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Glycoforest 1.0.

Journal article
Authors Oliver Horlacher
Chunsheng Jin
Davide Alocci
Julien Mariethoz
Markus Müller
Niclas G. Karlsson
Frederique Lisacek
Published in Analytical chemistry
Volume 89
Issue 20
Pages 10932-10940
ISSN 1520-6882
Publication year 2017
Published at Institute of Biomedicine, Department of Medical Biochemistry and Cell Biology
Pages 10932-10940
Language en
Links dx.doi.org/10.1021/acs.analchem.7b0...
www.ncbi.nlm.nih.gov/entrez/query.f...
Subject categories Bioinformatics (Computational Biology), Biochemistry and Molecular Biology

Abstract

Tandem mass spectrometry, when combined with liquid chromatography and applied to complex mixtures, produces large amounts of raw data, which needs to be analyzed to identify molecular structures. This technique is widely used, particularly in glycomics. Due to a lack of high throughput glycan sequencing software, glycan spectra are predominantly sequenced manually. A challenge for writing glycan-sequencing software is that there is no direct template that can be used to infer structures detectable in an organism. To help alleviate this bottleneck, we present Glycoforest 1.0, a partial de novo algorithm for sequencing glycan structures based on MS/MS spectra. Glycoforest was tested on two data sets (human gastric and salmon mucosa O-linked glycomes) for which MS/MS spectra were annotated manually. Glycoforest generated the human validated structure for 92% of test cases. The correct structure was found as the best scoring match for 70% and among the top 3 matches for 83% of test cases. In addition, the Glycoforest algorithm detected glycan structures from MS/MS spectra missing a manual annotation. In total 1532 MS/MS previously unannotated spectra were annotated by Glycoforest. A portion containing 521 spectra was manually checked confirming that Glycoforest annotated an additional 50 MS/MS spectra overlooked during manual annotation.

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