Measuring functional similarity between transcriptional enhancers using deep learning

项目总结 了解转录调控仍然是分子生物学fi领域的一项主要任务。增强剂有 调节基因何时何地表达及其表达水平的遗传因素。这些元素 很难发现，因为它们的位置和方向不受其目标基因的限制。 一些疾病和对某些疾病的易感性与增强剂的突变和变异有关。多重 已经开发了用于定位增强剂的实验和计算方法。计算方法 更适合处理现在正在测序的大量基因组，因为它们更快，更便宜， 而且与实验方法相比，劳动强度更低。尽管有许多可用的计算工具，但我们缺乏 一种复杂的工具，可以测量一对序列的增强子活性的相似性。我们在这里求婚 利用深层人工神经网络(DANS)来开发这样的工具。这个项目的长期目标是 为了破译控制基因调控的密码，fic的目标如下：(I)设计一个计算工具 测量增强子-增强子相似性，(Ii)通过实验验证最多96个假定的增强子，(Iii)了解 增强子语法，以及(Iv)注释50多个昆虫基因组中的增强子。为了实现这些目标，一部小说 提出了DANS的应用前景。当前的工具利用DAN来回答一个是-否的问题：序列 是否具有与由特定训练集的已知增强剂组成的组织特异性fic增强剂类似的活性？ 这些方法需要在每个组织上训练单独的网络，导致在 不同的组织。相反，这里我们使用Dann来回答一个相关但不同的问题：这个序列 是否具有与单一已知组织特异性fic增强子相似的增强子活性？这个深度网络应该会执行 在不同的细胞类型上一致，因为它是在成对的序列上训练的-而不是像 在可用的工具中的情况-代表所有已知的增强剂的组织。丹人是受过训练的 识别具有相似增强子活性的序列对和具有不同活性的序列对，包括(I)两个 在两个不同组织中活性的增强剂，(Ii)一个增强子和随机基因组序列，以及(Iii)两个随机序列 基因组序列。该工具输出的分数介于0和1之间，表示增强器活动的相似性程度 两个序列中的一个是。使用比DANS简单得多的机器学习算法，我们演示了 具有相似增强子活性的对可以从随机基因组序列对或对 一个增强子和一个具有非常高准确度的随机基因组序列。这个新工具有许多重要的 潜在的应用包括跨细胞类型和相关物种的增强剂的一致注释。我们的工具 可以注释在具有少量已知增强子的细胞类型中活动的增强子，并且它可以注释 在相关基因组中存在一组已知的增强子时，在其中一个基因组中划定增强子。发现 新的转录因子结合位点是另一个潜在的应用。研究增强剂的“设计原则”和 可以使用所提议的工具来促进变体的效果。这样的应用将推进我们的fi领域。

PROJECT SUMMARY Understanding transcriptional regulation remains as a major task in the molecular biology field. Enhancers are genetic elements that regulate when and where genes are expressed and their expression levels. These elements are hard to discover because their locations and orientations are not constrained with respect to their target genes. Several diseases and susceptibility to certain diseases are linked to mutations and variants in enhancers. Multiple experimental and computational methods have been developed for locating enhancers. Computational methods are more suitable to handle the large number of genomes being sequenced now because they are faster, cheaper, and less labor intensive than experimental methods. Despite many available computational tools, we lack a sophisticated tool that can measure similarity in the enhancer activity of a pair of sequences. We propose here utilizing Deep Artificial Neural Networks (DANNs) to develop such a tool. The long-term objective of this project is to decipher the code governing gene regulation with the following specific aims: (i) design a computational tool for measuring enhancer-enhancer similarity, (ii) validate up to 96 putative enhancers experimentally, (iii) understand enhancer grammar, and (iv) annotate enhancers in more than 50 insect genomes. To achieve these aims, a novel application of DANNs is proposed. Current tools utilize DANNs to answer a yes-no question: does a sequence have similar activity to the tissue-specific enhancers comprising a particular training set of known enhancers? These approaches require training a separate network on each tissue, leading to inconsistent performances on different tissues. Instead, here we use a DANN to answer a related but different question: does this sequence have similar enhancer activity to a single known tissue-specific enhancer? This deep network should perform consistently on different cell types because it is trained on pairs of sequences — not individual sequences as is the case in the available tools — representing all tissues for which there are known enhancers. The DANN is trained to recognize sequence pairs with similar enhancer activities and those with dissimilar activities including (i) two enhancers active in two different tissues, (ii) one enhancer and a random genomic sequence, and (iii) two random genomic sequences. The tool outputs a score between 0 and 1, indicating how similar the enhancer activities of the two sequences are. Using a much simpler machine learning algorithm than DANNs, we demonstrate that pairs with similar enhancer activities can be separated from pairs of random genomic sequences or pairs of one enhancer and a random genomic sequence with a very high accuracy. The new tool has many important potential applications including consistent annotation of enhancers across cell types and related species. Our tool can annotate enhancers active in a cell type that has a small number of known enhancers, and it can annotate enhancers in related genomes when there is a set of known enhancers demarcated in one of them. Discovering new transcription factor binding sites is another potential application. Studying enhancer “design principles” and the effects of variants can be facilitated using the proposed tool. Such applications will advance our field.