Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Trackable and scalable LC-MS metabolomics data processing using asari. |
| Συγγραφείς: |
Li, Shuzhao, Siddiqa, Amnah, Thapa, Maheshwor, Chi, Yuanye, Zheng, Shujian |
| Πηγή: |
Nature Communications; 7/11/2023, Vol. 14 Issue 1, p1-12, 12p |
| Θεματικοί όροι: |
Metabolomics, Software development tools, Structural frames, Quality control |
| Εταιρία/Οντότητα: |
Microsoft Corp. |
| Περίληψη: |
Significant challenges remain in the computational processing of data from liquid chomratography-mass spectrometry (LC-MS)-based metabolomic experiments into metabolite features. In this study, we examine the issues of provenance and reproducibility using the current software tools. Inconsistency among the tools examined is attributed to the deficiencies of mass alignment and controls of feature quality. To address these issues, we develop the open-source software tool asari for LC-MS metabolomics data processing. Asari is designed with a set of specific algorithmic framework and data structures, and all steps are explicitly trackable. Asari compares favorably to other tools in feature detection and quantification. It offers substantial improvement in computational performance over current tools, and it is highly scalable. Reproducible and scalable data processing is key to the progress of metabolomics. Here, the authors present a software tool that offers predictable metabolomics feature detection and improved computational performance in large datasets. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: |
Complementary Index |