ABI Innovation: Computational Tools for Transcriptome Reconstruction

本项目旨在开发一种高效准确的新计算方法来识别新转录本及其表达水平。转录组组装和基因表达谱是当今广泛的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。尽管转录本组装器之前已经开发出来，但它们中的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模型，将开发一种创新的新方法，结合几个科学学科的思想。通过确保该方法适用于现代下一代测序仪器常规产生的非常大的数据集，该项目将对真核生物物种光谱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用于商业、教育或基础研究。这种新方法使用了一种优化技术，称为特殊构建的流量网络中的最大流量，以确定基因表达水平，同时组装基因的每个剪接变体。它还结合了来自全基因组组装的技术，这有可能极大地提高对替代剪接变体的检测。通过使用预组装reads，与转录组组装相关的计算负荷和内存存储需求将大大减少，因为许多短reads将被组合成跨越多个外显子的较长contigs。此外，新方法将解决转录组组装方法的关键需求，该方法能够处理草稿基因组中存在的众多缺口，并通过将位于基因组多个片段上的转录本部分拼接在一起产生更好的组装转录本。该项目的结果将在http://ccb.jhu.edu上传播。

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.