Trinity: Transcriptome assembly for genetic and functional analysis of cancer

Trinity：用于癌症遗传和功能分析的转录组组装

• 批准号: 9126450
• 负责人: AVIV REGEV
• 金额: $ 66.45万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-17 至 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9126450
• 关键词: Adopted; Algorithms; Alternative Splicing; Automobile Driving; Bacteria; Bioinformatics; Cancer Biology; Cells; Cloud Computing; Code; Communities; Computer Analysis; Computer software; Data; Data Set; Diagnostic; Documentation; Educational workshop; Emerging Technologies; Environment; Epigenetic Process; Exons; Fee-for-Service Plans; Funding; Galaxy; Genetic; Genetic Heterogeneity; Genetic Transcription; Genetic Variation; Genome; Health; High Performance Computing; Human; Industry; Inferior; Introns; Lead; Letters; Malignant Neoplasms; Manuals; Maps; Measures; Messenger RNA; Methods; Microbe; Mining; Mutation; Online Systems; Pattern; Performance; Persons; Process; RNA; RNA Editing; RNA Splicing; Reading; Research; Research Infrastructure; Research Personnel; Resort; Resources; Sampling; Sequence Analysis; Services; Shapes; Speed; Structure; Technology; The Cancer Genome Atlas; Training; Training Support; Transcript; Update; Variant; Viral; Work; anticancer research; base; cancer cell; cancer genome; computing resources; disease diagnosis; epigenetic variation; improved; literature citation; microbial; microbiome; new therapeutic target; open source; reconstruction; reference genome; symposium; tool; transcriptome; transcriptome sequencing; transcriptomics; tumor; tumor heterogeneity; tumorigenesis; user-friendly; virome

DESCRIPTION (provided by applicant): RNA-Seq studies indicate that the cancer transcriptome are shaped by genetic changes, variation in gene transcription, mRNA processing, editing and stability, and the cancer microbiome. Deciphering this variation and understanding its implications on tumorigenesis requires sophisticated computational analyses. Most RNA-Seq analyses rely on methods that first map short reads to a reference genome, and then compare them to annotated transcripts or assemble them. However, this strategy can be limited when the cancer genome is substantially different than the reference or for detecting sequences from the cancer microbiome. 'Assembly first' (de novo) methods that combine reads into transcripts without any mapping are a compelling alternative. The assembled transcriptome can then be used to identify mutations, splicing patterns, expression levels, tumor-associated microbes, and - if collected from single cells - characterize tumor heterogeneity. There is thus an enormous need for computationally efficient, accurate and user friendly tools for transcriptome reconstruction and analysis in cancer. Trinity, first released in mid-2011 and freely available as Open Source, is the leading software for de novo RNA-Seq assembly, with over 16,000 downloads, 177 literature citations, and a host of modules for downstream analyses, contributed by 3rd party developers. While widely-adopted in the general research community, Trinity (and any de novo RNA-Seq assembly) is only now emerging in the cancer domain. Here, we will enhance and maintain Trinity as a leading tool for cancer transcriptomics. We will tailor analytic modules for critical tasks in cancer biology, working with a network of cancer researchers on Driving Cancer Projects (Aim 1). We will continue to update the Trinity software to enhance the core algorithm, leverage new sequencing technologies as they arise, and incorporate additional 3rd party tools (Aim 2). We will enhance the Trinity software for different computational environments, including user-friendly interfaces to high performance computing infrastructure freely available to any NCI-funded researcher (Aim 3). We will grow the Trinity cancer user community, using online and in- person training and support (Aim 4), to allow any cancer researcher to leverage it.

描述(申请人提供)：RNA-Seq研究表明，癌症转录组是由基因变化、基因转录变异、信使核糖核酸的加工、编辑和稳定性以及癌症微生物组形成的。破译这种变异并理解其对肿瘤发生的影响需要复杂的计算分析。大多数RNA-Seq分析依赖的方法是首先将短片段映射到参考基因组，然后将它们与带注释的转录本进行比较或组装。然而，当癌症基因组与参考基因组有很大不同或用于检测癌症微生物组的序列时，这一策略可能会受到限制。“组装优先”(De Novo)方法是一种令人信服的替代方法，这种方法将阅读内容组合成转录，而不需要任何映射。然后，组装的转录组可以用于识别突变、剪接模式、表达水平、肿瘤相关微生物，以及-如果从单个细胞收集-表征肿瘤的异质性。因此，对用于癌症转录组重建和分析的计算高效、准确和用户友好的工具的需求是巨大的。《三位一体》，2011年年中首次发行，免费发行 以开源形式提供，是重新组装RNA-Seq的领先软件，有超过16,000次下载，177篇文献引用，以及由第三方开发商贡献的一系列用于下游分析的模块。虽然在普通研究界被广泛采用，三位一体(和任何从头RNA-序列组装)现在才在癌症领域出现。在这里，我们将加强并保持利邦作为癌症转录组学的领先工具。我们将为癌症生物学中的关键任务量身定做分析模块，与癌症研究人员网络合作推动癌症项目(目标1)。我们将继续更新三一软件，以增强核心算法，在出现新的测序技术时利用它们，并纳入其他第三方工具(AIM 2)。我们将为不同的计算环境增强三一软件，包括向任何NCI资助的研究人员免费提供的高性能计算基础设施的用户友好界面(AIM 3)。我们将通过在线和面对面的培训和支持(目标4)来扩大三一癌症用户社区，以允许任何癌症研究人员利用它。