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.

中文翻译：

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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


随着测序技术的发展，微生物基因组测序分析受到广泛关注。对于没有足够生物信息学技能的经验不足的用户来说，通过读取分析来理解微生物鉴定的测序数据，特别是细菌鉴定仍然具有挑战性。为了解决有效分析基因组信息的挑战，在本文中，我们开发了一种有效的方法和自动生物信息学管道（PBGI），用于细菌基因组识别，使用由细菌产生的短读长或长读长测序数据进行自动和定制的生物信息学分析。 Illumina、PacBio 和 Oxford Nanopore 等多个平台。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通过短读长或长读长分析进行细菌识别，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 免费获取。