Advanced computational methods in analyzing high-throughput sequencing data

分析高通量测序数据的先进计算方法

• 批准号: 10367263
• 负责人: Heng Li
• 金额: $ 34.43万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10367263
• 关键词: Address; Adrenergic alpha-Antagonists; Advanced Development; Algorithms; Attention; Bacterial Genome; Biological; Biomedical Research; Code; Complex; Computational algorithm; Computer software; Computing Methodologies; Data; Funding; Future; Generations; Genes; Genetic Variation; Genome; Graph; Haplotypes; Hi-C; High-Throughput Nucleotide Sequencing; Label; Length; Mainstreaming; Maps; Medical; Memory; Methods; Modernization; Nucleotides; Parents; Performance; Phase; Price; Principal Investigator; Proteins; Protocols documentation; Publications; RNA Splicing; Repetitive Sequence; Research; Resolution; Seeds; Sequence Alignment; Sequence Analysis; Techniques; Technology; Work; base; clinical application; computerized tools; genetic pedigree; genome annotation; high throughput analysis; human data; improved; insertion/deletion mutation; nanopore; power analysis; programs; spelling; success; tool

PROJECT SUMMARY High-performance computational algorithms are essential to the analysis of large-scale biological sequence data and have received broad attention. Developed several years or even more than a decade ago, many mainstream software packages for sequence alignment, assembly and genome annotation do not take full advantage of modern accurate long-read data or cannot keep up with the throughput of current technologies. The development of advanced algorithms is critical to the applications of sequencing technologies in the near future. Based on our work in the previous funding cycle, this project will address this pressing need with four proposals: (1) developing an alignment algorithm for accurate long reads and high-quality assemblies for more comprehensive alignment through highly repetitive regions and long segmental duplications; (2) extending our hifiasm assembler to the high-quality assembly of more accurate Oxford Nanopore reads available nowadays; (3) combining our hifiasm and dipasm algorithms for more accurate and more contiguous haplotype-resolved assembly without pedigree data; (4) developing a protein-to-genome aligner to assist large-scale gene annotation of new species. Upon completion, the proposed studies will result in high-performance user facing tools for sequence alignment and assembly that are faster and more accurate than the current generation.

项目概要 高性能计算算法对于大规模生物序列数据的分析至关重要 并受到广泛关注。很多主流都是几年甚至十多年前开发的 用于序列比对、组装和基因组注释的软件包没有充分利用 现代准确的长读取数据或无法跟上当前技术的吞吐量。发展历程 先进算法的发展对于测序技术在不久的将来的应用至关重要。基于我们的 在上一个资助周期的工作中，该项目将通过四项建议来解决这一迫切需求：（1）开发 用于精确长读取和高质量组装的比对算法，以实现更全面的比对 通过高度重复的区域和长片段的重复； (2) 将我们的 hifiasm 汇编器扩展到 如今可提供更准确的 Oxford Nanopore 读数的高质量组装； (3) 结合我们的hifiism 和 dipasm 算法，可实现更准确、更连续的单倍型解析组装，无需谱系 数据; （4）开发蛋白质到基因组比对仪，协助新物种的大规模基因注释。之上 完成后，拟议的研究将产生面向用户的高性能工具，用于序列比对和 装配速度比当前一代更快、更准确。