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

基本信息

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
项目类别：
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

项目摘要

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，基因变异所塑造的。转录，mRNA加工，编辑和稳定性以及癌症体。破译这种变化并了解其对肿瘤发生的影响需要复杂的计算分析，并且是可以解决对散装RNA-seq的分析以及单个肿瘤细胞的转录组。大多数RNA-seq 分析依赖于首先映射简短读取到参考基因组的方法，然后将其比较注释的成绩单或组装它们。但是，当癌症基因组是与参考或检测癌症微生物组序列的参考大不相同。 `集会首先将读取成转录本无映射的转录本的方法是令人信服的替代方法。然后，组装的转录组可用于识别突变，融合转录本，剪接模式，表达水平，肿瘤相关的微生物，如果从单细胞中收集，则表征肿瘤异质性。因此，非常需要计算高效，准确和用户友好的工具用于癌症的转录组重建和分析。三位一体，首次于2011年中期发布，免费可用作为开源，是从头RNA-seq组装的领先软件，由数百万次执行成千上万的RSearcher，超过4K文献引用，现在包括许多下游模块由三位一体开发团队贡献或由第三方开发人员贡献的分析。在这里，我们会的继续增强和维持三位一体并进一步发展我们的三位一体癌转录组分析（CTAT）作为大量和单细胞癌转录组学的领先工具套件。我们将量身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开发癌症项目（AIM） 1）。我们将继续更新三位一体软件以增强核心算法，利用新的测序技术并将基因组数据与无基因组组装整合在一起（AIM 2）。我们将整合三位一体CTAT 通过FireCloud进入NCI云计算平台，以进行可扩展的癌症转录组数据处理和分析（AIM 3）。我们将使用在线和亲自培训以及支持（目标4），以允许任何癌症研究人员以潜水员方式利用它。