Abstract
OBJECTIVES:
Single nucleotide variants (SNVs) are the most common type of genetic variation among humans. High-throughput sequencing methods have recently characterized millions of SNVs in several thousand individuals from various populations, most of which are benign polymorphisms. Identifying rare disease-causing SNVs remains challenging, and often requires functional in vitro studies. Prioritizing the most likely pathogenic SNVs is of utmost importance, and several computational methods have been developed for this purpose. However, these methods are based on different assumptions, and often produce discordant results. The aim of the present study was to evaluate the performance of 11 widely used pathogenicity prediction tools, which are freely available for identifying known pathogenic SNVs: Fathmn, Mutation Assessor, Protein Analysis Through Evolutionary Relationships (Phanter), Sorting Intolerant From Tolerant (SIFT), Mutation Taster, Polymorphism Phenotyping v2 (Polyphen-2), Align Grantham Variation Grantham Deviation (Align-GVGD), CAAD, Provean, SNPs&GO, and MutPred.
METHODS:
We analyzed 40 functionally proven pathogenic SNVs in four different genes associated with differences in sex development (DSD): 17β-hydroxysteroid dehydrogenase 3 (HSD17B3), steroidogenic factor 1 (NR5A1), androgen receptor (AR), and luteinizing hormone/chorionic gonadotropin receptor (LHCGR). To evaluate the false discovery rate of each tool, we analyzed 36 frequent (MAF>0.01) benign SNVs found in the same four DSD genes. The quality of the predictions was analyzed using six parameters: accuracy, precision, negative predictive value (NPV), sensitivity, specificity, and Matthews correlation coefficient (MCC). Overall performance was assessed using a receiver operating characteristic (ROC) curve.
RESULTS:
Our study found that none of the tools were 100% precise in identifying pathogenic SNVs. The highest specificity, precision, and accuracy were observed for Mutation Assessor, MutPred, SNP, and GO. They also presented the best statistical results based on the ROC curve statistical analysis. Of the 11 tools evaluated, 6 (Mutation Assessor, Phanter, SIFT, Mutation Taster, Polyphen-2, and CAAD) exhibited sensitivity >0.90, but they exhibited lower specificity (0.42-0.67). Performance, based on MCC, ranged from poor (Fathmn=0.04) to reasonably good (MutPred=0.66).
CONCLUSION:
Computational algorithms are important tools for SNV analysis, but their correlation with functional studies not consistent. In the present analysis, the best performing tools (based on accuracy, precision, and specificity) were Mutation Assessor, MutPred, and SNPs&GO, which presented the best concordance with functional studies.
Disorders of Sex Development; Pathogenicity Prediction Tools; Genetics
INTRODUCTION
The term “differences in sex development” (DSD) refers to congenital conditions in which chromosomal, gonadal, or anatomical sex development is atypical (11. Wisniewski AB, Batista RL, Costa EMF, Finlayson C, Sircili MHP, Dénes FT, et al. Management of 46,XY Differences/Disorders of Sex Development (DSD) Throughout Life. Endocr Rev. 2019;40(6):1547-72. https://doi.org/10.1210/er.2019-00049.
https://doi.org/10.1210/er.2019-00049...
). They can be classified into three major categories: sex chromosome DSDs, 46,XX DSDs, and 46,XY DSDs (22. Lee PA, Houk CP, Ahmed SF, Hughes IA; International Consensus Conference on Intersex organized by the Lawson Wilkins Pediatric Endocrine Society and the European Society for Paediatric Endocrinology. Consensus statement on management of intersex disorders. International Consensus Conference on Intersex. Pediatrics. 2006;118(2):e488-500. https://doi.org/10.1542/peds.2006-0738.
https://doi.org/10.1542/peds.2006-0738...
). Most causes of DSDs are genetically determined, and several genes have been found to be associated with the DSD phenotype (33. Mendonca BB. Gender assignment in patients with disorder of sex development. Curr Opin Endocrinol Diabetes Obes. 2014;21(6):511-4. https://doi.org/10.1097/MED.0000000000000115.
https://doi.org/10.1097/MED.000000000000...
