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.

项目摘要 高性能的计算算法对于大规模生物序列数据的分析至关重要 受到了广泛的关注。几年前甚至十几年前发展起来的，很多主流 用于序列比对、组装和基因组注释的软件包没有充分利用 现代精确的长时间读取数据或无法跟上当前技术的吞吐量。发展 先进算法的发展对测序技术在不久的将来的应用至关重要。基于我们 鉴于上一个供资周期的工作，本项目将通过四项建议来满足这一迫切需要： 用于准确长读取的比对算法和用于更全面比对的高质量组装 通过高度重复的区域和长片段重复;（2）将我们的hifiasm组装程序扩展到 高质量的组装更准确的牛津纳米孔读取现在可用;（3）结合我们的hifiasm 和DIPASM算法，用于更准确和更连续的单体型分辨组装， 数据;（4）开发蛋白质到基因组比对器，以协助新物种的大规模基因注释。后 完成后，拟议的研究将产生高性能的面向用户的序列比对工具， 比当前一代更快、更准确的装配。