Effective identification of bacterial genomes from short and long read sequencing data
Effective identification of bacterial genomes from short and long read sequencing data
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从短读长和长读长测序数据中有效识别细菌基因组
DOI：
10.1109/tcbb.2021.3095164
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通讯作者：
Yongzhuang Liu
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作者：
Jian Liu;Jialiang Sun;Yongzhuang Liu
随着测序技术的发展，微生物基因组测序分析受到了广泛的关注。对于缺乏足够生物信息学技能的缺乏经验的用户来说，通过READ分析来理解用于微生物鉴定，特别是细菌鉴定的测序数据仍然是具有挑战性的。为了应对有效分析基因组信息的挑战，本文开发了一种有效的方法和自动生物信息学流水线PBGI用于细菌基因组鉴定，利用Illumina、PacBio和牛津纳米孔等多个平台产生的短读或长读测序数据进行自动化和定制化的生物信息学分析。在实际数据集上对所提出的方法进行了评估，表明PBGI提供了一种用户友好的方式来通过短或长阅读分析来进行细菌鉴定，并且可以提供准确的分析结果。PBGI的源代码可在https://github.com/lyotvincent/PBGI.上免费获得
With the development of sequencing technology, microbiological genome sequencing analysis has attracted extensive attention. For inexperienced users without sufficient bioinformatics skills, making sense of sequencing data for microbial identification, especially for bacterial identification, through reads analysis is still challenging. In order to address the challenge of effectively analyzing genomic information, in this paper, we develop an effective approach and automatic bioinformatics pipeline called PBGI for bacterial genome identification, performing automatedly and customized bioinformatics analysis using short-reads or long-reads sequencing data produced by multiple platforms such as Illumina, PacBio and Oxford Nanopore. An evaluation of the proposed approach on the practical data set is presented, showing that PBGI provides a user-friendly way to perform bacterial identification through short or long reads analysis, and could provide accurate analyzing results. The source code of the PBGI is freely available at https://github.com/lyotvincent/PBGI.