title: MUMAL2: Improving sensitivity in shotgun proteomics using cost sensitive artificial neural networks and a threshold selector algorithm.
abstract: BACKGROUND
  This work presents a machine learning strategy to increase sensitivity in tandem mass spectrometry (MS/MS) data analysis for peptide/protein identification. MS/MS yields thousands of spectra in a single run which are then interpreted by software. Most of these computer programs use a protein database to match peptide sequences to the observed spectra. The peptide-spectrum matches (PSMs) must also be assessed by computational tools since manual evaluation is not practicable. The target-decoy database strategy is largely used for error estimation in PSM assessment. However, in general, that strategy does not account for sensitivity.
  
  
  RESULTS
  In a previous study, we proposed the method MUMAL that applies an artificial neural network to effectively generate a model to classify PSMs using decoy hits with increased sensitivity. Nevertheless, the present approach shows that the sensitivity can be further improved with the use of a cost matrix associated with the learning algorithm. We also demonstrate that using a threshold selector algorithm for probability adjustment leads to more coherent probability values assigned to the PSMs. Our new approach, termed MUMAL2, provides a two-fold contribution to shotgun proteomics. First, the increase in the number of correctly interpreted spectra in the peptide level augments the chance of identifying more proteins. Second, the more appropriate PSM probability values that are produced by the threshold selector algorithm impact the protein inference stage performed by programs that take probabilities into account, such as ProteinProphet. Our experiments demonstrate that MUMAL2 reached around 15% of improvement in sensitivity compared to the best current method. Furthermore, the area under the ROC curve obtained was 0.93, demonstrating that the probabilities generated by our model are in fact appropriate. Finally, Venn diagrams comparing MUMAL2 with the best current method show that the number of exclusive peptides found by our method was nearly 4-fold higher, which directly impacts the proteome coverage.
  
  
  CONCLUSIONS
  The inclusion of a cost matrix and a probability threshold selector algorithm to the learning task further improves the target-decoy database analysis for identifying peptides, which optimally contributes to the challenging task of protein level identification, resulting in a powerful computational tool for shotgun proteomics.
authors.affiliation: Department of Informatics, Universidade Federal de Viçosa, Viçosa, 36570-900, Brazil. fabio.cerqueira@ufv.br.
authors.forename: Fabio Ribeiro
authors.initials: FR
authors.lastname: Cerqueira
authors.affiliation: Department of Informatics, Universidade Federal de Viçosa, Viçosa, 36570-900, Brazil.|Department of Computing and Construction, Centro Federal de Educação Tecnológica de Minas Gerais, Rua 19 de Novembro, 121, Timóteo, 35180-008, Brazil.
authors.forename: Adilson Mendes
authors.initials: AM
authors.lastname: Ricardo
authors.affiliation: Department of Informatics, Universidade Federal de Viçosa, Viçosa, 36570-900, Brazil.|Department of Computer Science, University of Sheffield, Western Bank, S10 2TN, Sheffield, UK.
authors.forename: Alcione
authors.initials: A
authors.lastname: de Paiva Oliveira
authors.affiliation: Research and Product Development of Genoptix, a Novartis company, 2110 Rutherford Rd, Carlsbad, 92008, USA.
authors.forename: Armin
authors.initials: A
authors.lastname: Graber
authors.affiliation: Institute of Health Care Engineering with European Notified Body of Medical Devices, Graz University of Technology, Stremayrgasse 16/II, Graz, A-8010, Austria.
authors.forename: Christian
authors.initials: C
authors.lastname: Baumgartner
mesh_terms: D000465:Algorithms; D030562:Databases, Protein / Q000191:economics; D016571:Neural Networks, Computer; D010455:Peptides / Q000737:chemistry; D011336:Probability; D020543:Proteome / Q000737:chemistry; D040901:Proteomics / Q000191:economics / Q000379:methods*; D012984:Software; D053719:Tandem Mass Spectrometry / Q000379:methods
chemical_list: D010455:Peptides; D020543:Proteome
keywords: Artificial neural network; Cost sensitive classification; Data mining; Peptide/protein identification; Phosphoproteomics; Shotgun proteomics