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

从头组装工具：使用无偏差引擎进行研究 (DNA-TRUE)

• 批准号: 8631896
• 负责人: Inanc Birol
• 金额: $ 24.95万
• 依托单位: BRITISH COLUMBIA CANCER AGENCY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-03-04 至 2017-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8631896
• 关键词: 3' Untranslated Regions; Algorithms; Architecture; Area; Bioinformatics; Biological; Biology; Cereals; Collaborations; Communities; Computer Systems; Computer software; DNA Resequencing; DNA Sequence; DNA Sequence Rearrangement; Data; Detection; Development; Devices; Drops; Equilibrium; Event; Gene Expression Profile; Generations; Genome; Genomics; Goals; Graph; Health; High Performance Computing; High-Throughput Nucleotide Sequencing; Human Genome; Hybrids; Individual; Infectious Agent; Instruction; International; Joints; Length; Life; Malignant Neoplasms; Medicine; Memory; Performance; Phylogeny; Polyadenylation; Population Study; Procedures; Process; Protein Isoforms; RNA Sequences; RNA Splicing; Reading; Research; Research Personnel; Sampling; Seeds; Site; Slice; Source; Staging; Technology; Testing; The Cancer Genome Atlas; Time; Transcript; Trees; Variant; anticancer research; base; cancer genome; cohort; cost; cost effective; data structure; design; falls; improved; indexing; insight; interest; metagenome; microbiome; new technology; next generation; operation; research study; scaffold; shared memory; success; tool

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.

在重新陈述实验中，组装读取成连贯的图片，可以联合分析 原始阅读，提供一种公正的方法来检测个体之间的基因组差异 人群研究或确定癌症研究的躯体变化。这种方法正在获得 兴趣作为大规模研究，例如癌症基因组图集（TCGA）和国际 癌症基因组联盟（ICGC）项目，汇编了他们的初步发现。 我们实施从头组装算法及其下游分析管道是 该领域的流行工具询问基因组（ABYS）和转录组（反式ABYS）。 使用这些工具，我们的团队一直在为许多癌症研究做出分析结果， 包括几个TCGA和ICGC项目。我们还使这些软件可用于 社区;截至2014年1月，深渊和跨性别者已统称为700多个 引用（来源：Thomson-Reuters）在Google享受充满活力的用户讨论场所时 组。在我们的分析平台成功的基础上，我们将继续发展我们的 算法，并将使它们适应快速发展的测序技术的数据。 我们建议提高深渊和跨性别的表现，并继续支持 具有更好基因组，转录组和元基因组组装和分析的用户群增长 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；当前的替代同工型在组装转录组中使用 切片图；重建3'未翻译区域；并完善重叠群以参考一致性和 他们对更好的结构变化和嵌合转录检测的解释。 为了实现这些目标，我们将重点介绍（1）对主要的算法改进 序列组件和对齐方式，（2）高性能计算平台和 优化我们对下一代中央处理单元（CPU）的分析方法 体系结构和（3）下游分析管道，建筑精简的标准操作 程序。 随着测序技术的变化，它们的吞吐量仍在增加 指数级，需要调整已建立的生物信息学工具，例如深渊和 跨性别，提高其性能，并使他们的使用可访问 社区。我们工具的持续开发将使转化基因组学研究 精确的个人医学之路。