Methods Inf Med 2019; 58(01): 050-059
DOI: 10.1055/s-0039-1692463
Original Article
Georg Thieme Verlag KG Stuttgart · New York

Web-Based Bioinformatics Predictors: Recommendations to Assess Lysosomal Cholesterol Trafficking Diseases-Related Genes

Laura López de Frutos
1  Instituto de Investigación Sanitaria Aragón (IIS Aragón), Unidad de Investigación Traslacional (GIIS012), Hospital Universitario Miguel Servet, Zaragoza, Spain
2  Fundación para el Estudio y la Terapéutica de la Enfermedad de Gaucher y Otras Lisosomales (FEETEG), Zaragoza, Spain
,
Jorge J. Cebolla
1  Instituto de Investigación Sanitaria Aragón (IIS Aragón), Unidad de Investigación Traslacional (GIIS012), Hospital Universitario Miguel Servet, Zaragoza, Spain
3  Departamento de Bioquímica, Biología Molecular y Celular, Facultad de Ciencias, Universidad de Zaragoza, Zaragoza, Spain
,
Pilar Irún
1  Instituto de Investigación Sanitaria Aragón (IIS Aragón), Unidad de Investigación Traslacional (GIIS012), Hospital Universitario Miguel Servet, Zaragoza, Spain
4  Centro de Investigación Biomédica en Red (CIBER), Zaragoza, Spain
,
Ralf Köhler
5  Fundación Agencia Aragonesa para la Investigación y el Desarrollo (ARAID), Zaragoza, Spain
,
Pilar Giraldo
1  Instituto de Investigación Sanitaria Aragón (IIS Aragón), Unidad de Investigación Traslacional (GIIS012), Hospital Universitario Miguel Servet, Zaragoza, Spain
2  Fundación para el Estudio y la Terapéutica de la Enfermedad de Gaucher y Otras Lisosomales (FEETEG), Zaragoza, Spain
› Author Affiliations
Funding None.
Further Information

Publication History

20 September 2018

07 May 2019

Publication Date:
05 July 2019 (online)

Abstract

Introduction The growing number of genetic variants of unknown significance (VUS) and availability of several in silico prediction tools make the evaluation of potentially deleterious gene variants challenging.

Materials and Methods We evaluated several programs and software to determine the one that can predict the impact of genetic variants found in lysosomal storage disorders (LSDs) caused by defects in cholesterol trafficking best. We evaluated the sensitivity, specificity, accuracy, precision, and Matthew's correlation coefficient of the most common software.

Results Our findings showed that for exonic variants, only MutPred1 reached 100% accuracy and generated the best predictions (sensitivity and accuracy = 1.00), whereas intronic variants, SROOGLE or Human Splicing Finder (HSF) generated the best predictions (sensitivity = 1.00, and accuracy = 1.00).

Discussion Next-generation sequencing substantially increased the number of detected genetic variants, most of which were considered to be VUS, creating a need for accurate pathogenicity prediction. The focus of the present study is the importance of accurately predicting LSDs, with majority of previously unreported specific mutations.

Conclusion We found that the best prediction tool for the NPC1, NPC2, and LIPA variants was MutPred1 for exonic regions and HSF and SROOGLE for intronic regions and splice sites.

Authors' Contributions

Laura López de Frutos designed and performed the research, analyzed the data and drafted the manuscript. Jorge J. Cebolla and Pilar Irún performed the research and analyzed the data. Ralf Köhler reviewed and revised the manuscript, and Pilar Giraldo designed the research. All authors read and approved the manuscript before submission.


Authors J.J. Cebolla and P. Irún should be regarded as joint second authors.


Supplementary Material