Bioinformatics-Compute-Server

生物信息计算服务器

• 批准号: 422216132
• 金额: --
• 依托单位: Technische Universität München (TUM)
• 依托单位国家: 德国
• 项目类别: Major Research Instrumentation
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 无数据
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/422216132?language=en
• 关键词: Bioinformatics; Compute; Server

We develop innovative bioinformatics approaches for applications in modern molecular biology, biomedicine and systems/network medicine. We require compute hardware allowing for storing and analyzing massive so-called omics data. Our aim is to extract new knowledge and to generate novel hypothesis, such as mechanistic biomarkers. We employ advanced bid data technologies as well as artificial intelligence and machine learning. Our research focus lies on the combined analysis of multi-scale omics data (e.g. transcriptomics and epigenomics) in spatiotemporal resolution together with molecular signaling pathways. The later are modeled as graphs with nodes and edges between them. We develop innovative heuristic methods for high-throughput omics data (terabytes of sequence and spectrometry data) and genome-scale networks (with thousands of nodes and hundreds of thousands of edges) to deliver systems (bio)medicine results and targets. Those are used afterwards as mechanistic biomarkers and may help, for instance in medicine, to identify novel drug targets, to suggest drug repurposing candidates, and to find synergistic mechanisms of multiple drugs (network pharmacology). This requires a local highly parallel compute server infrastructure with many processor cores (>48) and big main memory (>256 GB), which can be accessed concurrently by all compute cores. The typical services offered, for instance, by our Leibniz Rechenzentrum, do not cover this specific area. However, such a system is the only way to associate tens of thousands of genes and small RNAs over hundreds of biomedical samples over 5-10 time points and 5-10 locations with each other and with the molecular signaling networks to effectively identify differentially altered hot spots. Such spots are the novel, mechanistic hypotheses that we aim to generate, which can be tested for drugability and validated in wet labs afterwards.

我们为现代分子生物学、生物医学和系统/网络医学的应用开发创新的生物信息学方法。我们需要计算硬件来存储和分析大量所谓的组学数据。我们的目标是提取新的知识和产生新的假设，如机械生物标志物。我们采用先进的投标数据技术以及人工智能和机器学习技术。我们的研究重点是多尺度组学数据（如转录组学和表观基因组学）在时空分辨率上与分子信号通路的结合分析。后者被建模为带有节点和边的图。我们为高通量组学数据（tb级序列和光谱数据）和基因组规模网络（具有数千个节点和数十万个边缘）开发创新的启发式方法，以提供系统（生物）医学结果和目标。它们随后被用作机械性生物标记物，并可能有助于，例如在医学中，确定新的药物靶点，建议药物再利用候选药物，并发现多种药物的协同机制（网络药理学）。这需要一个本地的高度并行的计算服务器基础设施，它具有许多处理器内核（>48）和大的主存（>256 GB），可以被所有计算内核并发访问。例如，我们莱布尼茨研究中心提供的典型服务不包括这一特定领域。然而，这样的系统是将数以万计的基因和小rna与数百个生物医学样本在5-10个时间点和5-10个位置上相互关联，并与分子信号网络相关联，以有效识别差异改变的热点的唯一途径。这些斑点是我们旨在产生的新颖的、机械的假设，可以测试其药物性，然后在潮湿的实验室中进行验证。