TAILVAR (Terminal extension Analysis for Improved prediction of Lengthened VARiants)

TAILVAR is built using a Random Forest model that predicts the pathogenicity of stop-loss variants. By integrating a combination of in-silico prediction scores, transcript, and protein features of C-terminal extensions, TAILVAR provides a score ranging from 0 to 1, indicating the probability of pathogenic potential. TAILVAR score cutoffs ≥ 0.70 and ≤ 0.30 can be used to distinguish potential pathogenic/likely pathogenic and benign/likely benign variants. For more information, please visit https://github.com/dr-yoon/TAILVARTAILVAR 基于随机森林（Random Forest）模型构建，用于预测终止丢失变异（stop-loss variants）的致病性。该工具整合了计算机模拟（in silico）预测评分、C端延伸区域的转录本与蛋白质特征，可输出取值范围为0至1的评分，用以表征对应变异具备致病潜能的概率。当TAILVAR评分的截断值设定为≥0.70与≤0.30时，可用于区分潜在致病性/可能致病性变异与良性/可能良性变异。如需了解更多信息，请访问 https://github.com/dr-yoon/TAILVAR