Proteomics

Dataset Information

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Development of a Robust Score and False Discovery Rate for Metabolite Identification

ABSTRACT: We used machine learning (ML) to improve metabolite identification confidence in GC-MS data. This project was funded by PNNL's M/Q initiative. All data was collected on an Agilent GC 7890A coupled with a single quadrupole MSD 5975C. Metabolites were derivatized prior to analysis.

INSTRUMENT(S): Agilent 7890A GC with 5975C inert XL MSD

ORGANISM(S): Trichoderma Reesei (ncbitaxon:51453) Aspergillus (ncbitaxon:5052) Homo Sapiens (ncbitaxon:9606) Aspergillus Nidulans (ncbitaxon:162425)

SUBMITTER: Chaevien Clendinen  

PROVIDER: MSV000089933 | MassIVE | Wed Jul 20 23:51:00 BST 2022

REPOSITORIES: MassIVE

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Publications

Evaluating Retention Index Score Assumptions to Refine GC-MS Metabolite Identification.

Degnan David J DJ   Bramer Lisa M LM   Flores Javier E JE   Paurus Vanessa L VL   Corilo Yuri E YE   Clendinen Chaevien S CS  

Analytical chemistry 20230502 19

As metabolomics grows into a high-throughput and high demand research field, current metrics for the identification of small molecules in gas chromatography-mass spectrometry (GC-MS) still require manual verification. Though steps have been taken to improve scoring metrics by combining spectral similarity (SS) and retention index (RI), the problem persists. A large body of literature has analyzed and refined SS scores, but few studies have explicitly studied improvements to RI scores. Here, we e  ...[more]

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