title: Reliability of transmembrane predictions in whole-genome data.
abstract: Transmembrane prediction methods are generally benchmarked on a set of proteins with experimentally verified topology. We have investigated if the accuracy measured on such datasets can be expected in an unbiased genomic analysis, or if there is a bias towards 'easily predictable' proteins in the benchmark datasets. As a measurement of accuracy, the concordance of the results from five different prediction methods was used (TMHMM, PHD, HMMTOP, MEMSAT, and TOPPRED). The benchmark dataset showed significantly higher levels (up to five times) of agreement between different methods than in 10 tested genomes. We have also analyzed which programs are most prone to make mispredictions by measuring the frequency of one-out-of-five disagreeing predictions.
authors.affiliation: Center for Genomics and Bioinformatics, Karolinska Institutet, 17177, Stockholm, Sweden.
authors.forename: Lukas
authors.initials: L
authors.lastname: Käll
authors.forename: Erik L L
authors.initials: EL
authors.lastname: Sonnhammer
mesh_terms: D000465:Algorithms; D000818:Animals; D019295:Computational Biology; D003198:Computer Simulation; D019992:Databases as Topic; D004926:Escherichia coli / Q000235:genetics; D016678:Genome; D006801:Humans; D008565:Membrane Proteins / Q000737:chemistry*; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*; D020543:Proteome; D015203:Reproducibility of Results; D012984:Software
chemical_list: D008565:Membrane Proteins; D011506:Proteins; D020543:Proteome