). Recent studies in individuals with DSDs have characterized numerous single nucleotide variants (SNV) in several genes, most of which are benign polymorphisms. However, distinguishing rare disease-causing SNVs from rare polymorphisms remains challenging. Functional studies for disease association variants are often used, but are laborious and time-consuming (44. Kolesinska Z, Acierno J Jr, Ahmed SF, Xu C, Kapczuk K, Skorczyk-Werner A, et al. Integrating clinical and genetic approaches in the diagnosis of 46,XY disorders of sex development. Endocr Connect. 2018;7(12):1480-90. https://doi.org/10.1530/EC-18-0472.
https://doi.org/10.1530/EC-18-0472...
,55. Hughes LA, McKay-Bounford K, Webb EA, Dasani P, Clokie S, Chandran H, et al. Next generation sequencing (NGS) to improve the diagnosis and management of patients with disorders of sex development (DSD). Endocr Connect. 2019;8(2):100-10. https://doi.org/10.1530/EC-18-0376.
https://doi.org/10.1530/EC-18-0376...
).
Many methods have been developed for the computational prediction of the pathogenicity of SNVs, which are based on evolutionary conservation, protein structure/function, or assembly parameters, such as allelic diversity, pathogenicity, and association with genome-wide association studies (66. Leong IU, Stuckey A, Lai D, Skinner JR, Love DR. Assessment of the predictive accuracy of five in silico prediction tools, alone or in combination, and two metaservers to classify long QT syndrome gene mutations. BMC Med Genet. 2015;16:34. https://doi.org/10.1186/s12881-015-0176-z
https://doi.org/10.1186/s12881-015-0176-...
). Studies analyzing the performance of prediction programs have been completed using a large number of missense variants (77. Thusberg J, Olatubosun A, Vihinen M. Performance of mutation pathogenicity prediction methods on missense variants. Hum Mutat. 2011;32(4):358-68. https://doi.org/10.1002/humu.21445.
https://doi.org/10.1002/humu.21445...
). In the present study, we compared the performance of 11 widely used pathogenic prediction tools in the analysis of proven pathogenic DSD-causing SNVs in four different genes.
MATERIAL AND METHODS
Dataset
We analyzed 40 disease-causing SNVs in four different genes associated with DSD: 17β-hydroxysteroid dehydrogenase 3 (HSD17B3), steroidogenic factor 1 (NR5A1), androgen receptor (AR), and luteinizing hormone/chorionic gonadotropin receptor (LHCGR). All pathogenic allelic variants have been published with functional studies showing loss of function activity (Table 1). To evaluate the false discovery rate of each tool, we selected 36 frequent benign SNVs (MAF>0.01) found in the same DSD genes (Table 1).
Prediction Methods
We selected 11 widely used pathogenic prediction tools freely available on the Web: Fathmn, Mutation Assessor, Protein Analysis Through Evolutionary Relationships (Phanter), SIFT (Sorting Intolerant From Tolerant), Mutation Taster, Polymorphism Phenotyping v2 (Polyphen-2), Align Grantham Variation Grantham Deviation (Align-GVGD), CAAD, Provean, and SNPs&GO (Table 2).
Statistical Analysis
The quality of the predictions was analyzed using six parameters: accuracy, precision, negative predictive value (NPV), sensitivity, specificity, and Matthews correlation coefficient (MCC). In the equations below, tp, tn, fp, and fn refer to true positive, true negative, false positive, and false negative, respectively.
The MCC (4343. Matthews BW. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta. 1975;405(2):442-51. https://doi.org/10.1016/0005-2795(75)90109-9.
https://doi.org/10.1016/0005-2795(75)901...
) is an important statistics tool that is widely used in bioinformatics as a performance metric, as it is not affected by the differing proportions of neutral and pathogenic datasets predicted by the different programs. Additionally, we also assessed the overall performance of deleterious prediction with the receiver operating characteristic (ROC) curve and area under the curve (AUC), using MedCalc for Windows, version 15.0 (MedCalc Software, Ostend, Belgium). ROC curves are an indicator of probability and performance for classification problems at various threshold settings, and AUCs represent the degree or measure of separability. Together, they indicate how capable a model is of distinguishing between classes. The higher the AUC, the better the model is at predicting an outcome (4444. Ma H, Bandos AI, Gur D. On the use of partial area under the ROC curve for comparison of two diagnostic tests. Biom J. 2015;57(2):304-20. https://doi.org/10.1002/bimj.201400023.
https://doi.org/10.1002/bimj.201400023...
