PDF(Pubmed)
Abstract：
UNASSIGNED: Variant interpretation is essential for identifying patients\' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).
UNASSIGNED: The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.
UNASSIGNED: VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications.
UNASSIGNED: VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.
UNASSIGNED: The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.
UNASSIGNED: VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications.
UNASSIGNED: VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.
摘要：
背景：变体解释对于在基因组中检测到的数百万患者中识别患者的致病遗传变异至关重要。数以百计的变化影响预测器（VIP），也称为变异效应预测因子(VEP)，已经为此目的开发了，有各种各样的方法和目标。为了方便探索可用的VIP选项，我们已经创建了变量影响预测数据库(VIPdb)。
结果：VariantImpactPredictor数据库（VIPdb）版本2展示了过去25年开发的VIP集合，总结他们的特点，ClinGen校准分数,CAGI评估结果，出版物详细信息,访问信息，和引文模式。我们先前在2019年总结了217个VIP及其在VIPDB中的功能。在这个基础上，我们确定并分类了另外186名贵宾，在Vipdb版本2中总共有403个VIP。大多数VIP都有能力预测单核苷酸变体和非同义变体的影响。自2010年代以来，已经开发了更多的VIP来预测插入和删除的影响。相比之下,相对较少的VIP专门用于预测拼接，结构,同义词,和监管变体。对贵宾的引用率不断提高，反映了贵宾使用的持续增长，引文的演变趋势揭示了该领域和个体方法的发展。
结论：VIPdb版本2总结了403个贵宾及其功能，可能促进各种变体解释应用的VIP探索。
背景：VIPDB版本2可在https://genomeinterpretation.org/vipdb/上获得。
结果：VariantImpactPredictor数据库（VIPdb）版本2展示了过去25年开发的VIP集合，总结他们的特点，ClinGen校准分数,CAGI评估结果，出版物详细信息,访问信息，和引文模式。我们先前在2019年总结了217个VIP及其在VIPDB中的功能。在这个基础上，我们确定并分类了另外186名贵宾，在Vipdb版本2中总共有403个VIP。大多数VIP都有能力预测单核苷酸变体和非同义变体的影响。自2010年代以来，已经开发了更多的VIP来预测插入和删除的影响。相比之下,相对较少的VIP专门用于预测拼接，结构,同义词,和监管变体。对贵宾的引用率不断提高，反映了贵宾使用的持续增长，引文的演变趋势揭示了该领域和个体方法的发展。
结论：VIPdb版本2总结了403个贵宾及其功能，可能促进各种变体解释应用的VIP探索。
背景：VIPDB版本2可在https://genomeinterpretation.org/vipdb/上获得。
