Deep tensor genomic imputation

项目概要/摘要 高通量测序分析使科学家能够测量生物化学特性，如转录因子 结合，组蛋白修饰和基因表达在几乎任何细胞系或原代组织（“生物样品”）。 不幸的是，测量每个生物样品中所有可能的生化特性是不可行的，这既是因为 有限的样品可用性，因为成本过高。我们以前开发了一个国家- 最先进的估算方法，称为鳄梨，可以填补这些数据集中的漏洞。鳄梨耦合张量 使用深度神经网络进行分解。该方法可扩展到大型数据集，并提供更准确的 与ChromImpute或PREDICTD等竞争方法相比，我们已经应用了鳄梨 系统地与NIH ENCODE数据集进行比较，并通过ENCODE网站公开提供估算结果 por tal. 在这里，我们建议以四种重要的方式扩展Avocado。首先，我们将扩展Avocado以处理单细胞 数据集，从而有效地将每个单细胞实验转化为计算机辅助测定， 每个单元格的属性并行。其次，我们将扩展Avocado，使其能够处理Hi-C等数据， DNA的三维特性。该扩展涉及将Avocado的3D张量（生物样品测定） 基因组位置）到具有两个基因组位置轴的4D张量。这一扩展将适用于各种 数据类型，包括各种类型的Hi-C数据、SPRITE、GAM、ChIA-PET和PLAC-seq。三是 增强鳄梨使用变异感知基因组序列，以实现高分辨率的调控 profiles.最后，我们将利用估算的数据来推断顺式调控序列注释和分子生物学特性。 在最全面的细胞环境集合之一的调控非编码变体的影响。 该项目产生的所有软件都将是开源的，所有的估算数据和潜在的 因子分解将通过与NIH 4D Nucleome相关的门户网站公开提供， ENCODE Consortia，为这些数据集的用户提供了宝贵的公共资源。

Project Summary/Abstract High-throughput sequencing assays allow scientists to measure biochemical properties like transcription factor binding, histone modifications, and gene expression in nearly any cell line or primary tissue (“biosample”). Unfortunately, measuring all possible biochemical properties in every biosample is infeasible, both because of limited sample availability and because the cost would be prohibitive. We have previously developed a state-of- the-art imputation method, called Avocado, that can fill in the holes in such data sets. Avocado couples tensor factorization with a deep neural network. The method is scalable to large data sets and provides more accurate imputations than competing methods such as ChromImpute or PREDICTD. We have already applied Avocado systematically to the NIH ENCODE data set and made the imputations publicly available via the ENCODE web por tal. Here, we propose to extend Avocado in four important ways. First, we will extend Avocado to handle single-cell data sets, thereby effectively turning each single-cell experiment into an in silico co-assay that measures multiple properties of each cell in parallel. Second, we will extend Avocado to work with data such as Hi-C, which measures three-dimensional properties of DNA. The extension involves converting Avocado's 3D tensor (biosample assay genomic position) to a 4D tensor with two genomic position axes. This extension will apply to a wide variety of data types, including various types of Hi-C data, SPRITE, GAM, ChIA-PET and PLAC-seq. Third, we will enhance Avocado to use variant aware genomic sequence to enable high-resolution imputation of regulatory profiles. Finally, we will leverage the imputed data to infer cis-regulatory sequence annotations and the molecular impact of regulatory non-coding variants in one of the most comprehensive collections of cellular contexts. All of the software produced by this project will be open source, and all of the imputed data and latent factorizations will be made publicly available via the web portals associated with the NIH 4D Nucleome and ENCODE Consortia, providing a valuable public resource for users of these data sets.