链霉菌基因组岛和次生代谢物合成相关的生物信息学工具及数据库

随着DNA测序技术的进步, 迄今为止已有12个链霉菌基因组被测序。面对海量组学的数据, 急需采用生物信息学方法来大规模深度挖掘这些重要微生物资源, 进而实现链霉菌资源挖掘和代谢潜力释放的深度互动。围绕链霉菌基因组比较分析中菌株特有的基因组岛和次生代谢物生物合成基因簇的识别及功能解析等两个常见问题, 本文收集了近期开发的一些常用生物信息学工具和二级数据库。以链霉菌染色体核心区和两臂的划分、天蓝色链霉菌和变铅青链霉菌基因组岛的识别、卡特利链霉菌巨型质粒的鉴别为例, 简介了这些生物信息学资源的使用方法。此外, 还简述了我们课题组进行放线菌型整合性接合元件识别和开发硫肽生物合成基因簇预测新工具的一些尝试。生物信息学工具和二级数据库在链霉菌基因组比较分析中有重要作用, 可将研究重点迅速地聚焦在某株菌的可移动遗传元件和次生代谢物生成基因簇上, 确定其对应的菌株特有表型, 及解析新型化合物生物合成和调控机理。

Bioinformatics tools and databases focused on genomic islands and secondary metabolite biosynthesis of Streptomyces

With many Streptomyces genomic sequences made available by next generation sequencing (NGS), the bioinformatics resource comes as the answer to an acute demand of the big data mining. In this study, I collected the frequently used tools and open-access databases for genome alignment and island detection, suitable for the large Streptomyces genomes with high G+C content. To illustrate these user-friendly tools, I identified the Actinobacteria integrative and conjugative elements in 12 completely sequenced Streptomyces genomes, which were found to be inserted into the core regions of the Streptomyces chromosomes. the bioinformatics resources currently available for secondary metabolites isolated from Streptomyces species are also described briefly, such as the web-based tool ThioFinder to rapidly identify thiopeptide biosynthetic gene cluster from DNA sequence using a profile Hidden Markov Model approach.