Abstract
The molecular mechanism associated with mammalian meiosis has yet to be fully explored, and one of the main reasons for this lack of exploration is that some meiosis-essential genes are still unknown. The profiling of gene expression during spermatogenesis has been performed in previous studies, yet few studies have aimed to find new functional genes. Since there is a huge gap between the number of genes that are able to be quantified and the number of genes that can be characterized by phenotype screening in one assay, an efficient method to rank quantified genes according to phenotypic relevance is of great importance. We proposed to rank genes by the probability of their function in mammalian meiosis based on global protein abundance using machine learning. Here, nine types of germ cells focusing on continual substages of meiosis prophase I were isolated, and the corresponding proteomes were quantified by high-resolution MS. By combining meiotic labels annotated from the mouse genomics informatics mouse knockout database and the spermatogenesis proteomics dataset, a supervised machine learning package, FuncProFinder (https://github.com/sjq111/FuncProFinder), was developed to rank meiosis-essential candidates. Of the candidates whose functions were unannotated, four of 10 genes with the top prediction scores, Zcwpw1, Tesmin, 1700102P08Rik, and Kctd19, were validated as meiosis-essential genes by knockout mouse models. Therefore, mammalian meiosis-essential genes could be efficiently predicted based on the protein abundance dataset, which provides a paradigm for other functional gene mining from a related abundance dataset.