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

SpliceCore：一个基于云的平台，用于检测、量化和解释下一代测序数据中的选择性剪接变异。

• 批准号: 8980250
• 负责人: MARTIN AKERMAN
• 金额: $ 22.5万
• 依托单位: ENVISAGENICS, INC.
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-06 至 2017-03-05
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8980250
• 关键词: Algorithms; Alternative Splicing; Basic Science; Big Data; Bioinformatics; Biological; Biological Markers; Businesses; Clinical Trials; Code; Collaborations; Computer software; Computing Methodologies; Coupled; Data; Data Analyses; Data Set; Databases; Decision Making; Defect; Detection; Development; Diagnostic; Disease; Drug Targeting; Environment; Event; Exons; Expenditure; Genes; Goals; Hereditary Disease; High-Throughput RNA Sequencing; Hour; Institutes; Laboratories; Language; Literature; Malignant Neoplasms; Marketing; Messenger RNA; Methods; Modeling; Modification; Mutation; Names; Normal tissue morphology; Nucleotides; Oncogenes; Other Genetics; Pathway interactions; Performance; Phase; Prevalence; Process; RNA; RNA Sequence Analysis; RNA Sequences; Research Personnel; Resources; Reverse Transcriptase Polymerase Chain Reaction; Role; Running; Sampling; Scientist; Services; Small Business Innovation Research Grant; Small RNA; Solutions; Speed; Spinal Muscular Atrophy; Technology; Testing; The Cancer Genome Atlas; Therapeutic; Time; Variant; Work; anticancer research; cancer genetics; cancer therapy; clinically relevant; cloud based; commercialization; computer human interaction; computer program; computerized data processing; cost; cost effective; design; drug discovery; flexibility; heuristics; human disease; improved; innovative technologies; laptop; malignant breast neoplasm; member; next generation sequencing; novel; object recognition; preclinical study; professor; programs; protein structure function; prototype; public health relevance; repository; small molecule; success; transcriptome sequencing; transcriptomics

 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.

 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，这是一种基于云的资源，用于从 RNA-seq 数据中发现、分析和解释选择性剪接 (AS)。 15% 的已知疾病是由 AS 缺陷引发的，AS 是一种向基因传递功能多样性的 mRNA 成熟过程，有缺陷的 AS 可以通过小分子和 RNA 治疗化合物进行治疗，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 AS 事件来发现新的药物靶点和生物标志物。 SpliceCore 套件结合了冷泉港实验室 (CSHL) 开发和验证的三种算法：SpliceTrap，用于检测 AS 配置文件；用于识别显着的 AS 变异；以及 SpliceImpact，用于优先考虑具有治疗潜力的生物学相关的 AS 事件。我们目前正在 CSHL 应用这些算法来发现 AS 事件的病因。 2013 年，转录组学市场价值为 17 亿美元，预计到 2019 年将达到 37 亿美元，2014 年至 2019 年的复合年增长率为 13.7%。 RNA-seq 数据正在癌症基因组图谱 (TCGA) 等公共存储库中快速积累， Geuvadis 和 ENCODE 项目预计，由于下一代测序成本的降低以及 SpliceCore 的早期成功将降低成本、时间和复杂性，涉及 AS 分析的临床前研究数量将会增加。为了提供商业原型，有必要预测在基于云的环境中同时运行的多个用户的需求，我们该项目的目标是研究符合用户定制的经济有效的计算策略。因此，我们的目标是（1）开发数据处理方法和预测启发法，以提高计算性能，同时减少云支出；（2）通过发现新颖的 AS 来提高检测灵敏度，并使用这种新功能生成数据库。 (3) 通过对象识别和利用“组学”数据集的新定量指标来开发人机交互，从而改进 SpliceImpact 生物学解释。快速、稳健的数据分析由于生物医学领域对 AS 分析的需求不断增加，Envisagenics 的创新技术可以缓解这一问题，并通过新的高通量能力和有前景的临床试验进行了密切合作。 AS 领域领先的生物信息学家、纪念斯隆-凯特琳癌症研究所副会员、生物医学大数据分析计算方法专家 Gunnar Rätsch 博士以及著名科学家 Adrian 博士Krainer 是冷泉港实验室的教授，他破译了许多 AS 机制及其对癌症和其他遗传性疾病的影响。