U.S. Army Combat Capabilities Development Command Chemical Biological Center, Aberdeen Proving Ground, MD, 21010, USA.
Excet, Inc., Springfield, VA, 22150, USA.
BMC Bioinformatics. 2021 Mar 1;22(1):98. doi: 10.1186/s12859-021-04019-5.
Tracking dispersal of microbial populations in the environment requires specific detection methods that discriminate between the target strain and all potential natural and artificial interferents, including previously utilized tester strains. Recent work has shown that genomic insertion of short identification tags, called "barcodes" here, allows detection of chromosomally tagged strains by real-time PCR. Manual design of these barcodes is feasible for small sets, but expansion of the technique to larger pools of distinct and well-functioning assays would be significantly aided by software-guided design.
Here we introduce barCoder, a bioinformatics tool that facilitates the process of creating sets of uniquely identifiable barcoded strains. barCoder utilizes the genomic sequence of the target strain and a set of user-specified PCR parameters to generate a list of suggested barcode "modules" that consist of binding sites for primers and probes, and appropriate spacer sequences. Each module is designed to yield optimal PCR amplification and unique identification. Optimal amplification includes metrics such as ideal melting temperature and G+C content, appropriate spacing, and minimal stem-loop formation; unique identification includes low BLAST hits against the target organism, previously generated barcode modules, and databases (such as NCBI). We tested the ability of our algorithm to suggest appropriate barcodes by generating 12 modules for Bacillus thuringiensis serovar kurstaki-a simulant for the potential biowarfare agent Bacillus anthracis-and three each for other potential target organisms with variable G+C content. Real-time PCR detection assays directed at barcodes were specific and yielded minimal cross-reactivity with a panel of near-neighbor and potential contaminant materials.
The barCoder algorithm facilitates the generation of synthetically barcoded biological simulants by (a) eliminating the task of creating modules by hand, (b) minimizing optimization of PCR assays, and (c) reducing effort wasted on non-unique barcode modules.
追踪环境中微生物种群的扩散需要特定的检测方法,这些方法能够区分目标菌株和所有潜在的自然和人工干扰物,包括以前使用过的测试菌株。最近的研究表明,在基因组中插入短的识别标签,即“条形码”,可以通过实时 PCR 检测到染色体标记的菌株。这些条形码的手动设计对于小的集合是可行的,但是通过软件引导的设计将这项技术扩展到更大的、具有独特功能的检测池将大大得到辅助。
在这里,我们介绍了 barCoder,这是一种生物信息学工具,可简化创建独特可识别条形码菌株集的过程。barCoder 利用目标菌株的基因组序列和一组用户指定的 PCR 参数,生成一系列建议的条形码“模块”列表,这些模块由引物和探针的结合位点以及适当的间隔序列组成。每个模块旨在产生最佳的 PCR 扩增和独特识别。最佳扩增包括理想的熔解温度和 G+C 含量、适当的间隔、最小的茎环形成等指标;独特识别包括针对目标生物、先前生成的条形码模块和数据库(如 NCBI)的低 BLAST 命中。我们通过为苏云金芽孢杆菌 serovar kurstaki 生成 12 个模块来测试我们的算法生成合适条形码的能力,苏云金芽孢杆菌 serovar kurstaki 是潜在生物战剂炭疽芽孢杆菌的模拟物,并且为其他潜在目标生物生成了 3 个模块,这些目标生物的 G+C 含量不同。针对条形码的实时 PCR 检测分析具有特异性,并且与一组近邻和潜在污染物材料的交叉反应最小。
barCoder 算法通过(a)消除手动创建模块的任务,(b)最小化 PCR 分析的优化,以及(c)减少浪费在非独特条形码模块上的工作,促进了合成条形码生物模拟物的生成。