An effective strategy for assembling the sex-limited chromosome

IF 11.8 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES
Xiao-Bo Wang, Hong-Wei Lu, Qing-You Liu, A-Lun Li, Hong-Ling Zhou, Yong Zhang, Tian-Qi Zhu, Jue Ruan
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引用次数: 0

Abstract

Background Most currently available reference genomes lack the sequence map of sex-limited (such as Y and W) chromosomes, which results in incomplete assemblies that hinder further research on sex chromosomes. Recent advancements in long-read sequencing and population sequencing have provided the opportunity to assemble sex-limited chromosomes without the traditional complicated experimental efforts. Findings We introduce the first computational method, Sorting long Reads of Y or other sex-limited chromosome (SRY), which achieves improved assembly results compared to flow sorting. Specifically, SRY outperforms in the heterochromatic region and demonstrates comparable performance in other regions. Furthermore, SRY enhances the capabilities of the hybrid assembly software, resulting in improved continuity and accuracy. Conclusions Our method enables true complete genome assembly and facilitates downstream research of sex-limited chromosomes.
组装性别限制染色体的有效策略
背景 目前可用的大多数参考基因组都缺乏性限(如 Y 和 W)染色体的序列图,导致组装不完整,阻碍了对性染色体的进一步研究。最近在长线程测序和群体测序方面取得的进展为我们提供了无需传统的复杂实验工作就能组装性限染色体的机会。研究结果 我们介绍了第一种计算方法--Y或其他性别限制染色体长读数排序法(SRY),与流式排序法相比,SRY的组装结果更好。具体来说,SRY 在异染色质区域的表现更好,在其他区域的表现也不相上下。此外,SRY 还增强了混合组装软件的功能,从而提高了连续性和准确性。结论 我们的方法实现了真正的全基因组组装,并促进了性别限制染色体的下游研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
GigaScience
GigaScience MULTIDISCIPLINARY SCIENCES-
CiteScore
15.50
自引率
1.10%
发文量
119
审稿时长
1 weeks
期刊介绍: GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.
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