Efficient Data Structures and Algorithms for Genomics Sequence Data

基因组序列数据的高效数据结构和算法

• 批准号: 7987503
• 负责人: Pierre Baldi
• 金额: $ 17.69万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-30 至 2013-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7987503
• 关键词: Accounting; Address; Algorithms; Automobile Driving; Bacteria; Base Sequence; Binding Sites; Bioinformatics; Biological Sciences; Biology; Collaborations; Communication; Computer software; Computing Methodologies; Copy Number Polymorphism; DNA Binding; DNA Resequencing; Data; Data Compression; Data Set; Databases; Detection; Diploidy; Evolution; Foundations; Future; Genbank; Gene Expression; Gene Expression Regulation; Genetic; Genome; Genomics; Human; Human Genome; Individual; Internet; Life; Measures; Medicine; Messenger RNA; Methods; Microarray Analysis; Mission; Organism; Privacy; Process; Protocols documentation; Publications; Scientist; Secure; Security; Single Nucleotide Polymorphism; Small RNA; Solid; Structure; Technology; Testing; Text; Transcription factor genes; Virus; Yeasts; digital; driving force; encryption; file format; genome sequencing; man; mitochondrial genome; research study; scale up; tool; transmission process; trend

DESCRIPTION (provided by applicant): The long-term objective of this project is to develop and deploy efficient data structures and algorithms for the storage, transmission, querying, privacy protection, and management of large-scale High-Throughput Sequencing (HTS) and genomic information. HTS technologies are in the process of driving profound, revolutionary, changes in biology and medicine. They are becoming the tool of choice for addressing fundamental questions in biology, from evolution to gene regulation, and for providing the foundation to the personalized medicine of tomorrow, as it becomes possible to cheaply resequence individual genomes. A project to sequence 1,000 human genomes is well underway and soon it will be possible to sequence a human genome for less than $1,000. In addition to the obvious challenges to understand the structure, function, and evolution of genomes, modern high-throughput genome sequencing methods also raise questions about how to efficiently represent, store, transmit, query, and protect the privacy of genomic sequence information. Currently, HTS and genome data are typically stored using a flat-text file format which is inefficient not only in terms of storage capacity and communication bandwidth, but also in terms of information extraction and security. The proposed effort aims at removing this fundamental bottleneck and address the genomic data deluge by: (1) Developing efficient data structures and compression algorithms for HTS and genomic data that support also rapid extraction and protection of genomic information, with compression factors for genomic data in the range of 1,000 and beyond; (2) Developing security and privacy preserving algorithms and protocols to protect genomic data; (3) Implementing and testing these data structures, protocols, and algorithms on a variety of data including HTS data from different HTS technologies (e.g. Solexa, SOLiD, 454), individual human mitochondrial genome data (e.g. the MITOMAP database), individual human SNP data (e.g. dbSNP), and individual human genome data (e.g. The 1000 Genome Project); and (3) Validating and deploying the technology through multiple channels, from publications, to Web servers, to distribution of optimized software, to collaborations with life- scientists, HTS companies, and large sequencing centers.

描述（由申请人提供）：该项目的长期目标是开发和部署有效的数据结构和算法，用于存储，传输，查询，隐私保护以及管理大型高通量测序（HTS）和基因组信息。 HTS技术正在推动深刻，革命性的生物学和医学变化。它们正在成为解决生物学基本问题，从进化到基因调节的首选工具，并为明天的个性化医学奠定了基础，因为有可能便宜地重新恢复个体基因组。一个对1,000个人类基因组进行序列的项目正在进行中，很快就有可能以不到1,000美元的价格对人类基因组进行测序。除了了解基因组的结构，功能和演变所面临的明显挑战外，现代的高通量基因组测序方法还引发了有关如何有效代表，存储，传输，查询和保护基因组序列信息隐私的问题。当前，HTS和基因组数据通常是使用平面文件格式存储的，该格式不仅在存储容量和通信带宽方面效率低下，而且在信息提取和安全性方面也是如此。提出的努力旨在消除这种基本瓶颈，并通过以下方式解决基因组数据洪水量：（1）为HTS开发有效的数据结构和压缩算法，以及支持基因组信息的快速提取和保护基因组信息的基因组数据，并具有1,000和超越范围内基因组数据的压缩因子； （2）开发保存算法和协议以保护基因组数据的安全性和隐私性； （3）在各种数据上实施和测试这些数据结构，协议和算法，包括来自不同HTS技术的HTS数据（例如Solexa，Solid，454），单个人类人体线粒体基因组数据（例如，MITOMAP数据库），个体人类SNP数据（例如，人类SNP数据）（例如，人类dbsnp）和个人基因组合（E. （3）通过多个渠道验证和部署技术，从出版物到网络服务器，再到优化软件的分发，再到与生活科学家，HTS公司和大型测序中心的合作。