Comparison of Predictive In Silico Tools on Missense Variants in GJB2, GJB6, and GJB3 Genes Associated with Autosomal Recessive Deafness 1A (DFNB1A)
In silico predictive software allows assessing the effect of amino acid substitutions on the structure or function of a protein without conducting functional studies. The accuracy of in silico pathogenicity prediction tools has not been previously assessed for variants associated with autosomal rece...
Saved in:
| Main Authors: | , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
|
| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2019/5198931 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832549297176969216 |
|---|---|
| author | Vera G. Pshennikova Nikolay A. Barashkov Georgii P. Romanov Fedor M. Teryutin Aisen V. Solov’ev Nyurgun N. Gotovtsev Alena A. Nikanorova Sergey S. Nakhodkin Nikolay N. Sazonov Igor V. Morozov Alexander A. Bondar Lilya U. Dzhemileva Elza K. Khusnutdinova Olga L. Posukh Sardana A. Fedorova |
| author_facet | Vera G. Pshennikova Nikolay A. Barashkov Georgii P. Romanov Fedor M. Teryutin Aisen V. Solov’ev Nyurgun N. Gotovtsev Alena A. Nikanorova Sergey S. Nakhodkin Nikolay N. Sazonov Igor V. Morozov Alexander A. Bondar Lilya U. Dzhemileva Elza K. Khusnutdinova Olga L. Posukh Sardana A. Fedorova |
| author_sort | Vera G. Pshennikova |
| collection | DOAJ |
| description | In silico predictive software allows assessing the effect of amino acid substitutions on the structure or function of a protein without conducting functional studies. The accuracy of in silico pathogenicity prediction tools has not been previously assessed for variants associated with autosomal recessive deafness 1A (DFNB1A). Here, we identify in silico tools with the most accurate clinical significance predictions for missense variants of the GJB2 (Cx26), GJB6 (Cx30), and GJB3 (Cx31) connexin genes associated with DFNB1A. To evaluate accuracy of selected in silico tools (SIFT, FATHMM, MutationAssessor, PolyPhen-2, CONDEL, MutationTaster, MutPred, Align GVGD, and PROVEAN), we tested nine missense variants with previously confirmed clinical significance in a large cohort of deaf patients and control groups from the Sakha Republic (Eastern Siberia, Russia): Сх26: p.Val27Ile, p.Met34Thr, p.Val37Ile, p.Leu90Pro, p.Glu114Gly, p.Thr123Asn, and p.Val153Ile; Cx30: p.Glu101Lys; Cx31: p.Ala194Thr. We compared the performance of the in silico tools (accuracy, sensitivity, and specificity) by using the missense variants in GJB2 (Cx26), GJB6 (Cx30), and GJB3 (Cx31) genes associated with DFNB1A. The correlation coefficient (r) and coefficient of the area under the Receiver Operating Characteristic (ROC) curve as alternative quality indicators of the tested programs were used. The resulting ROC curves demonstrated that the largest coefficient of the area under the curve was provided by three programs: SIFT (AUC = 0.833, p = 0.046), PROVEAN (AUC = 0.833, p = 0.046), and MutationAssessor (AUC = 0.833, p = 0.002). The most accurate predictions were given by two tested programs: SIFT and PROVEAN (Ac = 89%, Se = 67%, Sp = 100%, r = 0.75, AUC = 0.833). The results of this study may be applicable for analysis of novel missense variants of the GJB2 (Cx26), GJB6 (Cx30), and GJB3 (Cx31) connexin genes. |
| format | Article |
