Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA.
The Donnelly Centre and Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON, Canada.
Genome Biol. 2023 Apr 20;24(1):82. doi: 10.1186/s13059-023-02922-z.
The impact of millions of individual genetic variants on molecular phenotypes in coding sequences remains unknown. Multiplexed assays of variant effect (MAVEs) are scalable methods to annotate relevant variants, but existing software lacks standardization, requires cumbersome configuration, and does not scale to large targets. We present satmut_utils as a flexible solution for simulation and variant quantification. We then benchmark MAVE software using simulated and real MAVE data. We finally determine mRNA abundance for thousands of cystathionine beta-synthase variants using two experimental methods. The satmut_utils package enables high-performance analysis of MAVEs and reveals the capability of variants to alter mRNA abundance.
目前尚不清楚数百万个个体遗传变异对编码序列中分子表型的影响。变异效应的多重分析（MAVE）是注释相关变异的可扩展方法，但现有的软件缺乏标准化，需要繁琐的配置，并且无法扩展到大型目标。我们提出 satmut_utils 作为模拟和变异量化的灵活解决方案。然后，我们使用模拟和真实的 MAVE 数据对 MAVE 软件进行基准测试。最后，我们使用两种实验方法确定了数千个胱硫醚β-合酶变体的 mRNA 丰度。satmut_utils 包能够实现 MAVEs 的高性能分析，并揭示了变异改变 mRNA 丰度的能力。
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