A machine learning enhanced EMS mutagenesis probability map for efficient identification of causal mutations in Caenorhabditis elegans

• Published in: PLoS genetics Vol. 20; no. 8; p. e1011377
• Main Authors: Guo, Zhengyang, Wang, Shimin, Wang, Yang, Wang, Zi, Ou, Guangshuo
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 26.08.2024; Public Library of Science (PLoS)
• Subjects: Caenorhabditis elegans; Chemical mutagenesis; Chromatin; Chromosome mapping; DNA sequencing; Gene mutations; Genetic aspects; Genetic research; Genetic screening; Genomes; Genomics; Machine learning; Methane; Methods; Nucleotide sequencing; Organosulfur compounds; China
• ISSN: 1553-7404; 1553-7390; 1553-7404
• DOI: 10.1371/journal.pgen.1011377

Chemical mutagenesis-driven forward genetic screens are pivotal in unveiling gene functions, yet identifying causal mutations behind phenotypes remains laborious, hindering their high-throughput application. Here, we reveal a non-uniform mutation rate caused by Ethyl Methane Sulfonate (EMS) mutagenesis in the C . elegans genome, indicating that mutation frequency is influenced by proximate sequence context and chromatin status. Leveraging these factors, we developed a machine learning enhanced pipeline to create a comprehensive EMS mutagenesis probability map for the C . elegans genome. This map operates on the principle that causative mutations are enriched in genetic screens targeting specific phenotypes among random mutations. Applying this map to Whole Genome Sequencing (WGS) data of genetic suppressors that rescue a C . elegans ciliary kinesin mutant, we successfully pinpointed causal mutations without generating recombinant inbred lines. This method can be adapted in other species, offering a scalable approach for identifying causal genes and revitalizing the effectiveness of forward genetic screens.