).
We randomized the results of prediction of SNV present in the DDS genes as pathogenic or benign, based on the classification given by each program. The classifications “probably benign”, “benign”, “low”, and “neutral”, provided by Fathmn, Mutation Assessor, Phanter, SIFT, Mutation Taster, Provean, and Polyphen-2, were considered as benign. The classifications “possibly damaging”, “probably damaging”, “high”, “medium”, “deleterious”, and “damaging”, were considered as pathogenic. For CAAD, which uses numeric scores, values ≤10 were classified as benign, and those >10 were classified as pathogenic. Align-GVGD also used numeric scores, which were classified as benign up to 25, and anything above was classified as pathogenic.
RESULTS
Based on the results for each program, none of the tools were 100% precise in identifying pathogenic SNVs. The values for the parameters measured are listed in Table 3, and include all pathogenic and benign variants. Phanter had the highest precision in the classification of pathogenic variants (38 out of 40 known to be pathogenic), followed by Mutation Taster and Polyphen-2 (both 37 out of 40 known to be pathogenic). Align-GVGD correctly classified fewer known pathogenic SNVs than any other tool (33 of 40 known to be pathogenic). Phanter and Mutation Taster both classified a high number of know benign SNVs as pathogenic (21 and 17, respectively, of 36).
Mutation Assessor, MutPred, and SNPs&GO presented more consistent results regarding the nature of the SNVs (pathogenic or benign). MutPred had the highest accuracy, precision, and specificity (0.83, 0.85, and 0.83, respectively), as seen in Table 4. Mutation Assessor has the highest sensitivity of all the tools evaluated, although five other tools (Phanter, SIFT, Mutation Taster, Polyphen-2, and CAAD) exhibited sensitivity >0.90, however, they were found to have lower specificity (0.42-0.67). Based on MCC, performance ranged from poor (Fathmn=0.04) to reasonably good (MutPred=0.66). Fathmn and Align-GVGD exhibited the worst performance, with a high number of false positive results (MMC=0.04 and 0.06, respectively).
The comparative predictive performance of each tool was evaluated using the AUC scores from ROC plots and the true negative rate (TNR, or specificity) as measurements. The analysis was separated into random groups, since the program analyzed a maximum of six samples at a time (Figure 1). The AUC values varied from low (Mutation Taster=0.55) to reasonably good (SNPs-&-GO=0.89), and two programs (Fathmn and Align-GVD) for which the MMC values were poor (0.04 and 0.06, respectively), improved in the statistical analysis made using the ROC curve (0.67 and 0.57, respectively). The other programs did not see a change in their statistical values at the same level.
ROC curves of 11 methods separated into random groups: A - Align-GVD, CAAD, Fathmn, Mutation Assessor, Mutation Taster and MutPred; B - Phanter, Polyphen-2, Provean, SIFT, and SNPs&GO.
DISCUSSION
In the present study, we analyzed and compared the abilities of 11 widely available tools for predicting the pathogenicity of SNVs. Although some algorithms are based on the same data sets, they differ in the database for conservation analysis and structural attributes. They also differ in the information required to run the predictions, as some programs request the accession number of the gene, others the protein change, nucleotide change, or chromosomal position.
Overall, we found that Mutation Assessor, MutPred, and SNPs&GO were the most reliable predictors for SNV classifications. They also exhibited the best AUC results. The accuracy of all tools evaluated ranged from poor to reasonably good (MMC=0.04-0.66). These results are consistent with what has been shown in previous studies (77. Thusberg J, Olatubosun A, Vihinen M. Performance of mutation pathogenicity prediction methods on missense variants. Hum Mutat. 2011;32(4):358-68. https://doi.org/10.1002/humu.21445.
https://doi.org/10.1002/humu.21445...
