Predictive Modeling of Alternative Splicing and Polyadenylation from Millions of Random Sequences

在人类基因组中，基因调控所占的比例远远超过了编码 proteins.然而，我们仍然没有足够的能力来确定哪些遗传变异损害基因， 可能导致罕见和常见人类疾病风险的方式。 了解非编码序列如何调节基因表达，以及能够预测 遗传变异对基因调控的功能性后果是该领域的最大挑战。在这里， 我们建议将联合收割机合成生物学、大规模并行功能分析和机器学习结合起来， 深刻推进我们对人类基因组“调控密码”的理解。虽然具有挑战性， 从大量经验数据中解开复杂代码的任务并非没有先例。比如说， 在过去的十年里，从事自然语言处理的计算机科学家们取得了巨大的成就。 进步，在很大程度上是由算法和计算改进的结合驱动的， 比前几代科学家可用的训练数据集要大得多。 区受“大数据”对机器学习中传统问题的革命性影响的启发，我们 我建议使用多几个数量级的训练数据集来模拟基因调控现象 比自然存在于人类基因组中的例子。我们预测，从大规模学习的模型 大量的合成样本将大大优于从少量自然样本中学习到的模型。 例子.我们将通过开发全面的、定量的和可预测的 选择性剪接和选择性多聚腺苷酸化的模型，这两种广泛的调节机制， 单个基因可以编码多种转录物和蛋白质。然而，我们预计， 范式-特别是，极大的功能行为的大规模并行测量 许多合成序列，然后是序列-功能关系的定量建模-可以是 推广到推进我们对不同形式的基因调控的理解。

The proportion of the human genome that underlies gene regulation dwarfs the proportion that encodes proteins. However, we remain poorly equipped for identifying which genetic variants compromise gene regulatory function in ways that may contribute to risk for both rare and common human diseases. Understanding how non-coding sequences regulate gene expression, as well as being able to predict the functional consequences of genetic variation for gene regulation, are paramount challenges for the field. Here, we propose to combine synthetic biology, massively parallel functional assays, and machine learning to profoundly advance our understanding of the `regulatory code' of the human genome. While challenging, the task of unravelling complex codes from large amounts of empirical data is not without precedent. For example, over the past decade, computer scientists working in natural language processing have made immense progress, driven in large part by a combination of algorithmic and computational improvements and enormously larger training datasets than were available to the previous generations of scientists working in this area. Inspired by the revolutionizing impact of “big data” for traditional problems in machine learning, we propose to model gene regulatory phenomena using training datasets with several orders of magnitude more examples than naturally exist in the human genome. We predict that the models learned from massive numbers of synthetic examples will strongly outperform models learned from the small number of natural examples. We will demonstrate our approach by developing comprehensive, quantitative, and predictive models for alternative splicing and alternative polyadenylation, two widespread regulatory mechanisms by which a single gene can code for multiple transcripts and proteins. However, we anticipate that this basic paradigm – specifically, the massively parallel measurement of the functional behavior of extremely large numbers of synthetic sequences followed by quantitative modeling of sequence-function relationships – can be generalized to advance our understanding of diverse forms of gene regulation.