COUSCOus: improved protein contact prediction using an empirical Bayes covariance estimator
<h3>Background</h3><p dir="ltr">The post-genomic era with its wealth of sequences gave rise to a broad range of protein residue-residue contact detecting methods. Although various coevolution methods such as PSICOV, DCA and plmDCA provide correct contact predictions, they...
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| مؤلفون آخرون: | , , , , , |
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2016
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| _version_ | 1864513556544749568 |
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| author | Reda Rawi (391865) |
| author2 | Raghvendra Mall (581171) Khalid Kunji (828224) Mohammed El Anbari (767963) Michael Aupetit (3582545) Ehsan Ullah (2698921) Halima Bensmail (10400) |
| author2_role | author author author author author author |
| author_facet | Reda Rawi (391865) Raghvendra Mall (581171) Khalid Kunji (828224) Mohammed El Anbari (767963) Michael Aupetit (3582545) Ehsan Ullah (2698921) Halima Bensmail (10400) |
| author_role | author |
| dc.creator.none.fl_str_mv | Reda Rawi (391865) Raghvendra Mall (581171) Khalid Kunji (828224) Mohammed El Anbari (767963) Michael Aupetit (3582545) Ehsan Ullah (2698921) Halima Bensmail (10400) |
| dc.date.none.fl_str_mv | 2016-12-15T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1186/s12859-016-1400-3 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/COUSCOus_improved_protein_contact_prediction_using_an_empirical_Bayes_covariance_estimator/27094528 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biological sciences Bioinformatics and computational biology Residue-residue contact prediction Shrinkage GLasso |
| dc.title.none.fl_str_mv | COUSCOus: improved protein contact prediction using an empirical Bayes covariance estimator |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Background</h3><p dir="ltr">The post-genomic era with its wealth of sequences gave rise to a broad range of protein residue-residue contact detecting methods. Although various coevolution methods such as PSICOV, DCA and plmDCA provide correct contact predictions, they do not completely overlap. Hence, new approaches and improvements of existing methods are needed to motivate further development and progress in the field. We present a new contact detecting method, COUSCOus, by combining the best shrinkage approach, the empirical Bayes covariance estimator and GLasso.</p><h3>Results</h3><p dir="ltr">Using the original PSICOV benchmark dataset, COUSCOus achieves mean accuracies of 0.74, 0.62 and 0.55 for the top L/10 predicted long, medium and short range contacts, respectively. In addition, COUSCOus attains mean areas under the precision-recall curves of 0.25, 0.29 and 0.30 for long, medium and short contacts and outperforms PSICOV. We also observed that COUSCOus outperforms PSICOV w.r.t. Matthew’s correlation coefficient criterion on full list of residue contacts. Furthermore, COUSCOus achieves on average 10% more gain in prediction accuracy compared to PSICOV on an independent test set composed of CASP11 protein targets. Finally, we showed that when using a simple random forest meta-classifier, by combining contact detecting techniques and sequence derived features, PSICOV predictions should be replaced by the more accurate COUSCOus predictions.</p><h3>Conclusion</h3><p dir="ltr">We conclude that the consideration of superior covariance shrinkage approaches will boost several research fields that apply the GLasso procedure, amongst the presented one of residue-residue contact prediction as well as fields such as gene network reconstruction.</p><h2>Other Information</h2><p dir="ltr">Published in: BMC Bioinformatics<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1186/s12859-016-1400-3" target="_blank">https://dx.doi.org/10.1186/s12859-016-1400-3</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_75b6e909fa4c1917426e37149e1b7b55 |
| identifier_str_mv | 10.1186/s12859-016-1400-3 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/27094528 |
| publishDate | 2016 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | COUSCOus: improved protein contact prediction using an empirical Bayes covariance estimatorReda Rawi (391865)Raghvendra Mall (581171)Khalid Kunji (828224)Mohammed El Anbari (767963)Michael Aupetit (3582545)Ehsan Ullah (2698921)Halima Bensmail (10400)Biological sciencesBioinformatics and computational biologyResidue-residue contact predictionShrinkageGLasso<h3>Background</h3><p dir="ltr">The post-genomic era with its wealth of sequences gave rise to a broad range of protein residue-residue contact detecting methods. Although various coevolution methods such as PSICOV, DCA and plmDCA provide correct contact predictions, they do not completely overlap. Hence, new approaches and improvements of existing methods are needed to motivate further development and progress in the field. We present a new contact detecting method, COUSCOus, by combining the best shrinkage approach, the empirical Bayes covariance estimator and GLasso.</p><h3>Results</h3><p dir="ltr">Using the original PSICOV benchmark dataset, COUSCOus achieves mean accuracies of 0.74, 0.62 and 0.55 for the top L/10 predicted long, medium and short range contacts, respectively. In addition, COUSCOus attains mean areas under the precision-recall curves of 0.25, 0.29 and 0.30 for long, medium and short contacts and outperforms PSICOV. We also observed that COUSCOus outperforms PSICOV w.r.t. Matthew’s correlation coefficient criterion on full list of residue contacts. Furthermore, COUSCOus achieves on average 10% more gain in prediction accuracy compared to PSICOV on an independent test set composed of CASP11 protein targets. Finally, we showed that when using a simple random forest meta-classifier, by combining contact detecting techniques and sequence derived features, PSICOV predictions should be replaced by the more accurate COUSCOus predictions.</p><h3>Conclusion</h3><p dir="ltr">We conclude that the consideration of superior covariance shrinkage approaches will boost several research fields that apply the GLasso procedure, amongst the presented one of residue-residue contact prediction as well as fields such as gene network reconstruction.</p><h2>Other Information</h2><p dir="ltr">Published in: BMC Bioinformatics<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1186/s12859-016-1400-3" target="_blank">https://dx.doi.org/10.1186/s12859-016-1400-3</a></p>2016-12-15T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1186/s12859-016-1400-3https://figshare.com/articles/journal_contribution/COUSCOus_improved_protein_contact_prediction_using_an_empirical_Bayes_covariance_estimator/27094528CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/270945282016-12-15T03:00:00Z |
| spellingShingle | COUSCOus: improved protein contact prediction using an empirical Bayes covariance estimator Reda Rawi (391865) Biological sciences Bioinformatics and computational biology Residue-residue contact prediction Shrinkage GLasso |
| status_str | publishedVersion |
| title | COUSCOus: improved protein contact prediction using an empirical Bayes covariance estimator |
| title_full | COUSCOus: improved protein contact prediction using an empirical Bayes covariance estimator |
| title_fullStr | COUSCOus: improved protein contact prediction using an empirical Bayes covariance estimator |
| title_full_unstemmed | COUSCOus: improved protein contact prediction using an empirical Bayes covariance estimator |
| title_short | COUSCOus: improved protein contact prediction using an empirical Bayes covariance estimator |
| title_sort | COUSCOus: improved protein contact prediction using an empirical Bayes covariance estimator |
| topic | Biological sciences Bioinformatics and computational biology Residue-residue contact prediction Shrinkage GLasso |