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，癌症转录组是由遗传变化，基因变异， 转录，mRNA加工，编辑和稳定性，以及癌症微生物组。解读这种变异 理解其对肿瘤发生的影响需要复杂的计算分析， 能够处理批量RNA-Seq以及单个肿瘤细胞转录组的分析。大多数RNA-Seq 分析依赖于首先将短读段映射到参考基因组，然后将它们与 注释的抄本或汇编它们。然而，当癌症基因组是 在一些实施方案中，所述方法用于检测与参考基本上不同的序列或用于检测来自癌症微生物组的序列。大会 将读段联合收割机组合成转录物而不进行任何定位的“第一”（从头）方法是一种令人信服的替代方法。 然后，组装的转录组可用于鉴定突变、融合转录物、剪接模式， 表达水平，肿瘤相关微生物，以及-如果从单细胞收集-表征肿瘤 异质性因此，对计算高效、准确和用户友好的工具存在巨大需求 用于癌症的转录组重建和分析。Trinity，于2011年年中首次发布，免费提供 作为开源，是从头RNA测序组装的领先软件，由 成千上万的搜索者，超过4k的文献引用，现在包括下游的模块主机 由Trinity开发团队或第三方开发人员提供的分析。在这里，我们将 继续加强和维持Trinity，并进一步发展我们的Trinity癌症转录组分析工具 （CTAT）作为批量和单细胞癌症转录组学的领先工具套件。我们将定制分析模块， 癌症生物学的关键任务，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Trinity软件，以增强核心算法，利用新的测序 技术和整合基因组数据与基因组自由组装（目标2）。我们将整合Trinity CTAT 通过FireCloud进入NCI云计算平台，进行可扩展的癌症转录组数据处理， 分析（目标3）。我们将发展Trinity癌症用户社区，使用在线和面对面培训， 支持（目标4），允许任何癌症研究人员以不同的方式利用它。