SuPreMo: a computational tool for streamlining in silico perturbation using sequence-based predictive models.
SuPreMo: a computational tool for streamlining in silico perturbation using sequence-based predictive models.
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SuPreMo：一种使用基于序列的预测模型简化计算机扰动的计算工具。
DOI：
10.1101/2023.11.03.565556
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Pollard,KatherineS
中科院分区：
文献类型：
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作者：
Gjoni,Ketrin;Pollard,KatherineS
基于序列的机器学习模型的不断发展提高了对该应用程序操作序列的需求。然而，使用模型编辑和评估基因组序列的现有方法具有局限性，例如与结构变体的不相容性、识别负责任的序列扰动的挑战以及对vcf文件输入和分阶段数据的需要。为了解决这些瓶颈，我们提出了Sequence Mutator for PredictiveModels（SuPreMo），这是一种可扩展的综合工具，用于执行和支持硅诱变实验。然后，我们演示了如何将参考序列和扰动序列对与机器学习模型一起使用，以确定致病变异的优先级或发现新的功能序列。可用性和实现SuPreMo是用Python编写的，只需一行代码即可运行，生成序列和3D基因组破坏评分。代码库、安装和使用说明以及教程位于GitHub页面：https://github.com/ketringjoni/SuPreMo。
SummaryThe increasing development of sequence-based machine learning models has raised the demand for manipulating sequences for this application. However, existing approaches to edit and evaluate genome sequences using models have limitations, such as incompatibility with structural variants, challenges in identifying responsible sequence perturbations, and the need for vcf file inputs and phased data. To address these bottlenecks, we presentSequence Mutator forPredictiveModels (SuPreMo), a scalable and comprehensive tool for performing and supportingin silicomutagenesis experiments. We then demonstrate how pairs of reference and perturbed sequences can be used with machine learning models to prioritize pathogenic variants or discover new functional sequences.Availability and implementationSuPreMo was written in Python, and can be run using only one line of code to generate both sequences and 3D genome disruption scores. The codebase, instructions for installation and use, and tutorials are on the GitHub page: https://github.com/ketringjoni/SuPreMo.