Interpretable Computational Models of Functional Genomics Data

项目总结 了解顺式调控元件(CRE)的协调如何影响生物过程，如 转录和选择性剪接，是计算基因组学的一个主要目标。这仍然是一个挑战。 因为任何给定基因座上的Cre活性可能取决于许多其他因素，包括序列上下文 和/或附近存在其他CRE。深卷积神经网络(CNN)的研究进展 彻底改变了我们从DNA序列预测调控功能的能力。与以前的计算不同 方法基于捕获Cres的相加模型的位置-权重矩阵，CNN原则上可以， 还可以学习CRE内的高阶依赖关系、与其他CRE的依赖关系以及与更广泛的序列上下文的依赖关系。 然而，CNN本质上是黑盒模型，其参数没有明确的生物学意义。 因此，将CNN改进后的预测转化为新的生物学见解仍然是一个挑战。在这里我们 建议开发三种不同的计算方法来全面刻画高阶 来自功能基因组学数据的CRE内部和不同CRE之间的相互作用，特别是芯片序列和 通过ENCODE公开提供的CLIP-SEQ数据。每种方法都有其各自的目标，并将 并行发展的。在目标1中，我们将开发一种新的后自组织模型可解释性方法，该方法基于 可解释的定量模型最初是为了了解实验室中复杂的遗传相互作用而开发的- 基于综合诱变(例如，不同效应的多重分析)来表征Cre依赖性 由CNN学习，使用合成序列来瞄准特定的生物假说。在目标2中，我们将开发 新的CNN架构，其中学习的参数将表示具有直接 生物学的解释。在目标3中，我们将结合贝叶斯非参数框架来建模CRES 使用基于CNN的CRE注释和GPU加速，开发新的方法来了解CRE是如何 都是在基因组中指定的。这些目标的成功实现将使我们的 了解已被利用但尚未完全揭示的高阶CRE依赖关系 CNN。这项工作将为社区提供：(1)一套新的开源计算工具， 解决在功能基因组数据中对CRE及其相关性进行建模的问题；以及(2) 转录因子和RNA结合蛋白的Cre语法的全基因组目录 将托管在用户友好的网络服务器上。

PROJECT SUMMARY Understanding how the coordination of cis-regulatory elements (CREs) influences biological processes, such as transcription and alternative splicing, is a major goal in computational genomics. This remains a challenge because CRE activity at any given locus may depend on a host of other factors, including sequence context and/or the presence of other CREs nearby. Recent developments in deep convolutional neural networks (CNNs) have revolutionized our ability to predict regulatory functions from DNA sequence. Unlike previous computational methods based on position-weight matrices, which capture an additive model of CREs, CNNs can, in principle, also learn higher-order dependencies within the CRE, with other CREs, and with the broader sequence context. However, CNNs are essentially black box models, with parameters that don’t have clear biological meaning. Hence it remains a challenge to translate the improved predictions of a CNN to new biological insights. Here we propose to develop three different computational methods that can comprehensively characterize higher-order interactions within CREs and across different CREs from functional genomics data, specifically ChIP-seq and CLIP-seq data publicly available through ENCODE. Each method serves as its own separate Aim and will be developed in parallel. In Aim 1, we will develop a new post hoc model interpretability method based on employing interpretable quantitative models originally developed to understand complex genetic interactions in laboratory- based comprehensive mutagenesis (e.g. multiplex assays of variant effects) to characterize CRE dependencies learned by a CNN, using synthetic sequences to target specific biological hypotheses. In Aim 2, we will develop new CNN architectures where the learned parameters will express higher-order interactions that have direct biological interpretations. In Aim 3, we will combine a Bayesian nonparametric framework for modeling CREs with CNN-based CRE annotations and GPU acceleration to develop new methods for understanding how CREs are specified in the genome. Successful completion of these Aims will provide a leap forward in our understanding of higher-order CRE dependencies that are exploited but have not yet been fully revealed by CNNs. This work will provide the community with: (1) a new suite of open-source computational tools that address the problem of modeling CREs and their dependencies in functional genomics data; and (2) a comprehensive genome-wide catalogue of CRE syntax for transcription factors and RNA-binding proteins that will be hosted on a user-friendly webserver.