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庞大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，但它们的巨大可能会使它们的解释在计算上不可行。该项目的目标是为大规模基因组序列数据集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。关键的潜在观察是，目前正在测序的大多数基因组与已经收集的基因组有很多相似之处。因此，新序列信息量的增长速度远远慢于基因组序列数据集的总规模。在最近的工作中，我们提供了一个概念证明，这种冗余可以通过压缩序列数据的方式来利用，从而允许对压缩数据进行直接计算，我们称之为“压缩基因组学”的方法范式。在本提案中，我们将压缩基因组学的框架扩展到几个迫切需要算法进步的应用领域，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，用于压缩表示和大规模下一代测序（NGS）数据集的高效下游分析；这将大大提高现有算法的技术水平和规模

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.