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)

基本信息

批准号：
8816112
负责人：
Inanc Birol
金额：
$ 24.97万
依托单位：
BRITISH COLUMBIA CANCER AGENCY
依托单位国家：
美国
项目类别：
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/8816112
关键词：
3&apos Untranslated Regions Algorithms Architecture Area Bioinformatics Biological Biology Cereals Collaborations Communities Computer Systems Computer software DNA Resequencing DNA Sequence Data Detection Development Devices Drops Equilibrium Event Gene Expression Profile Generations Genome Genomics Goals Graph Health High Performance Computing High-Throughput Nucleotide Sequencing 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 genome-wide human reference genome improved indexing insight interest metagenome microbiome new technology next generation operation research study scaffold shared memory success targeted sequencing tool transcriptome sequencing

项目摘要

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.

在重测序实验中，将读数组装成连贯的图片可以进行联合分析原始读数，提供了一种公正的方法来检测个体之间的基因组差异人口研究或识别癌症研究中的体细胞变化。这种方法正在获得大规模研究的兴趣，例如癌症基因组图谱 (TCGA) 和国际癌症基因组联盟 (ICGC) 项目汇编了他们的初步研究结果。我们的从头组装算法及其下游分析流程的实现是用于询问基因组 (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贡献分析结果，包括多个 TCGA 和 ICGC 项目。我们还为以下用户提供这些软件社区;截至 2014 年 1 月，ABySS 和 Trans-ABySS 总共收到了 700 多个引用（来源：汤森路透），同时享受 Google 充满活力的用户讨论场所团体。基于我们分析平台的成功，我们将继续开发我们的算法，并将使其适应快速发展的测序技术中的数据。我们建议提高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来扩大用户群工具。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；目前在组装的转录组中使用替代异构体作为切片图；重建3'非翻译区域；并将重叠群细化为参考比对和他们的解释是为了更好的结构变异和嵌合转录本检测。为了实现这些目标，我们将重点关注（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。随着测序技术日新月异，其通量仍在不断增加需要以指数方式适应现有的生物信息学工具，例如 ABySS 和 Trans-ABySS，提高其性能，并使不断增长的用户能够使用它们社区。我们工具的持续开发将使转化基因组学研究成为可能精准个人医疗之路。