| Autor: |
Rose, Bailey S, May, Jody C, Picache, Jaqueline A, Codreanu, Simona G, Sherrod, Stacy D, McLean, John A |
| Předmět: |
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| Zdroj: |
Bioinformatics; May2022, Vol. 38 Issue 10, p2872-2879, 8p |
| Abstrakt: |
Motivation Mass spectrometry-based untargeted lipidomics aims to globally characterize the lipids and lipid-like molecules in biological systems. Ion mobility increases coverage and confidence by offering an additional dimension of separation and a highly reproducible metric for feature annotation, the collision cross-section (CCS). Results We present a data processing workflow to increase confidence in molecular class annotations based on CCS values. This approach uses class-specific regression models built from a standardized CCS repository (the Unified CCS Compendium) in a parallel scheme that combines a new annotation filtering approach with a machine learning class prediction strategy. In a proof-of-concept study using murine brain lipid extracts, 883 lipids were assigned higher confidence identifications using the filtering approach, which reduced the tentative candidate lists by over 50% on average. An additional 192 unannotated compounds were assigned a predicted chemical class. Availability and implementation All relevant source code is available at https://github.com/McLeanResearchGroup/CCS-filter. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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