Shimokawa, E. | bioRxiv Science

bioRxiv2025

MarpolBase: Genome database for Marchantia polymorpha featuring high quality reference genome sequences

The liverwort Marchantia polymorpha is a key model organism for understanding land plant evolution, development, and gene regulation. To support the growing demand for high-quality genomic resources, we present MarpolBase, a comprehensive and integrated genome database that hosts newly assembled, high-accuracy reference genomes for both the male Tak-1 and female Tak-2 accessions, designated as ver. 7.1 reference genomes. These new assemblies, generated using PacBio HiFi long-read sequencing, represent nearly telomere-to-telomere chromosome-level genomes, with improvements in assembly continuity, annotation accuracy, and structural resolution--especially for repeat-rich regions and sex chromosomes. MarpolBase offers not only access to genome sequences and gene annotations but also provides a unified platform for data exploration, comparative analysis, and community-driven gene nomenclature. It includes keyword-searchable gene pages with structural and functional annotations, expression data integration, genome browser visualization, and online analytical and utility tools. By unifying genome assembly, annotation, nomenclature, and analysis tools in a single platform, MarpolBase serves as a central resource for functional genomics and evolutionary studies in M. polymorpha, and a model for future plant genome databases.

genomics↗

bioRxiv2023

Harnessing deep learning to analyze cryptic morphological variability of Marchantia polymorpha

Characterizing phenotypes is a fundamental aspect of biological sciences, although it can be challenging due to various factors. For instance, the liverwort (Marchantia polymorpha), a model system for plant biology, exhibits morphological variability, making it difficult to identify and quantify distinct phenotypic features using objective measures. To address this issue, we utilized a deep learning-based image classifier that can handle plant images directly without manual extraction of phenotypic features, and analyzed bright-field images of M. polymorpha. This dioicous plant species exhibits morphological differences between male and female wild accessions at an early stage of gemmaling growth, although it remains elusive whether the differences are attributable to sexual dimorphism or autosomal genetic variation. To dissect the genomic factors, we established a male and female set of recombinant inbred lines (RILs) from a set of male and female wild accessions. We then trained deep-learning models to classify the sexes of the RILs and the wild accessions. Our results showed that the trained classifiers accurately classified male and female gemmalings of wild accessions in the first week of growth, confirming the intuition of plant researchers in a reproducible and objective manner. In contrast, the RILs were less distinguishable, indicating that the differences between the parental wild accessions arose from autosomal variations instead of sexual dimorphism. Furthermore, we validated our trained models by an "explainable AI" technique that highlights image regions relevant to the classification. Our findings demonstrate that the classifier-based approach provides a powerful tool for analyzing plant species that lack standardized phenotyping metrics.

plant biology↗