Development of a Rapid Processing Pipeline and Graph-based Visualization for the Analysis of Next Generation Sequencing Data

在过去十年左右的时间里，新的实验室分析平台产生了大量生物数据。这些数据越来越复杂，规模越来越大。特别是DNA测序在过去十年中彻底改变了生物医学和生物科学。最近新的DNA测序平台的可用性意味着相对于几年前可能产生的数据，可以产生数量级更多的数据。这些进步进一步改变了我们对基础、应用和临床研究的科学方法的看法。例如，对许多相关生物体的全基因组进行测序的能力使得能够进行大规模的比较和进化研究，这直到最近都是不可想象的。测序也可用于确定哪些基因在任何给定的状态或时间通过RNA测序基因表达分析目前是活跃的。在分析基因表达研究中，RNA测序可以在没有先验知识的情况下识别和定量稀有基因，并可以提供有关所识别基因中序列变异的信息。当与“下拉”技术相结合时，这些方法还可以回答有关基因调控的重要问题，如转录因子或microRNA靶点结合。然而，这些技术进步带来了重大的分析挑战，特别是在目前产生的数据规模方面。例如，Illumina Solexa GA-2机器的单次运行仅产生大约100 Gb的序列数据。存在许多用于分析这些数据的方法，但是它们通常是缓慢的并且计算非常密集，需要大内存计算机或高性能计算集群以便有效地分析这些数据。如何最好地分析这一信息是一个持续和积极的讨论。解决其中一些问题的一种方法是开发用于数据分析的快速优化算法，并将数据可视化和分析为网络图。该提案旨在开发一个优化系统，用于分析此类数据。它将涉及开发非常快速和优化的算法，用于处理我们已经创建原型的数据。我们将利用相对较新的GPU硬件加速领域，使这些算法在消费者3D显卡上使用专用硬件时运行得更快。通过该系统处理的数据将使用围绕现有BioLayout 3D系统设计的定制3D可视化环境进行可视化。这些序列图已经证明了它们在识别新的序列元件和帮助组装它们的共有序列方面是有用的，在许多情况下有助于识别问题所在。此外，我们打算利用相关性分析的力量来处理RNA-seq数据，为从一级序列数据到每个基因摘要的标签的共表达分析提供综合解决方案。然而，在此过程中，我们还将提供基于网络和对齐的主要数据视图，以支持汇总分析。这将为用户提供新的方式来查看他们的数据以及读取如何相互作用以及基因组本身。整个系统将是模块化的，每个模块将从用Java编写的图形用户界面访问，这使用户能够控制分析模块，并允许快速分析从原始数据到基因组/基因水平分析的大规模数据集。

Over the last decade or so there has been an explosion of biological data emanating from new laboratory analysis platforms. These data are increasingly complex and large-scale. DNA sequencing in particular has revolutionized the biomedical and biological sciences over the last decade. The recent availability of new DNA sequencing platforms mean that orders of magnitude more data can be produced relative to what was possible just a few years ago. These advances have further changed the way we think about scientific approaches to basic, applied and clinical research. For example, the ability to sequence the whole genome of many related organisms has allowed large-scale comparative and evolutionary studies to be performed that were until recently unimaginable. Sequencing can also be used to determine which genes are currently active at any given state or time by RNA sequencing for gene-expression analyses. In analysing gene-expression studies, RNA-sequencing can identify and quantify rare genes without prior knowledge and can provide information regarding sequence variation in the identified genes. When combined with 'pull-down' technologies, these approaches can also answer important questions regarding gene regulation such as transcription factor or microRNA target binding. These advances in technology however come with significant analytical challenges, in particular with respect to the sheer scale of data now being produced. For example a single run of an Illumina Solexa GA-2 machine produces approximately 100Gb of sequence data alone. A number of approaches exist for the analysis of these data, however they are usually slow and extremely computationally intensive, requiring large-memory computers or high-performance computing clusters in order to effectively analyse these data. How best to analyse this information is an ongoing and active discussion. One approach to resolving some of these issues is to both develop fast optimal algorithms for data analysis and to visualise and analyse data as network graphs. This proposal is to develop an optimised system for the analysis of such data. It will involve the development of extremely fast and optimised algorithms for processing the data for which we have already created prototypes. We will utilise the relatively new field of GPU hardware acceleration to allow these algorithms to run significantly faster when utilising specialised hardware on a consumer 3D graphics card. Data processed through the system will be visualised using a customised 3D visualisation environment designed around the existing BioLayout Express3D system. These sequence graphs have already proved themselves useful identifying novel sequence elements and aiding the assembly of their consensus sequences, in many cases helping to identify where issues lie. Furthermore, we intend to harness the power of correlation analysis for working with RNA-seq data, providing an integrated solution for moving from primary sequence data through to co-expression analysis of tags per gene summaries. In doing however we will also provide network and alignment based views of the primary data that underpin the summary analyses. This will provide novel ways for users to see their data and how reads interact with each other and the genome itself. The entire system will be modular and each module will be accessed from a graphical user interface written in Java, that gives the user control over analysis modules and allows rapid analysis of large-scale datasets from the primary data to genome/gene level analyses.