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Web-Based Bioinformatics Predictors: Recommendations to Assess Lysosomal Cholesterol Trafficking Diseases-Related Genes.
Methods of Information in Medicine ( IF 1.7 ) Pub Date : 2019-06-01 , DOI: 10.1055/s-0039-1692463
Laura López de Frutos 1, 2 , Jorge J Cebolla 1, 3 , Pilar Irún 1, 4 , Ralf Köhler 5 , Pilar Giraldo 1, 2
Affiliation  

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.

中文翻译:

基于网络的生物信息学预测因子:评估溶酶体胆固醇贩运疾病相关基因的建议。

引言越来越多的未知重要性的遗传变异(VUS)和几种计算机模拟预测工具的可用性使评估潜在有害的基因变异具有挑战性。材料和方法我们评估了几种程序和软件,以确定可以预测由胆固醇运输缺陷引起的溶酶体贮积症(LSD)中发现的遗传变异的影响的一种程序和软件。我们评估了最常用软件的敏感性,特异性,准确性,准确性和Matthew相关系数。结果我们的研究结果表明,对于外显子变体,只有MutPred1达到100%的准确性并产生最佳预测(灵敏度和准确性= 1.00),而内含子变体SROOGLE或Human Splicing Finder(HSF)产生最好的预测(灵敏度= 1.00,和精度= 1.00)。讨论下一代测序大大增加了检测到的遗传变异的数量,其中大多数被认为是VUS,因此需要准确的致病性预测。本研究的重点是准确预测LSD的重要性,以及大多数以前未报告的特定突变。结论我们发现NPC1,NPC2和LIPA变体的最佳预测工具是外显子区域为MutPred1,内含子区域和剪接位点为HSF和SROOGLE。以及大多数以前未报告的特定突变。结论我们发现NPC1,NPC2和LIPA变体的最佳预测工具是外显子区域为MutPred1,内含子区域和剪接位点为HSF和SROOGLE。以及大多数以前未报告的特定突变。结论我们发现NPC1,NPC2和LIPA变体的最佳预测工具是外显子区域为MutPred1,内含子区域和剪接位点为HSF和SROOGLE。
更新日期:2019-06-01
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