title: 'Unite and conquer': enhanced prediction of protein subcellular localization by integrating multiple specialized tools.
abstract: BACKGROUND
  Knowing the subcellular location of proteins provides clues to their function as well as the interconnectivity of biological processes. Dozens of tools are available for predicting protein location in the eukaryotic cell. Each tool performs well on certain data sets, but their predictions often disagree for a given protein. Since the individual tools each have particular strengths, we set out to integrate them in a way that optimally exploits their potential. The method we present here is applicable to various subcellular locations, but tailored for predicting whether or not a protein is localized in mitochondria. Knowledge of the mitochondrial proteome is relevant to understanding the role of this organelle in global cellular processes.
  
  
  RESULTS
  In order to develop a method for enhanced prediction of subcellular localization, we integrated the outputs of available localization prediction tools by several strategies, and tested the performance of each strategy with known mitochondrial proteins. The accuracy obtained (up to 92%) surpasses by far the individual tools. The method of integration proved crucial to the performance. For the prediction of mitochondrion-located proteins, integration via a two-layer decision tree clearly outperforms simpler methods, as it allows emphasis of biologically relevant features such as the mitochondrial targeting peptide and transmembrane domains.
  
  
  CONCLUSIONS
  We developed an approach that enhances the prediction accuracy of mitochondrial proteins by uniting the strength of specialized tools. The combination of machine-learning based integration with biological expert knowledge leads to improved performance. This approach also alleviates the conundrum of how to choose between conflicting predictions. Our approach is easy to implement, and applicable to predicting subcellular locations other than mitochondria, as well as other biological features. For a trial of our approach, we provide a webservice for mitochondrial protein prediction (named YimLOC), which can be accessed through the AnaBench suite at http://anabench.bcm.umontreal.ca/anabench/. The source code is provided in the Additional File 2.
authors.affiliation: Robert Cedergren Center for Bioinformatics and Genomics, Biochemistry Department, Université de Montréal, 2900 Edouard-Montpetit, Montreal, QC, H3T 1J4, Canada. yaoqing.shen@umontreal.ca
authors.forename: Yao Qing
authors.initials: YQ
authors.lastname: Shen
authors.forename: Gertraud
authors.initials: G
authors.lastname: Burger
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D029681:Arabidopsis Proteins / Q000737:chemistry; D019295:Computational Biology / Q000295:instrumentation* / Q000379:methods*; D030562:Databases, Protein; D005189:False Positive Reactions; D020407:Internet; D008928:Mitochondria / Q000378:metabolism; D010455:Peptides / Q000737:chemistry; D010802:Phylogeny; D017434:Protein Structure, Tertiary; D021381:Protein Transport; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein; D012984:Software
chemical_list: D029681:Arabidopsis Proteins; D010455:Peptides; D011506:Proteins