CAREER: Towards Fast and Scalable Algorithms for Big Proteogenomics Data Analytics

蛋白质基因组学研究需要蛋白质组学的质谱数据（MS）和基因组学的下一代测序（NGS）数据的结合和集成。这种整合极大地增加了需要进行分析以得出生物学结论的数据集的规模。然而，现有工具的准确性较低，并且对于大型蛋白质基因组学数据的可扩展性较差。这项CAREER资助预计将为快速算法和高性能计算解决方案奠定基础，适用于分析大型蛋白质基因组学数据集。设计适合于千万亿级数据集的精确计算算法将被追求，软件实现将在大规模并行超级计算机和图形处理单元上运行。在这个CAREER提案的方向是设计和建造基础设施，这将对最广泛的生物和生态社区有用。为确保美国在STEM领域保持全球领先地位，美国将对K12、本科生和研究生实施全面的跨学科教育。因此，这个项目符合国家利益，正如NSF的使命所述：促进科学的进步，促进国家的健康、繁荣和福利。拟议的CAREER拨款的目标是设计和开发算法和高性能计算（HPC）基础，用于大型蛋白质基因组学数据的实用亚线性和并行算法-特别是具有先前未测序或部分测序基因组的非模式生物。蛋白质基因组学研究所需的MS和NGS数据集的集成显示出巨大的数据量和速度：NGS技术如Chip-Seq可以生成万亿字节的DNA/RNA数据，质谱仪可以生成数百万个光谱（每个光谱有数千个峰）。目前用于分析MS数据的系统主要是由启发式实践驱动的，并且不能很好地扩展。该CAREER提案将探索一类新的简化算法，用于分析MS数据，可以在亚线性时间内进行肽段推导，在亚线性空间中操作的压缩算法，以及在MS数据的有损简化形式上操作的denovo算法。新颖的低复杂度采样和约简算法可以利用MS数据的稀疏性，例如基于非均匀FFT的卷积核，可以产生不容易产生虚假相关性的优越相似性度量。大型系统生物学研究的瓶颈是粗粒度并行算法的低可扩展性，这些算法不能利用ms特定的数据特征，并且由于肽推导所需的计算时间不均匀而导致负载不平衡。该项目旨在探索多核和GPU平台上NGS和MS数据的可扩展算法的设计和实现，使用基于谱聚类的领域分解技术、基于工作量估计的MS特定混合负载平衡、HPC降维策略和新颖的核外草图和流细粒度并行算法。这些高性能计算解决方案可以使以前不切实际的蛋白质基因组学项目成为可能，并允许生物学家在不需要昂贵硬件的情况下进行计算实验。所有实现的算法都将作为与Galaxy框架接口的开源代码提供，以确保在系统生物学实验室中产生最大的影响。然后将这些设计的技术整合起来，以便无需重建转录组就可以完成光谱与RNA-Seq数据的匹配。提出的工具旨在揭示新的生物学见解，如新的基因、蛋白质和PTM，是理解生命之树中物种的基因组、蛋白质组学和进化方面的关键步骤。

Proteogenomics studies require combination and integration of mass spectrometry data (MS) for proteomics and next generation sequencing (NGS) data for genomics. This integration drastically increases the size of the data sets that need to be analyzed to make biological conclusions. However, existing tools yield low accuracy and exhibit poor scalability for big proteogenomics data. This CAREER grant is expected to lay a foundation for fast algorithmic and high performance computing solutions suitable for analyzing big proteogenomics data sets. Design of accurate computational algorithms suitable for peta-scale data sets will be pursued and the software implementation will run on massively parallel supercomputers and graphical processing units. The direction in this CAREER proposal is towards designing and building infrastructure, which would be useful for the broadest biological and ecological community. A comprehensive interdisciplinary education will be executed for K12, undergraduate and graduate students to ensure that US retains its global leadership position in STEM fields. This project thus serves the national interest, as stated by NSF's mission: to promote the progress of science and to advance the national health, prosperity and welfare.The goal of the proposed CAREER grant is to design and develop algorithmic and high performance computing (HPC) foundations for practical sublinear and parallel algorithms for big proteogenomics data - especially for non-model organisms with previously unsequenced or partially sequenced genomes. Integration of MS and NGS data sets required for proteogenomics studies exhibit enormous volume and velocity of data: NGS technologies such as Chip-Seq can generate tera-bytes of DNA/RNA data and mass spectrometers can generate millions of spectra (with thousand of peak per spectra). The current systems for analyzing MS data are mainly driven by heuristic practices and do not scale well. This CAREER proposal will explore a new class of reductive algorithms for analysis of MS data that can allow peptide deductions in sublinear time, compression algorithms that operate in sub-linear space, and denovo algorithms that operate on lossy reduced-form of the MS data. Novel low-complexity sampling and reductive algorithms that can exploit the sparsity of MS data such as non-uniform FFT based convolution kernels can lead to superior similarity metrics not prone to spurious correlations. The bottleneck in large system-biology studies is the low-scalability of coarse-grained parallel algorithms that do not exploit MS-specific data characteristics and lead to unbalanced loads due to non-uniform compute time required for peptide deductions. This project aims to explore design and implementation of scalable algorithms for both NGS and MS data on multicore and GPU platforms using domain decomposition techniques based on spectral clustering, MS-specific hybrid load-balancing based on work-load estimate, and HPC dimensionality reduction strategies and novel out-of-core sketching & streaming fine-grained parallel algorithms. These HPC solutions can enable previously impractical proteogenomics projects and allow biologists to perform computational experiments without needing expensive hardware. All of the implemented algorithms will be made available as open-source code interfaced with Galaxy framework to ensure maximum impact in systems biology labs. These designed techniques will then be integrated so that matching of spectra to RNA-Seq data can be accomplished without a reconstructed transcriptome. The proposed tools aim to reveal new biological insight such as novel genes, proteins and PTM's and are crucial steps towards understanding the genomic, proteomic and evolutionary aspects of species in the tree of life.