Deep tensor genomic imputation

深度张量基因组插补

• 批准号: 10557916
• 负责人: William Stafford Noble
• 金额: $ 38.38万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10557916
• 关键词: 3-Dimensional; Architecture; Avocado; Awareness; Binding; Biochemical; Biological Assay; Cell Line; Cells; Cellular Assay; Chromatin; Chromatin Interaction Analysis by Paired-End Tag Sequencing; Collection; Complex; Computer software; Couples; DNA; DNA Methylation; DNA Sequence; DNA sequencing; Data; Data Set; Disease; Epigenetic Process; Evaluation; Future; Gene Expression; Genetic Transcription; Genetic Variation; Genome; Genomics; Genotype-Tissue Expression Project; Goals; Health; Hi-C; High-Throughput Nucleotide Sequencing; Human; Individual; Internet; Investigation; Joints; Learning; Machine Learning; Measurement; Measures; Methodology; Methods; Methylation; Modeling; Molecular; Pattern; Positioning Attribute; Process; Property; Regulatory Element; Resolution; Resources; Sampling; Scientist; System; Techniques; Technology; Tissues; Training; United States National Institutes of Health; Untranslated RNA; Validation; Variant; Work; biological systems; cell type; cost; data standards; deep neural network; experimental study; genetic manipulation; genome-wide; genomic data; genomic locus; histone modification; improved; in silico; invention; large datasets; model organism; next generation; open source; predictive modeling; syntax; transcription factor; web portal

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.

项目概要/摘要 高通量测序分析使科学家能够测量转录因子等生化特性 几乎所有细胞系或原代组织（“生物样本”）中的结合、组蛋白修饰和基因表达。 不幸的是，测量每个生物样本中所有可能的生化特性是不可行的，这都是因为 样品供应有限，而且成本过高。我们之前开发了一个状态- 最先进的插补方法，称为 Avocado，可以填补此类数据集中的漏洞。鳄梨夫妇张量 使用深度神经网络进行因式分解。该方法可扩展到大数据集并提供更准确的 插补优于 ChromImpute 或 PREDICTD 等竞争方法。我们已经应用了鳄梨 系统地添加到 NIH ENCODE 数据集，并通过 ENCODE 网络公开提供估算结果 门户网站。 在这里，我们建议以四种重要方式扩展鳄梨。首先，我们将扩展 Avocado 来处理单细胞 数据集，从而有效地将每个单细胞实验转变为可测量多个的计算机联合测定 每个单元的属性并联。其次，我们将扩展 Avocado 以处理 Hi-C 等数据，该数据可测量 DNA 的三维特性。该扩展涉及转换 Avocado 的 3D 张量（生物样本测定 基因组位置）转换为具有两个基因组位置轴的 4D 张量。此扩展将适用于多种 多种数据类型，包括各类Hi-C数据、SPRITE、GAM、ChIA-PET和PLAC-seq。第三，我们将 增强鳄梨以使用变异感知基因组序列来实现监管的高分辨率估算 配置文件。最后，我们将利用估算数据来推断顺式调控序列注释和分子 最全面的细胞环境集合之一中监管非编码变体的影响。 该项目生产的所有软件都将开源，并且所有估算数据和潜在数据 因子分解将通过与 NIH 4D Nucleome 相关的门户网站公开提供， ENCODE Con​​sortia，为这些数据集的用户提供宝贵的公共资源。