,99. Dong C, Wei P, Jian X, Gibbs R, Boerwinkle E, Wang K, et al. Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. Hum Mol Genet. 2015;24(8):2125-37. https://doi.org/10.1093/hmg/ddu733.
https://doi.org/10.1093/hmg/ddu733...
), which is that the number of samples used in the analysis did not influence the statistical result as much.
In conclusion, computational algorithms are important screening tools for prioritizing and identifying disease-causing SNVs, but their correlation with functional studies is not consistent. In the present analysis, the highest-performing tools were Mutation Assessor, MutPred, and SNPs&GO.
ACKNOWLEDGMENTS
This work was supported by Grant 2013/02162-8 (to B.B.M) from the Fundação de Amparo è Pesquisa do Estado de São Paulo (Fapesp) and Grant 455845/2018-4 (to B.B.M) from the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq).
REFERENCES
-
1Wisniewski AB, Batista RL, Costa EMF, Finlayson C, Sircili MHP, Dénes FT, et al. Management of 46,XY Differences/Disorders of Sex Development (DSD) Throughout Life. Endocr Rev. 2019;40(6):1547-72. https://doi.org/10.1210/er.2019-00049
» https://doi.org/10.1210/er.2019-00049 -
2Lee PA, Houk CP, Ahmed SF, Hughes IA; International Consensus Conference on Intersex organized by the Lawson Wilkins Pediatric Endocrine Society and the European Society for Paediatric Endocrinology. Consensus statement on management of intersex disorders. International Consensus Conference on Intersex. Pediatrics. 2006;118(2):e488-500. https://doi.org/10.1542/peds.2006-0738
» https://doi.org/10.1542/peds.2006-0738 -
3Mendonca BB. Gender assignment in patients with disorder of sex development. Curr Opin Endocrinol Diabetes Obes. 2014;21(6):511-4. https://doi.org/10.1097/MED.0000000000000115
» https://doi.org/10.1097/MED.0000000000000115 -
4Kolesinska Z, Acierno J Jr, Ahmed SF, Xu C, Kapczuk K, Skorczyk-Werner A, et al. Integrating clinical and genetic approaches in the diagnosis of 46,XY disorders of sex development. Endocr Connect. 2018;7(12):1480-90. https://doi.org/10.1530/EC-18-0472
» https://doi.org/10.1530/EC-18-0472 -
5Hughes LA, McKay-Bounford K, Webb EA, Dasani P, Clokie S, Chandran H, et al. Next generation sequencing (NGS) to improve the diagnosis and management of patients with disorders of sex development (DSD). Endocr Connect. 2019;8(2):100-10. https://doi.org/10.1530/EC-18-0376
» https://doi.org/10.1530/EC-18-0376 -
6Leong IU, Stuckey A, Lai D, Skinner JR, Love DR. Assessment of the predictive accuracy of five in silico prediction tools, alone or in combination, and two metaservers to classify long QT syndrome gene mutations. BMC Med Genet. 2015;16:34. https://doi.org/10.1186/s12881-015-0176-z
» https://doi.org/10.1186/s12881-015-0176-z -
7Thusberg J, Olatubosun A, Vihinen M. Performance of mutation pathogenicity prediction methods on missense variants. Hum Mutat. 2011;32(4):358-68. https://doi.org/10.1002/humu.21445
» https://doi.org/10.1002/humu.21445 -
8Andersson S, Geissler WM, Wu L, Davis DL, Grumbach MM, New MI, et al. Molecular genetics and pathophysiology of 17 beta-hydroxysteroid dehydrogenase 3 deficiency. J Clin Endocrinol Metab. 1996;81(1):130-6. https://doi.org/10.1210/jcem.81.1.8550739
» https://doi.org/10.1210/jcem.81.1.8550739 -
9Dong C, Wei P, Jian X, Gibbs R, Boerwinkle E, Wang K, et al. Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. Hum Mol Genet. 2015;24(8):2125-37. https://doi.org/10.1093/hmg/ddu733
