ABI Innovation: Computational Tools for Transcriptome Reconstruction

ABI Innovation：转录组重建的计算工具

• 批准号: 1458178
• 负责人: Mihaela Pertea
• 金额: $ 66.28万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-01 至 2019-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1458178&HistoricalAwards=false
• 关键词: ABI; Innovation; Computational; Tools; Transcriptome

This project aims to develop an efficient and accurate new computational method for identifying novel transcripts and their expression levels. Transcriptome assembly and gene expression profiling are key components in a vast range of biological experiments today, playing a central role in unraveling the complexity of cell types, cell differentiation, responses to stress, and myriad other conditions. Although transcript assemblers have been developed previously, most of them perform poorly on real, large-scale RNA sequencing data sets, severely limiting their impact. To produce better transcript models, an innovative new method will be developed, combining ideas from several scientific disciplines. By ensuring that this method works on the very large data sets that are routinely produced by modern next-generation sequencing instruments, this project will have an impact on a wide range of studies across the spectrum of eukaryotic species. It will also enhance the research infrastructure by providing free, open source software that can be re-used by other scientists for commercial, educational, or basic research endeavors.This new method uses an optimization technique known as maximum flow in a specially-constructed flow network to determine gene expression levels, and it does this while simultaneously assembling each splice variant of a gene. It also incorporates techniques from whole-genome assembly, which has the potential to dramatically improve detection of alternative splice variants. By using pre-assembled reads, the computational load and memory storage requirements associated with transcriptome assembly will be greatly reduced, as many of the short reads will be combined into longer contigs that span multiple exons. Furthermore, the new method will address a critical need for a transcriptome assembly method that is able to handle the numerous gaps present in draft genomes, and to produce better-assembled transcripts by stitching together portions of transcripts situated on multiple fragments of the genome. The results of this project will be disseminated at http://ccb.jhu.edu.

该项目旨在开发一种高效、准确的新计算方法来识别新的转录本及其表达水平。转录组组装和基因表达谱是当今大量生物实验的关键组成部分，在揭示细胞类型、细胞分化、应激反应和无数其他条件的复杂性方面发挥着核心作用。尽管之前已经开发出转录本组装器，但大多数在真实的大规模 RNA 测序数据集上表现不佳，严重限制了它们的影响。为了产生更好的转录本模型，将开发一种创新的新方法，结合多个科学学科的想法。通过确保该方法适用于现代下一代测序仪器通常产生的非常大的数据集，该项目将对真核物种范围内的广泛研究产生影响。它还将通过提供免费的开源软件来增强研究基础设施，这些软件可以被其他科学家重复用于商业、教育或基础研究工作。这种新方法在专门构建的流网络中使用一种称为最大流的优化技术来确定基因表达水平，并在同时组装基因的每个剪接变体的同时实现这一点。它还结合了全基因组组装技术，有可能显着改善选择性剪接变体的检测。通过使用预组装的读数，与转录组组装相关的计算负载和内存存储要求将大大减少，因为许多短读数将被组合成跨越多个外显子的较长重叠群。此外，新方法将解决转录组组装方法的关键需求，该方法能够处理基因组草案中存在的众多缺口，并通过将位于基因组多个片段上的转录本部分缝合在一起来产生更好组装的转录本。该项目的结果将在 http://ccb.jhu.edu 上发布。