https://scholars.lib.ntu.edu.tw/handle/123456789/631736
標題: | An automated workflow on data processing (AutoDP) for semiquantitative analysis of urine organic acids with GC-MS to facilitate diagnosis of inborn errors of metabolism | 作者: | Wang, San-Yuan TE-I WENG Chen, Ju-Yu NI-CHUNG LEE Lee, Kun-Chen Lai, Mei-Ling YIN-HSIU CHIEN WUH-LIANG HWU GUAN-YUAN CHEN |
關鍵字: | AutoDP; Automation; Data processing; GC-MS; Inborn errors of metabolism | 公開日期: | 1-二月-2023 | 出版社: | ELSEVIER | 卷: | 540 | 來源出版物: | Clinica chimica acta; international journal of clinical chemistry | 摘要: | Determination of urine organic acids (UOAs) is essential to understand the disease progress of inborn errors of metabolism (IEM) and often relies on GC-MS analysis. However, the efficiency of analytical reports is sometimes restricted by data processing due to labor-intensive work if no proper tool is employed. Herein, we present a simple and rapid workflow with an R-based script for automated data processing (AutoDP) of GC-MS raw files to quantitatively analyze essential UOAs. AutoDP features automatic quality checks, compound identification and confirmation with specific fragment ions, retention time correction from analytical batches, and visualization of abnormal UOAs with age-matched references on chromatograms. Compared with manual processing, AutoDP greatly reduces analytical time and increases the number of identifications. Speeding up data processing is expected to shorten the waiting time for clinical diagnosis, which could greatly benefit clinicians and patients with IEM. In addition, with quantitative results obtained from AutoDP, it would be more feasible to perform retrospective analysis of specific UOAs in IEM and could provide new perspectives for studying IEM. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/631736 | ISSN: | 00098981 | DOI: | 10.1016/j.cca.2023.117230 |
顯示於: | 法醫學科所 |
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