Interpretable Computational Models of Functional Genomics Data

项目摘要 了解顺式调节元件（克雷斯）的协调如何影响生物过程，例如 转录和可变剪接是计算基因组学的主要目标。这仍然是一个挑战 因为CRE在任何给定位点的活性可能取决于许多其他因素，包括序列背景 和/或附近存在其他克雷斯。深度卷积神经网络（CNN）的最新发展 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， 基于位置权重矩阵的方法，其捕获克雷斯的加性模型，原则上， 还可以学习CRE内的高阶依赖关系，与其他克雷斯以及更广泛的序列上下文。 然而，CNN本质上是黑箱模型，其参数没有明确的生物学意义。 因此，将CNN的改进预测转化为新的生物学见解仍然是一个挑战。这里我们 建议开发三种不同的计算方法，可以全面表征高阶 来自功能基因组学数据的克雷斯内和不同克雷斯之间的相互作用，特别是ChIP-seq和 CLIP-seq数据可通过ENCODE公开获得。每种方法都有自己的目标， 平行发展。在目标1中，我们将开发一种新的事后模型可解释性方法， 可解释的定量模型最初是为了理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，变异效应的多重测定）来表征CRE依赖性 通过CNN学习，使用合成序列来针对特定的生物学假设。在目标2中，我们将开发 新的CNN架构，其中学习的参数将表达具有直接关系的高阶相互作用。 生物学诠释在目标3中，我们将结合联合收割机的贝叶斯非参数框架建模克雷斯 使用基于CNN的CRE注释和GPU加速，开发新方法来了解克雷斯如何 在基因组中被指定。这些目标的成功实现将使我们的 了解高阶CRE依赖关系，这些依赖关系被利用，但尚未完全揭示， CNN这项工作将为社区提供：（1）一套新的开源计算工具， 解决在功能基因组学数据中对克雷斯及其依赖性建模的问题;以及（2） 转录因子和RNA结合蛋白的CRE语法的全面的全基因组目录， 将被托管在一个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.