dependent Acquisition-Parallel Accumulation Serial Fragmentation (DDA-PASEF) and the second was Parallel Accumulation-Serial Fragmentation combined with data-independent acquisition (diaPASEF) [2,3]. Protein assembly was performed following search against the Swiss-Prot Human database using Peaks Studio for DDA datasets and Spectronaut for DIA datasets. The assembled result contains identified PSMs, peptides and proteins that are above threshold for each HeLa and SiHa sample. Coverage-wise, for DDA-PASEF, approximately 6,090 and 7,298 proteins were quantified for HeLa and SiHA sample, while13,339 and 8,773 proteins were quantified by diaPASEF for HeLa for SiHa sample, respectively. Consistency-wise, diaPASEF has fewer missing values (∼ 2%) compared to its DDA counterparts (∼5–7%). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the iProX partner repository [4] with the dataset identifier PXD029773. | Source Title: | DATA IN BRIEF | URI: | https://scholarbank.nus.edu.sg/handle/10635/241789 | ISSN: | 2352-3409 | DOI: | 10.1016/j.dib.2022.107919 |
Appears in Collections: | Staff Publications Elements |
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