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: 10251056
• 负责人: Brian Haas
• 金额: $ 82.28万
• 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 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10251056
• 关键词: Algorithms; Automobile Driving; B cell clonality; Base Sequence; Cancer Biology; Cataloging; Cells; Clinical; Clinical Research; Cloud Computing; Communities; Computer Analysis; Computer software; Data; Data Analyses; Data Set; Development; Disease; Documentation; Educational workshop; Ensure; Exons; Funding; Genetic; Genetic Identity; Genetic Transcription; Genome; Growth; Immunotherapy; Individual; Malignant - descriptor; Malignant Neoplasms; Maps; Messenger RNA; Metadata; Methods; Microbe; Modality; Mutation; Mutation Analysis; Mutation Detection; Patient Care; Patients; Pattern; Persons; Point Mutation; Protein Isoforms; RNA; RNA Splicing; Reporting; Research Personnel; Resolution; Sampling; Software Engineering; Specimen; Structure; T-Lymphocyte; Technology; The Cancer Genome Atlas; Time; Training; Training Support; Transcript; Update; Variant; Visualization; Work; anticancer research; base; cancer cell; cancer genome; cohort; computational platform; computerized data processing; computing resources; data resource; disease diagnosis; exome; exome sequencing; experience; flexibility; genome-wide; human reference genome; individual patient; insight; literature citation; microbiome; neoantigens; neoplastic cell; new therapeutic target; novel; novel sequencing technology; open source; reconstruction; reference genome; single-cell RNA sequencing; tool; transcriptome; transcriptome sequencing; transcriptomics; tumor; tumor heterogeneity; tumor microenvironment; tumorigenesis; user-friendly; virome

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, and being able to tackle analyses of bulk RNA-Seq as well as transcriptomes of individual tumor cells. 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, fusion transcripts, 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, executed millions of times by thousands of rsearchers, over 4k literature citations, and now includes a host of modules for downstream analyses, contributed by the Trinity development team or contributed by 3rd party developers. Here, we will continue to enhance and maintain Trinity and further develop our Trinity Cancer Transcriptome Analysis Tookit (CTAT) as leading tool suite for bulk and single-cell 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, leveraging new sequencing technologies and integrating genome data with genome-free assembly (Aim 2). We will integrate Trinity CTAT into the NCI cloud computing platform via FireCloud for scalable cancer transcriptome data processing and analyses (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 in diverse modalities.

Rna-seq研究表明，癌症转录组是由基因变化、基因变异形成的。 转录、信使核糖核酸的加工、编辑和稳定性，以及癌症微生物组。破译这一变异 而理解它对肿瘤发生的影响需要复杂的计算分析，并且 能够处理大宗RNA-Seq分析以及单个肿瘤细胞的转录本。大多数RNA序列 分析依赖于首先将短片段映射到参考基因组的方法，然后将它们与 带注释的成绩单或将其汇编。然而，当癌症基因组是 基本上不同于参考或用于检测来自癌症微生物组的序列。`集合 First‘(De Novo)方法不需要任何映射就可以将阅读内容合并到记录中，这是一个引人注目的选择。 然后可以使用组装的转录组来识别突变、融合转录本、剪接模式， 表达水平、肿瘤相关微生物，以及--如果从单个细胞采集--表征肿瘤 异质性。因此，存在对计算高效、准确和用户友好的工具的巨大需求 用于癌症的转录组重建和分析。三位一体，于2011年年中首次发布，可免费使用 作为开放源码，是重新组装RNA-Seq的领先软件，由 数以千计的搜索者，超过4K的文献引用，现在包括了一系列下游模块 分析，由利邦开发团队贡献或由第三方开发者贡献。在这里，我们将 继续增强和保持三位一体，并进一步开发我们的三位一体癌症转录组分析工具包 (CTAT)作为大宗和单细胞癌症转录分析的领先工具套件。我们将为以下对象定制分析模块 癌症生物学中的关键任务，与癌症研究人员网络合作推动癌症项目(AIM 1)。我们将继续更新三一软件，以增强核心算法，利用新的测序 将基因组数据与无基因组组装结合起来(目标2)。我们将整合利邦CTAT 通过FireCloud进入NCI云计算平台，用于可扩展的癌症转录组数据处理和 分析(目标3)。我们将通过在线和面对面的培训和 支持(目标4)，允许任何癌症研究人员以不同的方式利用它。