MOTIVATION: The goal of deciphering the human glycome has been hindered by the lack of high-throughput sequencing methods for glycans. Although mass spectrometry (MS) is a key technology in glycan sequencing, MS alone provides limited information about the identification of monosaccharide constituents, their anomericity and their linkages. These features of individual, purified glycans can be partly identified using well-defined glycan-binding proteins, such as lectins and antibodies that recognize specific determinants within glycan structures. RESULTS: We present a novel computational approach to automate the sequencing of glycans using metadata-assisted glycan sequencing, which combines MS analyses with glycan structural information from glycan microarray technology. Success in this approach was aided by the generation of a 'virtual glycome' to represent all potential glycan structures that might exist within a metaglycomes based on a set of biosynthetic assumptions using known structural information. We exploited this approach to deduce the structures of soluble glycans within the human milk glycome by matching predicted structures based on experimental data against the virtual glycome. This represents the first meta-glycome to be defined using this method and we provide a publically available web-based application to aid in sequencing milk glycans. AVAILABILITY AND IMPLEMENTATION: http://glycomeseq.emory.edu CONTACT: sagravat@bidmc.harvard.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: The goal of deciphering the human glycome has been hindered by the lack of high-throughput sequencing methods for glycans. Although mass spectrometry (MS) is a key technology in glycan sequencing, MS alone provides limited information about the identification of monosaccharide constituents, their anomericity and their linkages. These features of individual, purified glycans can be partly identified using well-defined glycan-binding proteins, such as lectins and antibodies that recognize specific determinants within glycan structures. RESULTS: We present a novel computational approach to automate the sequencing of glycans using metadata-assisted glycan sequencing, which combines MS analyses with glycan structural information from glycan microarray technology. Success in this approach was aided by the generation of a 'virtual glycome' to represent all potential glycan structures that might exist within a metaglycomes based on a set of biosynthetic assumptions using known structural information. We exploited this approach to deduce the structures of soluble glycans within the human milk glycome by matching predicted structures based on experimental data against the virtual glycome. This represents the first meta-glycome to be defined using this method and we provide a publically available web-based application to aid in sequencing milk glycans. AVAILABILITY AND IMPLEMENTATION: http://glycomeseq.emory.edu CONTACT: sagravat@bidmc.harvard.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: David J Ashline; Ying Yu; Yi Lasanajak; Xuezheng Song; Liya Hu; Sasirekha Ramani; Venkataram Prasad; Mary K Estes; Richard D Cummings; David F Smith; Vernon N Reinhold Journal: Mol Cell Proteomics Date: 2014-07-21 Impact factor: 5.911
Authors: Matthew P Campbell; Robyn Peterson; Julien Mariethoz; Elisabeth Gasteiger; Yukie Akune; Kiyoko F Aoki-Kinoshita; Frederique Lisacek; Nicolle H Packer Journal: Nucleic Acids Res Date: 2013-11-13 Impact factor: 16.971
Authors: Alexander J Noll; Ying Yu; Yi Lasanajak; Geralyn Duska-McEwen; Rachael H Buck; David F Smith; Richard D Cummings Journal: Biochem J Date: 2016-03-14 Impact factor: 3.857
Authors: Benjamin P Kellman; Anne Richelle; Jeong-Yeh Yang; Digantkumar Chapla; Austin W T Chiang; Julia A Najera; Chenguang Liang; Annalee Fürst; Bokan Bao; Natalia Koga; Mahmoud A Mohammad; Anders Bech Bruntse; Morey W Haymond; Kelley W Moremen; Lars Bode; Nathan E Lewis Journal: Nat Commun Date: 2022-05-04 Impact factor: 17.694
Authors: Benjamin P Kellman; Yujie Zhang; Emma Logomasini; Eric Meinhardt; Karla P Godinez-Macias; Austin W T Chiang; James T Sorrentino; Chenguang Liang; Bokan Bao; Yusen Zhou; Sachiko Akase; Isami Sogabe; Thukaa Kouka; Elizabeth A Winzeler; Iain B H Wilson; Matthew P Campbell; Sriram Neelamegham; Frederick J Krambeck; Kiyoko F Aoki-Kinoshita; Nathan E Lewis Journal: Beilstein J Org Chem Date: 2020-10-27 Impact factor: 2.883