Trinity: Transcriptome assembly for genetic and functional analysis of cancer

描述（由申请人提供）：RNA-Seq研究表明，癌症转录组由遗传变化、基因转录变异、mRNA加工、编辑和稳定性以及癌症微生物组形成。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分析依赖于首先将短读段映射到参考基因组的方法，然后将它们与注释的转录本进行比较或组装。然而，当癌症基因组基本上优于参考或用于检测来自癌症微生物组的序列时，这种策略可能受到限制。“组装优先”（从头）方法将联合收割机结合到转录物中而不需要任何映射是一种令人信服的替代方法。然后，组装的转录组可以用于鉴定突变、剪接模式、表达水平、肿瘤相关微生物，并且如果从单细胞收集，则表征肿瘤异质性。因此，对于用于癌症中的转录组重建和分析的计算高效、准确和用户友好的工具存在巨大的需求。Trinity于2011年年中首次发布， 作为开源软件提供，是从头RNA-Seq组装的领先软件，有超过16，000次下载，177篇文献引用，以及许多下游分析模块，由第三方开发人员贡献。虽然在一般研究界被广泛采用，但Trinity（以及任何从头RNA-Seq组装）现在才出现在癌症领域。在这里，我们将加强和维持Trinity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定制分析模块，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Trinity软件，以增强核心算法，利用新出现的测序技术，并纳入其他第三方工具（目标2）。我们将为不同的计算环境增强Trinity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（目标3）。我们将发展Trinity癌症用户社区，使用在线和面对面的培训和支持（目标4），让任何癌症研究人员利用它。

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 differerit 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.