title: Genome-wide prediction of prokaryotic two-component system networks using a sequence-based meta-predictor.
abstract: BACKGROUND
  Two component systems (TCS) are signalling complexes manifested by a histidine kinase (receptor) and a response regulator (effector). They are the most abundant signalling pathways in prokaryotes and control a wide range of biological processes. The pairing of these two components is highly specific, often requiring costly and time-consuming experimental characterisation. Therefore, there is considerable interest in developing accurate prediction tools to lessen the burden of experimental work and cope with the ever-increasing amount of genomic information.
  
  
  RESULTS
  We present a novel meta-predictor, MetaPred2CS, which is based on a support vector machine. MetaPred2CS integrates six sequence-based prediction methods: in-silico two-hybrid, mirror-tree, gene fusion, phylogenetic profiling, gene neighbourhood, and gene operon. To benchmark MetaPred2CS, we also compiled a novel high-quality training dataset of experimentally deduced TCS protein pairs for k-fold cross validation, to act as a gold standard for TCS partnership predictions. Combining individual predictions using MetaPred2CS improved performance when compared to the individual methods and in comparison with a current state-of-the-art meta-predictor.
  
  
  CONCLUSIONS
  We have developed MetaPred2CS, a support vector machine-based metapredictor for prokaryotic TCS protein pairings. Central to the success of MetaPred2CS is a strategy of integrating individual predictors that improves the overall prediction accuracy, with the in-silico two-hybrid method contributing most to performance. MetaPred2CS outperformed other available systems in our benchmark tests, and is available online at http://metapred2cs.ibers.aber.ac.uk, along with our gold standard dataset of TCS interaction pairs.
authors.affiliation: Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, SY23 3EB, UK. karaltn@gmail.com.
authors.forename: Altan
authors.initials: A
authors.lastname: Kara
authors.affiliation: Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, SY23 3EB, UK. mjv08@aber.ac.uk.
authors.forename: Martin
authors.initials: M
authors.lastname: Vickers
authors.affiliation: Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, SY23 3EB, UK. mts11@aber.ac.uk.
authors.forename: Martin
authors.initials: M
authors.lastname: Swain
authors.affiliation: Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, SY23 3EB, UK. dew@aber.ac.uk.
authors.forename: David E
authors.initials: DE
authors.lastname: Whitworth
authors.affiliation: Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, SY23 3EB, UK. narcis.fernandez@gmail.com.
authors.forename: Narcis
authors.initials: N
authors.lastname: Fernandez-Fuentes
mesh_terms: D019540:Area Under Curve; D001419:Bacteria / Q000235:genetics; D001426:Bacterial Proteins / Q000737:chemistry / Q000378:metabolism; D016680:Genome, Bacterial; D000071677:Histidine Kinase; D060066:Protein Interaction Maps; D011494:Protein Kinases / Q000737:chemistry / Q000378:metabolism; D012372:ROC Curve; D060388:Support Vector Machine
chemical_list: D001426:Bacterial Proteins; D011494:Protein Kinases; D000071677:Histidine Kinase