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

在重测序实验中，将读数组装成连贯的图片使得能够联合分析 原始读数，提供了一种无偏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 大规模的研究，如癌症基因组图谱（TCGA）和国际 癌症基因组联盟（ICGC）项目，汇编他们的初步研究结果。 我们的从头组装算法及其下游分析管道的实现是 询问基因组（ABySS）和转录组（trans-ABySS）领域的流行工具。 使用这些工具，我们的团队一直在为许多癌症研究提供分析结果， 包括几个TCGA和ICGC项目。我们还提供这些软件， 截至2014年1月，ABySS和Trans-ABySS共收到700多份 引用（来源：路透社），同时在谷歌享受充满活力的用户讨论场所 组基于我们分析平台的成功，我们将继续开发我们的 算法，并将使它们适应快速发展的测序技术的数据。 我们建议改善ABySS和Trans-ABySS的性能，并继续支持 通过更好的基因组、转录组和宏基因组组装和分析，扩大用户群 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;在组装的转录组中呈现替代同种型用途， 切片图;重建3'非翻译区;和将重叠群细化为参考比对， 它们的解释更好的结构变异和嵌合转录检测。 为了实现这些目标，我们将专注于（1）主要算法的改进 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，构建简化的标准操作 程序. 随着测序技术的迅速变化，其通量仍在增加， 指数，有必要适应既定的生物信息学工具，如ABySS和 Trans-ABySS，提高它们的性能，并使它们的使用可供越来越多的 社区我们的工具的持续发展将使翻译基因组学研究成为可能。 精准个人医疗之路

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.