Advanced computational methods in analyzing high-throughput sequencing data

项目总结 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 并受到了广泛的关注。在几年前甚至十多年前发展起来，许多主流 用于序列比对、组装和基因组注释的软件包没有充分利用 现代准确的长读数据或跟不上当前技术的吞吐量。最新进展 先进算法的研究对测序技术在不久的将来的应用至关重要。基于我们的 在前一个供资周期中，该项目将通过四项建议来解决这一紧迫需求：(1)制定 用于精确长读和高质量组装的比对算法可实现更全面的比对 通过高度重复的区域和长片段复制；(2)将我们的Hifiasm组装器扩展到 高质量组装当今可用的更准确的牛津纳米孔读数；(3)结合我们的HIFIASM 和DIPASM算法，用于更准确和更连续的单倍型解析组装，而不需要谱系 数据；(4)开发蛋白质到基因组的比对工具，以辅助新物种的大规模基因注释。vt.在.的基础上 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和 比当前一代更快、更准确的程序集。

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.