Li Yuejuan, Cao Jiabao, Wang Jun
CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology Chinese Academy of Sciences Beijing China.
University of Chinese Academy of Sciences Beijing China.
Imeta. 2023 Oct 12;2(4):e139. doi: 10.1002/imt2.139. eCollection 2023 Nov.
Structural variants (SVs, including large-scale insertions, deletions, inversions, and translocations) significantly impact the functions of genes in the microbial genome, and SVs in the microbiome are associated with diverse biological processes and human diseases. With the advancements in sequencing and bioinformatics technologies, increasingly, sequencing data and analysis tools are already being extensively utilized for microbiome SV analyses, leading to a higher demand for more dedicated SV analysis workflows. Moreover, due to the unique detection biases of various sequencing technologies, including short-read sequencing (such as Illumina platforms) and long-read sequencing (e.g., Oxford Nanopore and PacBio), SV discovery based on multiple platforms is necessary to comprehensively identify the wide variety of SVs. Here, we establish an integrated pipeline MetaSVs combining Nanopore long reads and Illumina short reads to analyze SVs in the microbial genomes from gut microbiome and further identify differential SVs that can be reflective of metabolic differences. Our pipeline provides researchers easy access to SVs and relevant metabolites in the microbial genomes without the requirement of specific technical expertise, which is particularly useful to researchers interested in metagenomic SVs but lacking sophisticated bioinformatic knowledge.
结构变异(SVs,包括大规模插入、缺失、倒位和易位)显著影响微生物基因组中基因的功能,并且微生物组中的SVs与多种生物学过程和人类疾病相关。随着测序和生物信息学技术的进步,测序数据和分析工具越来越多地被广泛用于微生物组SV分析,这导致对更专门的SV分析工作流程的需求增加。此外,由于包括短读长测序(如Illumina平台)和长读长测序(如Oxford Nanopore和PacBio)在内的各种测序技术存在独特的检测偏差,基于多个平台进行SV发现对于全面识别各种各样的SVs是必要的。在这里,我们建立了一个整合流程MetaSVs,它结合了Nanopore长读长和Illumina短读长,用于分析来自肠道微生物组的微生物基因组中的SVs,并进一步识别能够反映代谢差异的差异SVs。我们的流程为研究人员提供了轻松获取微生物基因组中SVs和相关代谢物的途径,而无需特定的技术专长,这对那些对宏基因组SVs感兴趣但缺乏复杂生物信息学知识的研究人员特别有用。