MetFID: artificial neural network-based compound fingerprint prediction for metabolite annotation.

ABSTRACT:

Introduction

Metabolite annotation is a critical and challenging step in mass spectrometry-based metabolomic profiling. In a typical untargeted MS/MS-based metabolomic study, experimental MS/MS spectra are matched against those in spectral libraries for metabolite annotation. Yet, existing spectral libraries comprise merely a marginal percentage of known compounds.

Objective

The objective is to develop a method that helps rank putative metabolite IDs for analytes whose reference MS/MS spectra are not present in spectral libraries.

Methods

We introduce MetFID, which uses an artificial neural network (ANN) trained for predicting molecular fingerprints based on experimental MS/MS data. To narrow the search space, MetFID retrieves candidates from metabolite databases usin

SUBMITTER: Fan Z 

PROVIDER: S-EPMC9547616 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature