Kohl Thomas Andreas, Utpatel Christian, Schleusener Viola, De Filippo Maria Rosaria, Beckert Patrick, Cirillo Daniela Maria, Niemann Stefan
Molecular and Experimental Mycobacteriology, Research Center Borstel, Borstel, Germany.
Emerging Bacterial Pathogens Unit, Division of Immunology, Transplantation and Infectious Diseases, IRCCS San Raffaele Scientific Institute, Milan, Italy.
PeerJ. 2018 Nov 13;6:e5895. doi: 10.7717/peerj.5895. eCollection 2018.
Analyzing whole-genome sequencing data of complex (MTBC) isolates in a standardized workflow enables both comprehensive antibiotic resistance profiling and outbreak surveillance with highest resolution up to the identification of recent transmission chains. Here, we present MTBseq, a bioinformatics pipeline for next-generation genome sequence data analysis of MTBC isolates. Employing a reference mapping based workflow, MTBseq reports detected variant positions annotated with known association to antibiotic resistance and performs a lineage classification based on phylogenetic single nucleotide polymorphisms (SNPs). When comparing multiple datasets, MTBseq provides a joint list of variants and a FASTA alignment of SNP positions for use in phylogenomic analysis, and identifies groups of related isolates. The pipeline is customizable, expandable and can be used on a desktop computer or laptop without any internet connection, ensuring mobile usage and data security. MTBseq and accompanying documentation is available from https://github.com/ngs-fzb/MTBseq_source.
以标准化流程分析复杂结核分枝杆菌复合群（MTBC）分离株的全基因组测序数据，既能进行全面的抗生素耐药性分析，又能以最高分辨率开展疫情监测，直至识别出近期的传播链。在此，我们介绍MTBseq，这是一种用于MTBC分离株下一代基因组序列数据分析的生物信息学流程。MTBseq采用基于参考图谱的工作流程，报告检测到的与抗生素耐药性具有已知关联的变异位点，并基于系统发育单核苷酸多态性（SNP）进行谱系分类。在比较多个数据集时，MTBseq会提供一个联合变异列表以及用于系统发育基因组分析的SNP位置的FASTA比对，并识别相关分离株组。该流程可定制、可扩展，无需任何网络连接即可在台式计算机或笔记本电脑上使用，确保了移动使用和数据安全。MTBseq及相关文档可从https://github.com/ngs-fzb/MTBseq_source获取。
