Background: Polyploidy is widely recognized as a significant evolutionary force in the plant kingdom, contributing to the diversification of plants. One of the notable features of allopolyploidy is the occurrence of homoeologous exchange (HE) events between the subgenomes, causing changes in genomic composition, gene expression, and phenotypic variations. However, the role of HE in plant adaptation and domestication remains unclear.
Results: Here we analyze the whole-genome resequencing data from Brassica napus accessions representing the different morphotypes and ecotypes, to investigate the role of HE in domestication. Our findings demonstrate frequent occurrence of HEs in Brassica napus, with substantial HE patterns shared across populations, indicating their potential role in promoting crop domestication. HE events are asymmetric, with the A genome more frequently replacing C genome segments. These events show a preference for specific genomic regions and vary among populations. We also identify candidate genes in HE regions specific to certain populations, which likely contribute to flowering-time diversification across diverse morphotypes and ecotypes. In addition, we assemble a new genome of a swede accession, confirming the HE signals on the genome and their potential involvement in root tuber development. By analyzing HE in another allopolyploid species, Brassica juncea, we characterize a potential broader role of HE in allopolyploid crop domestication.
Conclusions: Our results provide novel insights into the domestication of polyploid Brassica species and highlight homoeologous exchange as a crucial mechanism for generating variations that are selected for crop improvement in polyploid species.
Background: The design of nucleotide sequences with defined properties is a long-standing problem in bioengineering. An important application is protein expression, be it in the context of research or the production of mRNA vaccines. The rate of protein synthesis depends on the 5' untranslated region (5'UTR) of the mRNAs, and recently, deep learning models were proposed to predict the translation output of mRNAs from the 5'UTR sequence. At the same time, large data sets of endogenous and reporter mRNA translation have become available.
Results: In this study, we use complementary data obtained in two different cell types to assess the accuracy and generality of currently available models for predicting translational output. We find that while performing well on the data sets on which they were trained, deep learning models do not generalize well to other data sets, in particular of endogenous mRNAs, which differ in many properties from reporter constructs.
Conclusions: These differences limit the ability of deep learning models to uncover mechanisms of translation control and to predict the impact of genetic variation. We suggest directions that combine high-throughput measurements and machine learning to unravel mechanisms of translation control and improve construct design.
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