Application of network smoothing to glycan LC-MS profiling

Abstract Motivation Glycosylation is one of the most heterogeneous and complex protein post-translational modifications. Liquid chromatography coupled mass spectrometry (LC-MS) is a common high throughput method for analyzing complex biological samples. Accurate study of glycans require high resolut...

Full description

Saved in:
Bibliographic Details
Published in:Bioinformatics Vol. 34; no. 20; pp. 3511 - 3518
Main Authors: Klein, Joshua, Carvalho, Luis, Zaia, Joseph
Format: Journal Article
Language:English
Published: England Oxford University Press 15.10.2018
Subjects:
Algorithms
Chromatography, Liquid - methods
Glycomics - methods
Humans
Original Papers
Polysaccharides - chemistry
Software
Tandem Mass Spectrometry - methods
ISSN:1367-4803, 1367-4811, 1460-2059, 1367-4811
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Abstract Motivation Glycosylation is one of the most heterogeneous and complex protein post-translational modifications. Liquid chromatography coupled mass spectrometry (LC-MS) is a common high throughput method for analyzing complex biological samples. Accurate study of glycans require high resolution mass spectrometry. Mass spectrometry data contains intricate sub-structures that encode mass and abundance, requiring several transformations before it can be used to identify biological molecules, requiring automated tools to analyze samples in a high throughput setting. Existing tools for interpreting the resulting data do not take into account related glycans when evaluating individual observations, limiting their sensitivity. Results We developed an algorithm for assigning glycan compositions from LC-MS data by exploring biosynthetic network relationships among glycans. Our algorithm optimizes a set of likelihood scoring functions based on glycan chemical properties but uses network Laplacian regularization and optionally prior information about expected glycan families to smooth the likelihood and thus achieve a consistent and more representative solution. Our method was able to identify as many, or more glycan compositions compared to previous approaches, and demonstrated greater sensitivity with regularization. Our network definition was tailored to N-glycans but the method may be applied to glycomics data from other glycan families like O-glycans or heparan sulfate where the relationships between compositions can be expressed as a graph. Availability and implementation Built Executable http://www.bumc.bu.edu/msr/glycresoft/ and Source Code: https://github.com/BostonUniversityCBMS/glycresoft. Supplementary information Supplementary data are available at Bioinformatics online.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1367-4803
1367-4811
1460-2059
1367-4811
DOI:10.1093/bioinformatics/bty397