对可能良性内部和同名变异的综合生物信息和拼接分析揭示了致病性证据
Owen R Hirschi1,2, Stephanie A Felker3, Surya P Rednam1,2
1Baylor College of Medicine, Houston, Texas.
medRxiv : the preprint server for health sciences
|November 14, 2023
概括
较新的拼接预测工具可以识别与遗传疾病相关的罕见,以前被忽视的变异. 将这些算法与RNA分析或体外测试相结合,有助于诊断缺乏分子诊断的患者.
科学领域:
- 基因组学就是基因组学.
- 分子生物学分子生物学
- 临床遗传学 临床遗传学
背景情况:
- 目前的变异分析主要针对编码和近外因子的内基区域.
- 先进的拼接预测算法和体外试验对罕见的良性/可能良性 (B/LB) 变体的影响在很大程度上没有被描述.
结论:
- 先进的拼接预测算法可以在怀疑遗传性疾病的个体中识别以前被归类为B / LB的罕见变异.
- 将SpliceAI与RNA分析或体外试验试验相结合,提供了一种强有力的策略,以发现未被诊断的患者中引起疾病的变异.
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