Several algorithms and tools have been developed to (semi) automate the process of glycan identification by interpreting Mass Spectrometric data. However, each has limitations when annotating MSn data with thousands of MS spectra using uncurated public databases. Moreover, the existing tools are not designed to manage MSn data where n > 2. We propose a novel software package to automate the annotation of tandem MS data. This software consists of two major components. The first, is a free, semi-automated MSn data interpreter called the Glycomic Elucidation and Annotation Tool (GELATO). This tool extends and automates the functionality of existing open source projects, namely, GlycoWorkbench (GWB) and GlycomeDB. The second is a machine learning model called Smart Anotation Enhancement Graph (SAGE), which learns the behavior of glycoanalysts to select annotations generated by GELATO that emulate human interpretation of the spectra.
@article{arxiv.1512.08451,
title = {GELATO and SAGE: An Integrated Framework for MS Annotation},
author = {Khalifeh AlJadda and Rene Ranzinger and Melody Porterfield and Brent Weatherly and Mohammed Korayem and John A. Miller and Khaled Rasheed and Krys J. Kochut and William S. York},
journal= {arXiv preprint arXiv:1512.08451},
year = {2016}
}
Comments
To be submitted to Bioinformatics journal, Oxford press