Sökning: WFRF:(Noble William Stafford) > Transmembrane topol...
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000 | 03176naa a2200361 4500 | |
001 | oai:DiVA.org:kth-48849 | |
003 | SwePub | |
008 | 111123s2008 | |||||||||||000 ||eng| | |
024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-488492 URI |
024 | 7 | a https://doi.org/10.1371/journal.pcbi.10002132 DOI |
040 | a (SwePub)kth | |
041 | a engb eng | |
042 | 9 SwePub | |
072 | 7 | a ref2 swepub-contenttype |
072 | 7 | a art2 swepub-publicationtype |
100 | 1 | a Reynolds, Sheila M.4 aut |
245 | 1 0 | a Transmembrane topology and signal peptide prediction using dynamic bayesian networks |
264 | c 2008-11-07 | |
264 | 1 | b Public Library of Science (PLoS),c 2008 |
338 | a print2 rdacarrier | |
500 | a QC 20111128 | |
520 | a Hidden Markov models (HMMs) have been successfully applied to the tasks of transmembrane protein topology prediction and signal peptide prediction. In this paper we expand upon this work by making use of the more powerful class of dynamic Bayesian networks (DBNs). Our model, Philius, is inspired by a previously published HMM, Phobius, and combines a signal peptide submodel with a transmembrane submodel. We introduce a two-stage DBN decoder that combines the power of posterior decoding with the grammar constraints of Viterbi-style decoding. Philius also provides protein type, segment, and topology confidence metrics to aid in the interpretation of the predictions. We report a relative improvement of 13% over Phobius in full-topology prediction accuracy on transmembrane proteins, and a sensitivity and specificity of 0.96 in detecting signal peptides. We also show that our confidence metrics correlate well with the observed precision. In addition, we have made predictions on all 6.3 million proteins in the Yeast Resource Center (YRC) database. This large-scale study provides an overall picture of the relative numbers of proteins that include a signal-peptide and/or one or more transmembrane segments as well as a valuable resource for the scientific community. All DBNs are implemented using the Graphical Models Toolkit. Source code for the models described here is available at http://noble.gs.washington.edu/proj/philius. A Philius Web server is available at http://www.yeastrc.org/philius, and the predictions on the YRC database are available at http://www.yeastrc.org/pdr. | |
650 | 7 | a NATURVETENSKAPx Data- och informationsvetenskapx Bioinformatik0 (SwePub)102032 hsv//swe |
650 | 7 | a NATURAL SCIENCESx Computer and Information Sciencesx Bioinformatics0 (SwePub)102032 hsv//eng |
700 | 1 | a Käll, Lukas,d 1969-u KTH,Genteknologi4 aut0 (Swepub:kth)u1gqsept |
700 | 1 | a Riffle, Michael E.4 aut |
700 | 1 | a Bilmes, Jeff A.4 aut |
700 | 1 | a Noble, William Stafford4 aut |
710 | 2 | a KTHb Genteknologi4 org |
773 | 0 | t PloS Computational Biologyd : Public Library of Science (PLoS)g 4:11, s. e1000213-q 4:11<e1000213-x 1553-734Xx 1553-7358 |
856 | 4 | u https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1000213&type=printable |
856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-48849 |
856 | 4 8 | u https://doi.org/10.1371/journal.pcbi.1000213 |
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