Compressive Genomics for Large Omics Data Sets: Algorithms, Applications and Tools

大型组学数据集的压缩基因组学：算法、应用程序和工具

• 批准号: 9546755
• 负责人: BONNIE BERGER
• 金额: $ 35.02万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-05 至 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9546755
• 关键词: Acceleration; Address; Adoption; Age; Algorithms; Autistic Disorder; Autoimmune Diseases; Bioinformatics; Biological; Biological Process; Biology; Biomedical Research; Clinical; Cloud Computing; Cohort Studies; Collaborations; Communities; Complex; Computer software; Computers; Computing Methodologies; DNA sequencing; Data; Data Compression; Data Files; Data Set; Development; Dimensions; Disease; Ensure; Exhibits; Foundations; Fractals; Genetic Variation; Genome; Genomics; Goals; Grant; Health; Human; Indiana; Individual; Industry; Informatics; Intuition; Investigation; Mainstreaming; Malignant Neoplasms; Maps; Metagenomics; Methodology; Molecular; New England; Patients; Pattern; Pharmacogenomics; Privacy; Process; Progress Reports; Research; Research Personnel; Savings; Secure; Security; Sequence Analysis; Structure; Techniques; Technology; The Cancer Genome Atlas; Therapeutic; Time; Transcript; Universities; Variant; Work; autism spectrum disorder; base; cloud platform; cohort; computer framework; computerized tools; cryptography; data exchange; data structure; design; encryption; file format; genomic data; human DNA; human RNA sequencing; human data; human disease; improved; innovation; insight; interest; microbiome; microbiome therapeutics; monomethoxypolyethylene glycol; next generation sequencing; novel; software development; tool; transmission process; whole genome

Project Summary    High-throughput experimental technologies are generating increasingly massive and complex genomic sequence data sets. While these data hold the promise of uncovering entirely new biology, their sheer enormity threatens to make their interpretation computationally infeasible. The continued goal of this project is to design and develop innovative compression-based algorithmic techniques for efficiently processing massive biological data. We will branch out beyond compressive search to address the imminent need to securely store and process large-scale genomic data in the cloud, as well as to gain insights from massive metagenomic data.     The key underlying observation is that genomic data is highly structured, exhibiting high degrees of self-similarity. In our previous granting period, we exploited its high redundancy and low fractal dimension to enable scalable compressive storage and acceleration for search of sequence data as well as other biological data types relevant to structural bioinformatics and chemogenomics. In this renewal, we will continue to capitalize on the structure (i.e., compressibility) of genomic data to: (i) overcome privacy concerns that arise in sharing sensitive human data (e.g. on the cloud); (ii) address new challenges, beyond search, with metagenomic data; and (iii) seek to widen the adoption of the previous and newly-proposed compressive algorithms for industry, research, and clinical use. We will demonstrate the utility of our compressive techniques to the characterization of human genomic and metagenomic variation.   We will collaborate with co-I Sahinalp's lab (Indiana University, Bloomington) on developing and applying these tools to high-throughput data sets including autism spectrum disorder (with Isaac Kohane and Evan Eichler) and cancer (with PCAWG, Pan Cancer Analysis of Whole Genomes), the microbiome (with Eric Alm and Jian Peng), as well as human variation analysis (GATK, with Eric Lander and Eric Banks). The broad, long-term goal is to apply our compressive approach to massive biological data sets to elucidate the still obscure molecular landscape of diseases.    Successful completion of these aims will result in computational methods and tools that will significantly increase our ability to securely store, access and analyze massive data sets and will reveal fundamental aspects of genetic variation, as well as testable hypotheses for experimental investigations. Not only will all developed software be made publicly available, but as part of our integration aim, we will also ensure that the research community can make use of our innovations with minimal effort. Through our research collaborations, we will both build these tools and demonstrate their relevance to the characterization of human health and disease.

项目摘要   高通量实验技术正在产生越来越大的质量和复杂的基因组 序列数据集。尽管这些数据有望发现全新的生物学，但他们的纯粹 巨大的威胁使他们的解释在计算上是不可行的。持续的目标 项目是设计和开发基于创新的压缩算法技术，以有效地 处理大量的生物学数据。我们将超越压缩搜索，以解决 迫在眉睫的需要在云中安全地存储和处理大规模基因组数据，并获得 大量宏基因组数据的见解。     关键的基础观察是基因组数据是高度结构化的，表现出高度的 自相似。在以前的授予期内，我们探索了其高冗余性和低分形 维度以启用可扩展的压缩存储和加速度以搜索序列数据作为 以及其他与结构生物信息学和化学组学有关的生物数据类型。在这个 更新，我们将继续利用基因组数据的结构（即补充性）到：（i） 克服共享敏感人类数据（例如在云上）时出现的隐私问题； （ii）地址 带有宏基因组数据的新挑战，超越搜索； （iii）试图扩大采用 用于行业，研究和临床用途的以前和新提供的压缩算法。我们将 证明我们的压缩技术对人类基因组的表征和 宏基因组变异。   我们将与Co-I Sahinalp的实验室（布卢明顿印第安纳大学）合作开发和 将这些工具应用于包括自闭症谱系障碍在内的高通量数据集（与以撒有关 Kohane和Evan Eichler）和癌症（与PCAWG，PAN癌的整个基因组分析）， 微生物组（与Eric Alm和Jian Peng）以及人类变异分析（GATK，ERIC Lander和Eric Banks）。广泛的长期目标是将我们的压缩方法应用于 大量的生物数据集，以阐明疾病的分子景观仍然晦涩。   这些目标成功完成将导致计算方法和工具 提高我们安全存储，访问和分析大量数据集的能力，并将揭示 遗传变异的基本方面以及实验的可检验假设 投资。不仅可以使所有开发的软件公开可用，而且还可以作为我们的一部分 整合目标，我们还将确保研究界可以利用我们的创新 最小的努力。通过我们的研究合作，我们将构建这些工具并证明 它们与人类健康和疾病的特征相关。