title: DiagnoProt: a tool for discovery of new molecules by mass spectrometry.
abstract: BACKGROUND
  Around 75% of all mass spectra remain unidentified by widely adopted proteomic strategies. We present DiagnoProt, an integrated computational environment that can efficiently cluster millions of spectra and use machine learning to shortlist high-quality unidentified mass spectra that are discriminative of different biological conditions.
  
  
  RESULTS
  We exemplify the use of DiagnoProt by shortlisting 4366 high-quality unidentified tandem mass spectra that are discriminative of different types of the Aspergillus fungus.
  
  
  METHODS
  DiagnoProt, a demonstration video and a user tutorial are available at http://patternlabforproteomics.org/diagnoprot .
  
  
  BACKGROUND
  andrerfsilva@gmail.com or paulo@pcarvalho.com.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Systems Engineering and Computer Science Program, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
authors.forename: André R F
authors.initials: ARF
authors.lastname: Silva
authors.affiliation: Institut Pasteur, Structural Mass Spectrometry and Proteomics Unit, Paris, France.
authors.forename: Diogo B
authors.initials: DB
authors.lastname: Lima
authors.affiliation: Analytical Biochemistry and Proteomics Unit, Institut Pasteur de Montevideo/IIBCE, Montevideo, Uruguay.
authors.forename: Alejandro
authors.initials: A
authors.lastname: Leyva
authors.affiliation: Analytical Biochemistry and Proteomics Unit, Institut Pasteur de Montevideo/IIBCE, Montevideo, Uruguay.
authors.forename: Rosario
authors.initials: R
authors.lastname: Duran
authors.affiliation: Analytical Biochemistry and Proteomics Unit, Institut Pasteur de Montevideo/IIBCE, Montevideo, Uruguay.
authors.forename: Carlos
authors.initials: C
authors.lastname: Batthyany
authors.affiliation: Leônidas and Maria Deane Institute, Fiocruz, Amazonas, Brazil.
authors.forename: Priscila F
authors.initials: PF
authors.lastname: Aquino
authors.affiliation: Leônidas and Maria Deane Institute, Fiocruz, Amazonas, Brazil.
authors.forename: Juliana C
authors.initials: JC
authors.lastname: Leal
authors.affiliation: Proteomics Unit, Chemistry Institute, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
authors.forename: Jimmy E
authors.initials: JE
authors.lastname: Rodriguez
authors.affiliation: Proteomics Unit, Chemistry Institute, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
authors.forename: Gilberto B
authors.initials: GB
authors.lastname: Domont
authors.affiliation: Carlos Chagas Institute, Computational Mass Spectrometry & Proteomics Group, Fiocruz, Paraná, Brazil.
authors.forename: Marlon D M
authors.initials: MDM
authors.lastname: Santos
authors.affiliation: Institut Pasteur, Structural Mass Spectrometry and Proteomics Unit, Paris, France.
authors.forename: Julia
authors.initials: J
authors.lastname: Chamot-Rooke
authors.affiliation: Systems Engineering and Computer Science Program, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
authors.forename: Valmir C
authors.initials: VC
authors.lastname: Barbosa
authors.affiliation: Carlos Chagas Institute, Computational Mass Spectrometry & Proteomics Group, Fiocruz, Paraná, Brazil.|Laboratory for Toxinology, Fiocruz, Rio de Janeiro, Brazil.
authors.forename: Paulo C
authors.initials: PC
authors.lastname: Carvalho
mesh_terms: D001230:Aspergillus / Q000378:metabolism; D005656:Fungal Proteins / Q000032:analysis; D000069550:Machine Learning; D040901:Proteomics / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software; D053719:Tandem Mass Spectrometry / Q000379:methods*
chemical_list: D005656:Fungal Proteins