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GetPrimers: A generalized PCR-based genetic targeting primer designer enabling easy and standardized targeted gene modification across multiple systems.

Zepu MiaoHaiting WangXinyu TuZhengshen HuangShujing HuangXinxin ZhangFan WangZhishen HuangHuihui LiYue JiaoSong GaoZhipeng ZhouChun-Min ShanJing LiJia-Xing Yue
Published in: Yeast (Chichester, England) (2023)
Genetic targeting (e.g., gene knockout and tagging) based on polymerase chain reaction (PCR) is a simple yet powerful approach for studying gene functions. Although originally developed in classic budding and fission yeast models, the same principle applies to other eukaryotic systems with efficient homologous recombination. One-step PCR-based genetic targeting is conventionally used but the sizes of the homologous arms that it generates for recombination-mediated genetic targeting are usually limited. Alternatively, gene targeting can also be performed via fusion PCR, which can create homologous arms that are orders of magnitude larger, therefore substantially increasing the efficiency of recombination-mediated genetic targeting. Here, we present GetPrimers (https://www.evomicslab.org/app/getprimers/), a generalized computational framework and web tool to assist automatic targeting and verification primer design for both one-step PCR-based and fusion PCR-based genetic targeting experiments. Moreover, GetPrimers by design runs for any given genetic background of any species with full genome scalability. Therefore, GetPrimers is capable of empowering high-throughput functional genomic assays at multipopulation and multispecies levels. Comprehensive experimental validations have been performed for targeting and verification primers designed by GetPrimers across multiple organism systems and experimental setups. We anticipate GetPrimers to become a highly useful and popular tool to facilitate easy and standardized gene modification across multiple systems.
Keyphrases
  • genome wide
  • copy number
  • cancer therapy
  • dna repair
  • high throughput
  • dna methylation
  • oxidative stress
  • gene expression
  • machine learning
  • genome wide identification
  • deep learning
  • real time pcr
  • neural network