» https://doi.org/10.1093/hmg/ddu733 -
10Geissler WM, Davis DL, Wu L, Bradshaw KD, Patel S, Mendonca BB, et al. Male pseudohermaphroditism caused by mutations of testicular 17 beta-hydroxysteroid dehydrogenase 3. Nat Genet. 1994;7(1):34-9. https://doi.org/10.1038/ng0594-34
» https://doi.org/10.1038/ng0594-34 -
11Lindqvist A, Hughes IA, Andersson S. Substitution mutation C268Y causes 17 beta-hydroxysteroid dehydrogenase 3 deficiency. J Clin Endocrinol Metab. 2001;86(2):921-3. https://doi.org/10.1210/jcem.86.2.7172
» https://doi.org/10.1210/jcem.86.2.7172 -
12Lin L, Philibert P, Ferraz-de-Souza B, Kelberman D, Homfray T, Albanese A, et al. Heterozygous missense mutations in steroidogenic factor 1 (SF1/Ad4BP, NR5A1) are associated with 46,XY disorders of sex development with normal adrenal function. J Clin Endocrinol Metab. 2007;92(3):991-9. https://doi.org/10.1210/jc.2006-1672
» https://doi.org/10.1210/jc.2006-1672 -
13Camats N, Pandey AV, Fernández-Cancio M, Andaluz P, Janner M, Torán N, et al. Ten novel mutations in the NR5A1 gene cause disordered sex development in 46,XY and ovarian insufficiency in 46,XX individuals. J Clin Endocrinol Metab. 2012;97(7):E1294-306. https://doi.org/10.1210/jc.2011-3169
» https://doi.org/10.1210/jc.2011-3169 -
14Allali S, Muller JB, Brauner R, Lourenço D, Boudjenah R, Karageorgou V, et al. Mutation analysis of NR5A1 encoding steroidogenic factor 1 in 77 patients with 46, XY disorders of sex development (DSD) including hypospadias. PLoS One. 2011;6(10):e24117. https://doi.org/10.1371/journal.pone.0024117
» https://doi.org/10.1371/journal.pone.0024117 -
15Lin L, Achermann JC. Steroidogenic factor-1 (SF-1, Ad4BP, NR5A1) and disorders of testis development. Sex Dev. 2008;2(4-5):200-9. https://doi.org/10.1159/000152036
» https://doi.org/10.1159/000152036 -
16Imasaki K, Okabe T, Murakami H, Tanaka Y, Haji M, Takayanagi R, et al. Androgen insensitivity syndrome due to new mutations in the DNA-binding domain of the androgen receptor. Mol Cell Endocrinol. 1996;120(1):15-24. https://doi.org/10.1016/0303-7207(96)03812-9
» https://doi.org/10.1016/0303-7207(96)03812-9 -
17Ahmed SF, Cheng A, Dovey L, Hawkins JR, Martin H, Rowland J, et al. Phenotypic features, androgen receptor binding, and mutational analysis in 278 clinical cases reported as androgen insensitivity syndrome. J Clin Endocrinol Metab. 2000;85(2):658-65. https://doi.org/10.1210/jcem.85.2.6337
» https://doi.org/10.1210/jcem.85.2.6337 -
18Jääskeläinen J, Mongan NP, Harland S, Hughes IA. Five novel androgen receptor gene mutations associated with complete androgen insensitivity syndrome. Hum Mutat. 2006;27(3):291. https://doi.org/10.1002/humu.9405
» https://doi.org/10.1002/humu.9405 -
19Pals-Rylaarsdam R, Liu G, Brickman W, Duranteau L, Monroe J, El-Awady MK, et al. A novel double mutation in the luteinizing hormone receptor in a kindred with familial Leydig cell hypoplasia and male pseudohermaphroditism. Endocr Res. 2005;31(4):307-23. https://doi.org/10.1080/07435800500430890
» https://doi.org/10.1080/07435800500430890 -
20Themmen APN, Huhtaniemi IT. Mutations of gonadotropins and gonadotropin receptors: elucidating the physiology and pathophysiology of pituitary-gonadal function. Endocr Rev. 2000;21(5):551-83. https://doi.org/10.1210/edrv.21.5.0409
