title: An adaptive workflow coupled with Random Forest algorithm to identify intact N-glycopeptides detected from mass spectrometry.
abstract: BACKGROUND
  Despite many attempts for algorithm development in recent years, automated identification of intact glycopeptides from LC-MS(2) spectral data is still a challenge in both sensitivity and precision.
  
  
  RESULTS
  We implemented a supervised machine learning algorithm, Random Forest, in an automated workflow to identify N-glycopeptides using spectral features derived from ion trap-based LC-MS(2) data. The workflow streamlined high-confident N-glycopeptide spectral data and enabled adaptive model optimization with respect to different sampling strategies, training sample size and feature set. A critical evaluation of the features important for glycopeptide identification further facilitated effective feature selection for model improvement. Using split sample testing method from 577 high-confident N-glycopeptide spectral data, we demonstrated that an optimal true-positive rate, precision and false-positive rate of 73, 88 and 10%, respectively, can be attained for overall N-glycopeptide identification Availability and implementation: The workflow developed in this work and the application suite, Sweet-Heart, that the workflow supports for N-glycopeptide identification are available for download at http://sweet-heart.glycoproteomics.proteome.bc.sinica.edu.tw/.
authors.affiliation: Core Facilities for Protein Structural Analysis at Institute of Biological Chemistry and Institute of Biological Chemistry, Academia Sinica, Taipei 115, Taiwan.
authors.forename: Suh-Yuen
authors.initials: SY
authors.lastname: Liang
authors.affiliation: Core Facilities for Protein Structural Analysis at Institute of Biological Chemistry and Institute of Biological Chemistry, Academia Sinica, Taipei 115, Taiwan.
authors.forename: Sz-Wei
authors.initials: SW
authors.lastname: Wu
authors.affiliation: Core Facilities for Protein Structural Analysis at Institute of Biological Chemistry and Institute of Biological Chemistry, Academia Sinica, Taipei 115, Taiwan.
authors.forename: Tsung-Hsien
authors.initials: TH
authors.lastname: Pu
authors.affiliation: Core Facilities for Protein Structural Analysis at Institute of Biological Chemistry and Institute of Biological Chemistry, Academia Sinica, Taipei 115, Taiwan.
authors.forename: Fang-Yu
authors.initials: FY
authors.lastname: Chang
authors.affiliation: Core Facilities for Protein Structural Analysis at Institute of Biological Chemistry and Institute of Biological Chemistry, Academia Sinica, Taipei 115, Taiwan.
authors.forename: Kay-Hooi
authors.initials: KH
authors.lastname: Khoo
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D002851:Chromatography, High Pressure Liquid / Q000379:methods; D006020:Glycopeptides / Q000032:analysis* / Q000737:chemistry; D018258:Herpesvirus 2, Human / Q000737:chemistry; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D051379:Mice; D057188:Workflow
chemical_list: D006020:Glycopeptides