SpliceCore: A cloud-based platform to detect, quantify and interpret alternative splicing variation from next-generation sequencing data.

 描述(由申请人提供)：这个小型企业创新研究(SBIR)第一阶段项目将产生SpliceCore的第一个原型，这是一个基于云的资源，用于从RNA-seq数据中发现、分析和解释替代剪接(AS)。在所有已知的疾病中，有15%是由AS缺陷引发的，AS是一种将功能多样性传递给基因的mRNA成熟过程。有缺陷的AS可以通过小分子和RNA治疗化合物来治疗，其中一些目前正在进行临床试验。SpliceCore将通过从RNA-seq数据中提取与疾病相关的事件来发现新的药物靶点和生物标记物。SpliceCore套件结合了在冷泉港实验室(CSHL)开发和验证的三种算法：SpliceTrap，用于检测AS的特征；SpliceDuo，用于识别重要的AS变异；以及SpliceImpact，用于确定具有治疗潜力的生物学相关AS事件的优先顺序。我们目前正在CSHL应用这些算法，以发现导致乳腺癌的AS事件，并研究AS在脊柱肌萎缩症发病机制中的作用。2013年，Transcriptomics的市场价值为17亿美元，预计到2019年将达到37亿美元，2014至2019年的复合年增长率为13.7%。RNA-SEQ数据正迅速积累在公共存储库中，如癌症基因组图谱(TCGA)、Geuvadis和ENCODE项目。由于下一代测序成本的降低和RNA疗法的早期成功，预计涉及AS分析的临床前研究的数量将会增加。SpliceCore将降低与AS分析相关的成本、时间和复杂性。要交付商业原型，必须预测在基于云的环境中同时操作的多个用户的需求。我们这个项目的目标是研究符合用户定制规范的经济高效的计算策略。因此，我们的目标是(1)开发数据处理方法和预测启发式方法，在减少云支出的同时提高计算性能；(2)通过发现新的AS来提高检测灵敏度，并利用这一新能力为癌症特异性AS事件生成数据库；以及(3)通过对象识别和利用“组学”数据集的新量化指标开发人机交互，改进SpliceImpact生物学解释。市场上存在着巨大的挑战，如何用Envisagenics创新技术可以缓解的、具有实验可测试性的解决方案来进行经济高效、快速和可靠的数据分析。Envisagenics有一个巨大的机会，因为生物医学部门对AS分析的需求增加，新的高通量能力和有前景的临床试验加强了这一需求。这项工作是与AS领域的领先生物信息学家之一Gunnar Rätsch博士、纪念斯隆-凯特琳癌症研究所准成员Gunnar Rätsch博士、大数据生物医学数据分析计算方法专家和著名科学家冷泉港实验室教授禤浩焯Kainer博士密切合作的，他破译了AS的大部分机制及其对癌症和其他遗传疾病的影响。

 DESCRIPTION (provided by applicant): This Small Business Innovation Research (SBIR) Phase I project will yield the first prototype of SpliceCore, a cloud-based resource for the discovery, analysis and interpretation of Alternative Splicing (AS) from RNA-seq data. 15% of all known diseases are triggered by defects in AS, an mRNA maturation process that conveys functional diversity to genes. Defective AS is treatable by small molecules and RNA therapeutic compounds, some of which are currently in clinical trials. SpliceCore will discover new drug targets and biomarkers by extracting disease-relevant AS events from RNA-seq data. The SpliceCore suite combines three algorithms developed and validated at Cold Spring Harbor Laboratory (CSHL): SpliceTrap, for the detection of AS profiles; SpliceDuo, for the identification of significant AS variation; and SpliceImpact, for the prioritization of biologically relevant AS events with therapeutic potential. We are currently applying these algorithms at CSHL for the discovery of AS events causative of Breast Cancer and to study the role of AS in the mechanism of the Spinal Muscular Atrophy disease. The Transcriptomics market was valued at $1.7 billion in 2013 and it is expected to reach $3.7 billion by 2019 at a CAGR of 13.7% from 2014 to 2019. RNA-seq data is quickly accumulating in public repositories such as The Cancer Genome Atlas (TCGA), Geuvadis and the ENCODE project. It is expected that the number of pre-clinical studies involving AS profiling will increase as a result of the reduced costs of Next Generation Sequencing and the early success of RNA therapeutics. SpliceCore will reduce the cost, time and complexity associated with AS analysis. To deliver a commercial prototype, it is necessary to anticipate the demands of multiple users operating simultaneously in a cloud-based environment. Our objective for this project is to investigate cost-effective computing strategies that comply with user-tailored specifications. Therefore our aims are (1) to develop data processing methods and predictive heuristics that increase computing performance while reducing cloud expenditures; (2) to increase detection sensitivity by enabling the discovery of novel AS, and use this new capacity to generate a database for cancer-specific AS events; and (3) Improve SpliceImpact biological interpretation by developing human-computer interaction through object recognition and new quantitative metrics that capitalize on "omics" datasets. There is a great challenge in the market in making cost- effective, fast and robust data analysis with experimentally testable solutions which Envisagenics innovative technology could relief. Envisagenics has a tremendous opportunity due to the increased demand for AS analysis in the biomedical sector, reinforced by new high-throughput capabilities and promising clinical trials. This work is a close collaboration with one of the leading bioinformaticians in the AS field, Dr. Gunnar Rätsch Associate Member at Memorial Sloan-Kettering Institute for Cancer Research, expert in computational methods for the analysis of big biomedical data and the renown scientists Dr. Adrian Krainer, Professor at Cold Spring Harbor Laboratory, who has deciphered much of the AS mechanism and its implications to Cancer and other genetic disorders.