| id | doaj-art-2d306a026a634d3a81ca1fcf7ceb70cb |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-2d306a026a634d3a81ca1fcf7ceb70cb2025-02-03T06:11:35ZengWileyThe Scientific World Journal2356-61401537-744X2019-01-01201910.1155/2019/51989315198931Comparison of Predictive In Silico Tools on Missense Variants in GJB2, GJB6, and GJB3 Genes Associated with Autosomal Recessive Deafness 1A (DFNB1A)Vera G. Pshennikova0Nikolay A. Barashkov1Georgii P. Romanov2Fedor M. Teryutin3Aisen V. Solov’ev4Nyurgun N. Gotovtsev5Alena A. Nikanorova6Sergey S. Nakhodkin7Nikolay N. Sazonov8Igor V. Morozov9Alexander A. Bondar10Lilya U. Dzhemileva11Elza K. Khusnutdinova12Olga L. Posukh13Sardana A. Fedorova14Department of Molecular Genetics, Federal State Budgetary Scientific Institution “Yakut Science Centre of Complex Medical Problems”, Yakutsk, RussiaDepartment of Molecular Genetics, Federal State Budgetary Scientific Institution “Yakut Science Centre of Complex Medical Problems”, Yakutsk, RussiaDepartment of Molecular Genetics, Federal State Budgetary Scientific Institution “Yakut Science Centre of Complex Medical Problems”, Yakutsk, RussiaDepartment of Molecular Genetics, Federal State Budgetary Scientific Institution “Yakut Science Centre of Complex Medical Problems”, Yakutsk, RussiaDepartment of Molecular Genetics, Federal State Budgetary Scientific Institution “Yakut Science Centre of Complex Medical Problems”, Yakutsk, RussiaDepartment of Molecular Genetics, Federal State Budgetary Scientific Institution “Yakut Science Centre of Complex Medical Problems”, Yakutsk, RussiaDepartment of Molecular Genetics, Federal State Budgetary Scientific Institution “Yakut Science Centre of Complex Medical Problems”, Yakutsk, RussiaLaboratory of Molecular Biology, Institute of Natural Sciences, M.K. Ammosov North-Eastern Federal University, Yakutsk, RussiaLaboratory of Molecular Biology, Institute of Natural Sciences, M.K. Ammosov North-Eastern Federal University, Yakutsk, RussiaInstitute of Chemical Biology and Fundamental Medicine, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, RussiaInstitute of Chemical Biology and Fundamental Medicine, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, RussiaLaboratory of Human Molecular Genetics, Institute of Biochemistry and Genetics, Ufa Scientific Centre, Russian Academy of Sciences, Ufa, RussiaLaboratory of Human Molecular Genetics, Institute of Biochemistry and Genetics, Ufa Scientific Centre, Russian Academy of Sciences, Ufa, RussiaNovosibirsk State University, Novosibirsk, RussiaDepartment of Molecular Genetics, Federal State Budgetary Scientific Institution “Yakut Science Centre of Complex Medical Problems”, Yakutsk, RussiaIn silico predictive software allows assessing the effect of amino acid substitutions on the structure or function of a protein without conducting functional studies. The accuracy of in silico pathogenicity prediction tools has not been previously assessed for variants associated with autosomal recessive deafness 1A (DFNB1A). Here, we identify in silico tools with the most accurate clinical significance predictions for missense variants of the GJB2 (Cx26), GJB6 (Cx30), and GJB3 (Cx31) connexin genes associated with DFNB1A. To evaluate accuracy of selected in silico tools (SIFT, FATHMM, MutationAssessor, PolyPhen-2, CONDEL, MutationTaster, MutPred, Align GVGD, and PROVEAN), we tested nine missense variants with previously confirmed clinical significance in a large cohort of deaf patients and control groups from the Sakha Republic (Eastern Siberia, Russia): Сх26: p.Val27Ile, p.Met34Thr, p.Val37Ile, p.Leu90Pro, p.Glu114Gly, p.Thr123Asn, and p.Val153Ile; Cx30: p.Glu101Lys; Cx31: p.Ala194Thr. We compared the performance of the in silico tools (accuracy, sensitivity, and specificity) by using the missense variants in GJB2 (Cx26), GJB6 (Cx30), and GJB3 (Cx31) genes associated with DFNB1A. The correlation coefficient (r) and coefficient of the area under the Receiver Operating Characteristic (ROC) curve as alternative quality indicators of the tested programs were used. The resulting ROC curves demonstrated that the largest coefficient of the area under the curve was provided by three programs: SIFT (AUC = 0.833, p = 0.046), PROVEAN (AUC = 0.833, p = 0.046), and MutationAssessor (AUC = 0.833, p = 0.002). The most accurate predictions were given by two tested programs: SIFT and PROVEAN (Ac = 89%, Se = 67%, Sp = 100%, r = 0.75, AUC = 0.833). The results of this study may be applicable for analysis of novel missense variants of the GJB2 (Cx26), GJB6 (Cx30), and GJB3 (Cx31) connexin genes.http://dx.doi.org/10.1155/2019/5198931 |