» https://doi.org/10.1210/edrv.21.5.0409 -
21Stavrou SS, Zhu YS, Cai LQ, Katz MD, Herrera C, Defillo-Ricart M, et al. A novel mutation of the human luteinizing hormone receptor in 46XY and 46XX sisters. J Clin Endocrinol Metab. 1998;83(6):2091-8. https://doi.org/10.1210/jcem.83.6.4855
» https://doi.org/10.1210/jcem.83.6.4855 -
22Leung MY, Al-Muslim O, Wu SM, Aziz A, Inam S, Awadh M, et al. A novel missense homozygous inactivating mutation in the fourth transmembrane helix of the luteinizing hormone receptor in leydig cell hypoplasia. Am J Med Genet A. 2004;130A(2):146-53. https://doi.org/10.1002/ajmg.a.20681
» https://doi.org/10.1002/ajmg.a.20681 -
23Kraaij R, Post M, Kremer H, Milgrom E, Epping W, Brunner HG, et al. A missense mutation in the second transmembrane segment of the luteinizing hormone receptor causes familial male-limited precocious puberty. J Clin Endocrinol Metab. 1995;80(11):3168-72. https://doi.org/10.1210/jcem.80.11.7593421
» https://doi.org/10.1210/jcem.80.11.7593421 -
24Latronico AC, Abell AN, Arnhold IJ, Liu X, Lins TS, Brito VN, et al. A unique constitutively activating mutation in third transmembrane helix of luteinizing hormone receptor causes sporadic male gonadotropin-independent precocious puberty. J Clin Endocrinol Metab. 1998;83(7):2435-40. https://doi.org/10.1210/jcem.83.7.4968
» https://doi.org/10.1210/jcem.83.7.4968 -
25Laue L, Chan WY, Hsueh AJ, Kudo M, Hsu SY, Wu SM, et al. Genetic heterogeneity of constitutively activating mutations of the human luteinizing hormone receptor in familial male-limited precocious puberty. Proc Natl Acad Sci U S A. 1995;92(6):1906-10. https://doi.org/10.1073/pnas.92.6.1906
» https://doi.org/10.1073/pnas.92.6.1906 -
26Latronico AC, Anasti J, Arnhold IJ, Mendonça BB, Domenice S, Albano MC, et al. A novel mutation of the luteinizing hormone receptor gene causing male gonadotropin-independent precocious puberty. J Clin Endocrinol Metab. 1995;80(8):2490-4. https://doi.org/10.1210/jcem.80.8.7629248
» https://doi.org/10.1210/jcem.80.8.7629248 -
27Laue L, Wu SM, Kudo M, Hsueh AJ, Cutler GB Jr, Jelly DH, et al. Heterogeneity of activating mutations of the human luteinizing hormone receptor in male-limited precocious puberty. Biochem Mol Med. 1996;58(2):192-8. https://doi.org/10.1006/bmme.1996.0048
» https://doi.org/10.1006/bmme.1996.0048 -
28Shihab HA, Gough J, Cooper DN, Day IN, Gaunt TR. Predicting the functional consequences of cancer-associated amino acid substitutions. Bioinformatics. 2013;29(12):1504-10. https://doi.org/10.1093/bioinformatics/btt182
» https://doi.org/10.1093/bioinformatics/btt182 -
29Shihab HA, Gough J, Cooper DN, Stenson PD, Barker GL, Edwards KJ, et al. Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models. Hum Mutat. 2013;34(1):57-65. https://doi.org/10.1002/humu.22225
» https://doi.org/10.1002/humu.22225 -
30Shihab HA, Gough J, Mort M, Cooper DN, Day IN, Gaunt TR. Ranking non-synonymous single nucleotide polymorphisms based on disease concepts. Hum Genomics. 2014;8(1):11. https://doi.org/10.1186/1479-7364-8-11
» https://doi.org/10.1186/1479-7364-8-11 -
31Reva B, Antipin Y, Sander C. Determinants of protein function revealed by combinatorial entropy optimization. Genome Biol. 2007;8(11):R232. https://doi.org/10.1186/gb-2007-8-11-r232
» https://doi.org/10.1186/gb-2007-8-11-r232 -
