Deep tensor genomic imputation

深度张量基因组插补

• 批准号: 10096947
• 负责人: William Stafford Noble
• 金额: $ 39.86万
• 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/10096947
• 关键词: 3-Dimensional; Animal Model; 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; Gold; 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; large datasets; 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.

项目总结/文摘