| spellingShingle | Vera G. Pshennikova Nikolay A. Barashkov Georgii P. Romanov Fedor M. Teryutin Aisen V. Solov’ev Nyurgun N. Gotovtsev Alena A. Nikanorova Sergey S. Nakhodkin Nikolay N. Sazonov Igor V. Morozov Alexander A. Bondar Lilya U. Dzhemileva Elza K. Khusnutdinova Olga L. Posukh Sardana A. Fedorova Comparison of Predictive In Silico Tools on Missense Variants in GJB2, GJB6, and GJB3 Genes Associated with Autosomal Recessive Deafness 1A (DFNB1A) The Scientific World Journal |
| title | Comparison of Predictive In Silico Tools on Missense Variants in GJB2, GJB6, and GJB3 Genes Associated with Autosomal Recessive Deafness 1A (DFNB1A) |
| title_full | Comparison of Predictive In Silico Tools on Missense Variants in GJB2, GJB6, and GJB3 Genes Associated with Autosomal Recessive Deafness 1A (DFNB1A) |
| title_fullStr | Comparison of Predictive In Silico Tools on Missense Variants in GJB2, GJB6, and GJB3 Genes Associated with Autosomal Recessive Deafness 1A (DFNB1A) |
| title_full_unstemmed | Comparison of Predictive In Silico Tools on Missense Variants in GJB2, GJB6, and GJB3 Genes Associated with Autosomal Recessive Deafness 1A (DFNB1A) |
| title_short | Comparison of Predictive In Silico Tools on Missense Variants in GJB2, GJB6, and GJB3 Genes Associated with Autosomal Recessive Deafness 1A (DFNB1A) |
| title_sort | comparison of predictive in silico tools on missense variants in gjb2 gjb6 and gjb3 genes associated with autosomal recessive deafness 1a dfnb1a |
| url | http://dx.doi.org/10.1155/2019/5198931 |
| work_keys_str_mv | AT veragpshennikova comparisonofpredictiveinsilicotoolsonmissensevariantsingjb2gjb6andgjb3genesassociatedwithautosomalrecessivedeafness1adfnb1a AT nikolayabarashkov comparisonofpredictiveinsilicotoolsonmissensevariantsingjb2gjb6andgjb3genesassociatedwithautosomalrecessivedeafness1adfnb1a AT georgiipromanov comparisonofpredictiveinsilicotoolsonmissensevariantsingjb2gjb6andgjb3genesassociatedwithautosomalrecessivedeafness1adfnb1a AT fedormteryutin comparisonofpredictiveinsilicotoolsonmissensevariantsingjb2gjb6andgjb3genesassociatedwithautosomalrecessivedeafness1adfnb1a AT aisenvsolovev comparisonofpredictiveinsilicotoolsonmissensevariantsingjb2gjb6andgjb3genesassociatedwithautosomalrecessivedeafness1adfnb1a AT nyurgunngotovtsev comparisonofpredictiveinsilicotoolsonmissensevariantsingjb2gjb6andgjb3genesassociatedwithautosomalrecessivedeafness1adfnb1a AT alenaanikanorova comparisonofpredictiveinsilicotoolsonmissensevariantsingjb2gjb6andgjb3genesassociatedwithautosomalrecessivedeafness1adfnb1a AT sergeysnakhodkin comparisonofpredictiveinsilicotoolsonmissensevariantsingjb2gjb6andgjb3genesassociatedwithautosomalrecessivedeafness1adfnb1a AT nikolaynsazonov comparisonofpredictiveinsilicotoolsonmissensevariantsingjb2gjb6andgjb3genesassociatedwithautosomalrecessivedeafness1adfnb1a AT igorvmorozov comparisonofpredictiveinsilicotoolsonmissensevariantsingjb2gjb6andgjb3genesassociatedwithautosomalrecessivedeafness1adfnb1a AT alexanderabondar comparisonofpredictiveinsilicotoolsonmissensevariantsingjb2gjb6andgjb3genesassociatedwithautosomalrecessivedeafness1adfnb1a AT lilyaudzhemileva comparisonofpredictiveinsilicotoolsonmissensevariantsingjb2gjb6andgjb3genesassociatedwithautosomalrecessivedeafness1adfnb1a AT elzakkhusnutdinova comparisonofpredictiveinsilicotoolsonmissensevariantsingjb2gjb6andgjb3genesassociatedwithautosomalrecessivedeafness1adfnb1a AT olgalposukh comparisonofpredictiveinsilicotoolsonmissensevariantsingjb2gjb6andgjb3genesassociatedwithautosomalrecessivedeafness1adfnb1a AT sardanaafedorova comparisonofpredictiveinsilicotoolsonmissensevariantsingjb2gjb6andgjb3genesassociatedwithautosomalrecessivedeafness1adfnb1a |