ArticleBMC bioinformatics2007
'Unite and conquer': enhanced prediction of protein subcellular localization by integrating multiple specialized tools.
Article in BMC bioinformatics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 40 citations in OpenAlex.
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- PSI: a comprehensive and integrative approach for accurate plant subcellular localization prediction.PloS one · 2013Article
- Minimalist ensemble algorithms for genome-wide protein localization prediction.BMC bioinformatics · 2012Article
- Phylogenomic study of lipid genes involved in microalgal biofuel production-candidate gene mining and metabolic pathway analyses.Evolutionary bioinformatics online · 2012Article
- Learning cellular sorting pathways using protein interactions and sequence motifs.Journal of computational biology : a journal of computational molecular cell biology · 2011Article
- The use of classification trees for bioinformatics.Wiley interdisciplinary reviews. Data mining and knowledge discovery · 2011Article
- ClubSub-P: Cluster-Based Subcellular Localization Prediction for Gram-Negative Bacteria and Archaea.Frontiers in microbiology · 2011Article
- TESTLoc: protein subcellular localization prediction from EST data.BMC bioinformatics · 2010Article
- CoBaltDB: Complete bacterial and archaeal orfeomes subcellular localization database and associated resources.BMC microbiology · 2010Article
- MultiLoc2: integrating phylogeny and Gene Ontology terms improves subcellular protein localization prediction.BMC bioinformatics · 2009Article
- Diversity and dispersal of a ubiquitous protein family: acyl-CoA dehydrogenases.Nucleic acids research · 2009Article
- Validating subcellular localization prediction tools with mycobacterial proteins.BMC bioinformatics · 2009Article
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2 authors at 1 institution in 1 country.
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Abstract
backgroundKnowing 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.
resultsIn 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.
conclusionWe 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.
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