De Novo Assembly Tools: Research with Unbiased Engines (DNA-TRUE)

在重新测序实验中，将读数组装成连贯的图片能够联合分析 RAW Reads，提供了一种无偏见的方法来检测个体之间的基因组差异 人口研究或确定癌症研究中的体细胞变化。这种方法正在获得 对大规模研究感兴趣，如癌症基因组图谱和国际 癌症基因组联盟(ICGC)项目，汇编他们的初步发现。 我们实现的从头组装算法及其下游分析流水线是 该领域中流行的询问基因组(深渊)和转录本(跨深渊)的工具。 使用这些工具，我们的团队一直在为一些癌症研究贡献分析结果， 包括几个TCGA和ICGC项目。我们还将这些软件用于 社区；截至2014年1月，深渊和跨深渊总共收到了700多份 引文(来源：汤姆森路透社)，同时享受谷歌充满活力的用户讨论场所 组。在我们分析平台的成功基础上，我们将继续开发我们的 算法，并将使它们适应来自快速发展的测序技术的数据。 我们建议提高深渊和跨深渊的性能，并继续支持 不断增长的用户基础，更好的基因组、转录组和元基因组组装和分析 工具。我们还将扩展分析管道的功能，以集成正交数据 支持检测到的事件；在组装的转录本中呈现可选的异构体用法 切片图；重建3‘非翻译区；将重叠群细化为参考比对和 它们的解释更好地进行了结构变异和嵌合转录检测。 为了实现这些目标，我们将重点放在(1)算法改进上 序列组装和比对方法，(2)高性能计算平台，以及 优化我们对下一代中央处理器(CPU)的分析方法 架构，以及(3)下游分析管道，建立简化的标准操作 程序。 随着测序技术的快速变化，它们的吞吐量仍在不断增加 指数级地，需要调整已建立的生物信息学工具，如深渊和 跨越深渊，提高它们的性能，并使越来越多的人能够使用它们 社区。我们工具的持续开发将使翻译基因组学研究成为可能 精准的个人医学之路。

In a resequencing experiment, assembling reads into a coherent picture enables joint analysis of raw reads, offering an unbiased approach to detect genomic differences between individuals in population studies or to identify somatic changes in cancer research. This approach is gaining interest as large scale studies, such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) projects, compile their preliminary findings. Our implementation of a de novo assembly algorithm and its downstream analysis pipelines are popular tools in the field for interrogating genomes (ABySS) and transcriptomes (Trans-ABySS). Using these tools, our team has been contributing analysis results to a number of cancer studies, including several TCGA and ICGC projects. We also make these software available for the community; as of January 2014, ABySS and Trans-ABySS have collectively received over 700 citations (source: Thomson-Reuters) while enjoying vibrant user discussion venues at Google Groups. Building on the success of our analysis platforms, we will continue developing our algorithms, and will adapt them to data from the rapidly evolving sequencing technologies. We propose to improve the performance of ABySS and Trans-ABySS, and continue supporting a growing user base with better genome, transcriptome, and metagenome assembly and analysis tools. We will also expand the functionality of our analysis pipelines to integrate orthogonal data that support detected events; present alternative isoform usage in assembled transcriptomes as slice graphs; reconstruct 3' untranslated regions; and refine contig to reference alignments and their interpretation for better structural variation and chimeric transcript detection. To accomplish these goals, we will focus on (1) algorithmic improvements on the primary sequence assembly and alignment approaches, (2) high performance computing platforms, and optimize our analysis approaches on the next generation of central processing unit (CPU) architectures, and (3) downstream analysis pipelines, building streamlined standard operating procedures. With sequencing technologies changing rapidly, and their throughput still increasing exponentially, there is a need to adapt established bioinformatics tools, such as ABySS and Trans-ABySS, improve their performance, and make their use accessible to a growing community. The continued development of our tools will enable translational genomics studies on the road to precise personal medicine.