Trinity: Transcriptome assembly for genetic and functional analysis of cancer

描述（由申请人提供）：RNA-Seq研究表明，癌症转录组是由遗传变化、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转录本进行比较或将它们组装起来。然而，当癌症基因组与参考基因组存在本质差异或用于检测癌症微生物组序列时，这种策略可能受到限制。Assembly first’（de novo）方法是一种引人注目的替代方案，它将读取的数据组合到转录本中，而不需要任何映射。组装的转录组可以用来鉴定突变、剪接模式、表达水平、肿瘤相关微生物，如果从单个细胞收集，还可以表征肿瘤异质性。因此，对计算效率高、准确和用户友好的工具的巨大需求用于癌症转录组重建和分析。《Trinity》于2011年年中首次免费发行

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.