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However more accurate methods are needed for a reliable annotation of whole proteomes.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>Here we present PredGPI, a prediction method that, by coupling a Hidden Markov Model (HMM) and a Support Vector Machine (SVM), is able to efficiently predict both the presence of the GPI-anchor and the position of the \u03c9-site. PredGPI is trained on a non-redundant dataset of experimentally characterized GPI-anchored proteins whose annotation was carefully checked in the literature.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>PredGPI outperforms all the other previously described methods and is able to correctly replicate the results of previously published high-throughput experiments. PredGPI reaches a lower rate of false positive predictions with respect to other available methods and it is therefore a costless, rapid and accurate method for screening whole proteomes.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-9-392","type":"journal-article","created":{"date-parts":[[2008,9,23]],"date-time":"2008-09-23T18:13:57Z","timestamp":1222193637000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":561,"title":["PredGPI: a GPI-anchor predictor"],"prefix":"10.1186","volume":"9","author":[{"given":"Andrea","family":"Pierleoni","sequence":"first","affiliation":[]},{"given":"Pier Luigi","family":"Martelli","sequence":"additional","affiliation":[]},{"given":"Rita","family":"Casadio","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2008,9,23]]},"reference":[{"key":"2377_CR1","doi-asserted-by":"publisher","first-page":"993","DOI":"10.1194\/jlr.R700002-JLR200","volume":"48","author":"P Orlean","year":"2007","unstructured":"Orlean P, Menon AK: Thematic review series: lipid posttranslational modifications. 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