title: MUMAL: multivariate analysis in shotgun proteomics using machine learning techniques.
abstract: BACKGROUND
  The shotgun strategy (liquid chromatography coupled with tandem mass spectrometry) is widely applied for identification of proteins in complex mixtures. This method gives rise to thousands of spectra in a single run, which are interpreted by computational tools. Such tools normally use a protein database from which peptide sequences are extracted for matching with experimentally derived mass spectral data. After the database search, the correctness of obtained peptide-spectrum matches (PSMs) needs to be evaluated also by algorithms, as a manual curation of these huge datasets would be impractical. The target-decoy database strategy is largely used to perform spectrum evaluation. Nonetheless, this method has been applied without considering sensitivity, i.e., only error estimation is taken into account. A recently proposed method termed MUDE treats the target-decoy analysis as an optimization problem, where sensitivity is maximized. This method demonstrates a significant increase in the retrieved number of PSMs for a fixed error rate. However, the MUDE model is constructed in such a way that linear decision boundaries are established to separate correct from incorrect PSMs. Besides, the described heuristic for solving the optimization problem has to be executed many times to achieve a significant augmentation in sensitivity.
  
  
  RESULTS
  Here, we propose a new method, termed MUMAL, for PSM assessment that is based on machine learning techniques. Our method can establish nonlinear decision boundaries, leading to a higher chance to retrieve more true positives. Furthermore, we need few iterations to achieve high sensitivities, strikingly shortening the running time of the whole process. Experiments show that our method achieves a considerably higher number of PSMs compared with standard tools such as MUDE, PeptideProphet, and typical target-decoy approaches.
  
  
  CONCLUSIONS
  Our approach not only enhances the computational performance, and thus the turn around time of MS-based experiments in proteomics, but also improves the information content with benefits of a higher proteome coverage. This improvement, for instance, increases the chance to identify important drug targets or biomarkers for drug development or molecular diagnostics.
authors.affiliation: Department of Informatics, Federal University of Viçosa, 36570-000 Minas Geras, Brazil. fabio.cerqueira@ufv.br
authors.forename: Fabio R
authors.initials: FR
authors.lastname: Cerqueira
authors.forename: Ricardo S
authors.initials: RS
authors.lastname: Ferreira
authors.forename: Alcione P
authors.initials: AP
authors.lastname: Oliveira
authors.forename: Andreia P
authors.initials: AP
authors.lastname: Gomes
authors.forename: Humberto J O
authors.initials: HJ
authors.lastname: Ramos
authors.forename: Armin
authors.initials: A
authors.lastname: Graber
authors.forename: Christian
authors.initials: C
authors.lastname: Baumgartner
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002853:Chromatography, Liquid / Q000379:methods*; D019295:Computational Biology / Q000379:methods*; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D011506:Proteins / Q000032:analysis*; D040901:Proteomics / Q000379:methods*; D012680:Sensitivity and Specificity; D053719:Tandem Mass Spectrometry / Q000379:methods*
chemical_list: D011506:Proteins