32Tang H, Thomas PD. PANTHER-PSEP: predicting disease-causing genetic variants using position-specific evolutionary preservation. Bioinformatics. 2016;32(14):2230-2. https://doi.org/10.1093/bioinformatics/btw222
» https://doi.org/10.1093/bioinformatics/btw222 -
33Sim NL, Kumar P, Hu J, Henikoff S, Schneider G, Ng PC. SIFT web server: predicting effects of amino acid substitutions on proteins. Nucleic Acids Res. 2012;40(Web Server issue):W452-7. https://doi.org/10.1093/nar/gks539
» https://doi.org/10.1093/nar/gks539 -
34Schwarz JM, Cooper DN, Schuelke M, Seelow D. MutationTaster2: mutation prediction for the deep-sequencing age. Nat Methods. 2014;11(4):361-2. https://doi.org/10.1038/nmeth.2890
» https://doi.org/10.1038/nmeth.2890 -
35Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, et al. A method and server for predicting damaging missense mutations. Nat Methods. 2010;7(4):248-9. https://doi.org/10.1038/nmeth0410-248
» https://doi.org/10.1038/nmeth0410-248 -
36Tavtigian SV, Deffenbaugh AM, Yin L, Judkins T, Scholl T, Samollow PB, et al. Comprehensive statistical study of 452 BRCA1 missense substitutions with classification of eight recurrent substitutions as neutral. J Med Genet. 2006;43(4):295-305. https://doi.org/10.1136/jmg.2005.033878
» https://doi.org/10.1136/jmg.2005.033878 -
37Pejaver V, Urresti J, Lugo-Martinez J, Pagel KA, Lin GN, Nam HJ, et al. Inferring the molecular and phenotypic impact of amino acid variants with MutPred2. Nat Commun. 2020;11(1):5918. https://doi.org/10.1038/s41467-020-19669-x
» https://doi.org/10.1038/s41467-020-19669-x -
38Rentzsch P, Witten D, Cooper GM, Shendure J, Kircher M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 2019;47(D1):D886-D894. https://doi.org/10.1093/nar/gky1016
» https://doi.org/10.1093/nar/gky1016 -
39Choi Y, Sims GE, Murphy S, Miller JR, Chan AP. Predicting the functional effect of amino acid substitutions and indels. PLoS One. 2012;7(10):e46688. https://doi.org/10.1371/journal.pone.0046688
» https://doi.org/10.1371/journal.pone.0046688 -
40Calabrese R, Capriotti E, Fariselli P, Martelli PL, Casadio R. Functional annotations improve the predictive score of human disease-related mutations in proteins. Hum Mutat. 2009;30(8):1237-44. https://doi.org/10.1002/humu.21047
» https://doi.org/10.1002/humu.21047 -
41Capriotti E, Altman RB. Improving the prediction of disease-related variants using protein three-dimensional structure. BMC Bioinformatics. 2011;12 Suppl 4(Suppl 4):S3. https://doi.org/10.1186/1471-2105-12-S4-S3
» https://doi.org/10.1186/1471-2105-12-S4-S3 -
42Capriotti E, Calabrese R, Fariselli P, Martelli PL, Altman RB, Casadio R. WS-SNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation. BMC Genomics. 2013;14 Suppl 3(Suppl 3):S6. https://doi.org/10.1186/1471-2164-14-S3-S6
» https://doi.org/10.1186/1471-2164-14-S3-S6 -
43Matthews BW. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta. 1975;405(2):442-51. https://doi.org/10.1016/0005-2795(75)90109-9
» https://doi.org/10.1016/0005-2795(75)90109-9 -
44Ma H, Bandos AI, Gur D. On the use of partial area under the ROC curve for comparison of two diagnostic tests. Biom J. 2015;57(2):304-20. https://doi.org/10.1002/bimj.201400023
» https://doi.org/10.1002/bimj.201400023
Publication Dates
-
Publication in this collection
20 Jan 2021 -
Date of issue
2021
History
-
Received
18 June 2020 -
Accepted
27 Nov 2020