Compressive genomics for large omics data sets: Algorithms applications & tools

描述（由申请人提供）：高通量实验技术正在产生越来越大规模和复杂的基因组序列数据集。虽然这些数据有希望揭示全新的生物学，但它们的绝对相似性可能会使它们的解释在计算上变得不可行。该项目的目标是设计和开发创新的基于压缩的算法技术和公开可用的软件，用于大规模基因组序列数据集。关键的基本观察是，目前正在测序的大多数基因组与已经收集的基因组有很大的相似性。因此，新序列信息的量的增长比基因组序列数据集的总大小慢得多。在最近的工作中，我们已经提供了一个概念验证，即可以通过压缩序列数据来利用这种冗余，以便允许对压缩数据进行直接计算，这是一种我们称之为“压缩基因组学”的方法学范式。“在这项提案中，我们将压缩基因组学的框架扩展到几个额外的应用领域，这些领域迫切需要算法的进步，以跟上基因组和蛋白质测序数据的增长。特别是，我们将建立一个新的综合框架，用于大规模下一代测序（NGS）数据集的压缩表示和高效下游分析;这将显著推进现有算法的最新技术水平和规模， 基因组数据的增长，从而满足预期的未来测序技术加速的挑战。此外，我们将开发先进的压缩加速算法和软件，用于生物信息学中当前感兴趣的特定应用，并将其应用于真实的大规模“组学”数据集，以加速数据分析并导致新的生物学发现。也就是说，我们将与Kohane实验室合作分析神经发育障碍患者的高通量基因表达和NGS数据集，包括自闭症谱系障碍和帕金森氏症;广泛的长期目标是将我们的压缩方法应用于如此庞大的数据集，以阐明这些疾病仍然模糊的分子景观。了解来自患者的大量“组学”数据将使合理的、有针对性的药物设计和更智能的疾病管理成为可能，但它们的纯粹一致性可能会使所出现的问题在计算上变得不可行。在这里，我们开发的计算方法和工具，将从根本上推进国家的最先进的存储，检索和分析这些迅速扩大的数据集。

DESCRIPTION (provided by applicant): 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 goal of this project is to design and develop innovative compression-based algorithmic techniques and publicly-available software for large-scale genomic sequence data sets. The key underlying observation is that most genomes currently being sequenced share much similarity with genomes that have already been collected. Thus, the amount of new sequence information is growing much more slowly than the total size of genomic sequence data sets. In very recent work, we have provided a proof-of-concept that this redundancy can be exploited by compressing sequence data in such a way as to allow direct computation on the compressed data, a methodological paradigm we term "compressive genomics." In this proposal we broaden the framework of compressive genomics to several additional application areas in which algorithmic advances are urgently needed in order to keep pace with the growth in both genomic and protein sequencing data. In particular, we will build a novel comprehensive framework for compressive representation and highly efficient downstream analysis of large-scale next-generation sequencing (NGS) data sets; this will significantly advance the state of the art and scale over existing algorithms as the volume of genomic data grows, thus meeting the challenge of the expected future acceleration of sequencing technologies. Additionally, we will develop advanced, compressively-accelerated algorithms and software for specific applications of current interest in bioinformatics and apply them to real large-scale 'omics' data sets to accelerate data analytics and lead to novel biological discoveries. Namely, we will collaborate with the Kohane lab on analysis of high-throughput gene expression and NGS data sets from patients with neurodevelopmental disorders, including Autism Spectrum Disorder and Parkinson's; the broad, long-term goal is to apply our compressive approach to such massive data sets to elucidate the still obscure molecular landscape of these diseases. Understanding massive 'omics' data from patients will empower both rational, targeted drug design and more intelligent disease management, yet their sheer enormity threatens to make the arising problems computationally infeasible. Here, we develop computational methods and tools that will fundamentally advance the state-of-the-art in storage, retrieval and analysis of these rapidly expanding data sets.