PuckerLab publications (@puckerlabpapers.bsky.social)

New tool alert: MGSE estimates genome size from read mappings — works with short & long reads, even at low coverage. A mapping-based alternative to k-mer methods. 🧬 🔗 doi.org/10.1186/s128... #Genomics #Bioinformatics

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Mapping-based genome size estimation - BMC Genomics While the size of chromosomes can be measured under a microscope, obtaining the exact size of a genome remains a challenge. Biochemical methods and k-mer distribution-based approaches allow only estimations. An alternative approach to estimate the genome size based on high contiguity assemblies and read mappings is presented here. Analyses of Arabidopsis thaliana and Beta vulgaris data sets are presented to show the impact of different parameters. Oryza sativa, Brachypodium distachyon, Solanum lycopersicum, Vitis vinifera, and Zea mays were also analyzed to demonstrate the broad applicability of this approach. Further, MGSE was also used to analyze Escherichia coli, Saccharomyces cerevisiae, and Caenorhabditis elegans datasets to show its utility beyond plants. Mapping-based Genome Size Estimation (MGSE) and additional scripts are available on GitHub: https://github.com/bpucker/MGSE . MGSE predicts genome sizes based on short reads or long reads requiring a minimal coverage of 5-fold. https://doi.org/10.1186/s12864-025-11640-8