LI-Detector: a Method for Curating Ordered Gene-Replacement Libraries

Journal article

In recent years the availability of genome sequence information has grown logarithmically resulting in the identification of a plethora of uncharacterized genes. To address this gap in functional annotation, many high-throughput screens have been devised to uncover novel gene functions. Gene-replacement libraries are one such tool that can be screened in a high-throughput way to link genotype and phenotype and are key community resources. However, for a phenotype to be attributed to a specific gene, there needs to be confidence in the genotype. Construction of large libraries can be laborious and occasionally errors will arise. Here, we present a rapid and accurate method for the validation of any ordered library where a gene has been replaced or disrupted by a uniform linear insertion (LI). We applied our method (LI-detector) to the well-known Keio library of Escherichia coli gene-deletion mutants. Our method identified 3,718 constructed mutants out of a total of 3,728 confirmed isolates, with a success rate of 99.7% for identifying the correct kanamycin cassette position. This data set provides a benchmark for the purity of the Keio mutants and a screening method for mapping the position of any linear insertion, such as an antibiotic resistance cassette in any ordered library. IMPORTANCE The construction of ordered gene replacement libraries requires significant investment of time and resources to create a valuable community resource. During construction, technical errors may result in a limited number of incorrect mutants being made. Such mutants may confound the output of subsequent experiments. Here, using the remarkable E. coli Keio knockout library, we describe a method to rapidly validate the construction of every mutant.

Authors: Goodall, ECA; Morris, FC; McKeand, SA; Sullivan, R; Warner, IA

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: American Society for Microbiology
• Journal: Microbiology Spectrum
• Volume: 10
• Issue: 4
• Pages: e0083322-e0083322
• Publication date: 2022-07-20
• DOI: 10.1128/spectrum.00833-22
• EISSN: 2165-0497
• ISSN: 2165-0497
• Language: English
• Keywords: Process (computing); Biology; Computational biology; Data mining; Operating system; Detector; Political science; Mutant; Computer network; Computer science; Resource (disambiguation); Telecommunications; Information retrieval; Investment (military); Gene; Genetics