Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.
State Key Laboratory of Resource Insects, Institute of Sericulture and Systems Biology, Southwest University, Chongqing, 400715, China.
Sci Rep. 2023 Sep 30;13(1):16451. doi: 10.1038/s41598-023-43825-0.
Multiplex PCR is an increasingly popular method for identifying species, investigating environmental diversity, and conducting phylogenetic analysis. The complexity and increasing availability of diverse templates necessitate a highly automated approach to design degenerate primer pairs for specific targets with multiple sequences. Existing tools for degenerate primer design suffer from poor maintenance, semi-automation, low adaptability, and low tolerance for gaps. We developed PMPrimer, a Python-based tool for automated design and evaluation of multiplex PCR primer pairs for specific targets using diverse templates. PMPrimer automatically designs optimal multiplex PCR primer pairs using a statistical-based template filter; performs multiple sequence alignment, conserved region identification, and primer design; and evaluates the primers based on template coverage, taxon specificity, and target specificity. PMPrimer identifies conserved regions using Shannon's entropy method, tolerates gaps using a haplotype-based method, and evaluates multiplex PCR primer pairs based on template coverage and taxon specificity. We tested PMPrimer using datasets with diverse levels of conservation, sizes, and applications, including tuf genes of Staphylococci, hsp65 genes of Mycobacteriaceae, and 16S ribosomal RNA genes of Archaea. PMPrimer showed outstanding performance compared with existing tools and experimental validated primers. PMPrimer is available as a Python package at https://github.com/AGIScuipeng/PMPrimer .
多重 PCR 是一种越来越流行的方法，用于鉴定物种、研究环境多样性和进行系统发育分析。由于模板的复杂性和多样性不断增加，因此需要一种高度自动化的方法来针对具有多个序列的特定目标设计简并引物对。现有的简并引物设计工具存在维护不善、半自动化、适应性差和对缺口的容忍度低等问题。我们开发了 PMPrimer，这是一个基于 Python 的工具，用于使用多种模板自动设计和评估特定目标的多重 PCR 引物对。PMPrimer 使用基于统计的模板过滤器自动设计最佳的多重 PCR 引物对；执行多序列比对、保守区识别和引物设计；并根据模板覆盖率、分类特异性和目标特异性评估引物。PMPrimer 使用香农熵方法识别保守区，使用基于单倍型的方法容忍缺口，并根据模板覆盖率和分类特异性评估多重 PCR 引物对。我们使用具有不同保护水平、大小和应用的数据集测试了 PMPrimer，包括葡萄球菌的 tuf 基因、分枝杆菌的 hsp65 基因和古菌的 16S 核糖体 RNA 基因。与现有的工具和实验验证的引物相比，PMPrimer 表现出色。PMPrimer 可在 https://github.com/AGIScuipeng/PMPrimer 作为 Python 包获得。
