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Summary: Genome merging is a common phenomenon causing a wide range of consequences on phenotype, adaptation, and gene expression, yet its broader implications are not well‐understood. Two consequences of genome merging on gene expression remain particularly poorly understood: dosage effects and evolution of expression.We employed Chlamydomonas reinhardtii as a model to investigate the effects of asymmetric genome merging by crossing a diploid with a haploid strain to create a novel triploid line. Five independent clonal lineages derived from this triploid line were evolved for 425 asexual generations in a laboratory natural selection experiment.Utilizing fitness assays, flow cytometry, and RNA‐Seq, we assessed the immediate consequences of genome merging and subsequent evolution. Our findings reveal substantial alterations in genome size, gene expression, protein homeostasis, and cytonuclear stoichiometry. Gene expression exhibited expression‐level dominance and transgressivity (i.e. expression level higher or lower than either parent). Ongoing expression‐level dominance and a pattern of 'functional dominance' from the haploid parent was observed.Despite major genomic and nucleo‐cytoplasmic disruptions, enhanced fitness was detected in the triploid strain. By comparing gene expression across generations, our results indicate that proteostasis restoration is a critical component of rapid adaptation following genome merging in Chlamydomonas reinhardtii and possibly other systems. [ABSTRACT FROM AUTHOR]

Cannabis sativa L., a plant originating from Central Asia, is a versatile crop with applications spanning textiles, construction, pharmaceuticals, and food products. This study aimed to compile and analyze publicly available Cannabis RNA‐Seq data and develop an integrated database tool to help advance Cannabis research in various topics such as fiber production, cannabinoid biosynthesis, sex determination, and plant development. We identified 515 publicly available RNA‐Seq samples that, after stringent quality control, resulted in a high‐quality dataset of 394 samples. Utilizing the Jamaican Lion genome as reference, we constructed a comprehensive database and developed the Cannabis Expression Atlas (https://cannatlas.venanciogroup.uenf.br/), a web application for visualization of gene expression, annotation, and functional classification. Key findings include the quantification of 27,640 Cannabis genes and their classification into seven expression categories: not‐expressed, low‐expressed, housekeeping, tissue‐specific, group‐enriched, mixed, and expressed‐in‐all tissues. The study revealed substantial variability and coherence in gene expression across different tissues and chemotypes. We found 2,382 tissue‐specific genes, including 177 transcription factors. The Cannabis Expression Atlas constitutes a valuable tool for exploring gene expression patterns and offers insights into Cannabis biology, supporting research in plant breeding, genetic engineering, biochemistry, and functional genomics. [ABSTRACT FROM AUTHOR]

Premise: In plants, whole‐genome duplication (WGD) is a common mutation with profound evolutionary potential. Given the costs associated with a superfluous genome copy, polyploid establishment is enigmatic. However, in the right environment, immediate phenotypic changes following WGD can facilitate establishment. Metabolite abundances are the direct output of the cell's regulatory network and determine much of the impact of environmental and genetic change on the phenotype. While it is well known that an increase in the bulk amount of genetic material can increase cell size, the impact of gene dosage multiplication on the metabolome remains largely unknown. Methods: We used untargeted metabolomics on four genetically distinct diploid‐neoautotetraploid pairs of the greater duckweed, Spirodela polyrhiza, to investigate how WGD affects metabolite abundances per cell and per biomass. Results: Autopolyploidy increased metabolite levels per cell, but the response of individual metabolites varied considerably. However, the impact on metabolite level per biomass was restricted because the increased cell size reduced the metabolite concentration per cell. Nevertheless, we detected both quantitative and qualitative effects of WGD on the metabolome. Many effects were strain‐specific, but some were shared by all four strains. Conclusions: The nature and impact of metabolic changes after WGD depended strongly on the genotype. Dosage effects have the potential to alter the plant metabolome qualitatively and quantitatively, but were largely balanced out by the reduction in metabolite concentration due to an increase in cell size in this species. [ABSTRACT FROM AUTHOR]

Comparative genomics has become an indispensable part of modern biology due to the advancements in high‐throughput sequencing technologies and the accumulation of genomic data in public databases. However, the quality of genomic data and the choice of parameters used in software tools used for comparative genomics can greatly impact the accuracy of results.Here, we present cogeqc, an R/Bioconductor package that provides researchers with a toolkit to assess genome assembly and annotation quality, orthogroup inference, and synteny detection. The package offers context‐guided assessments of assembly and annotation statistics by comparing observed statistics to those of closely‐related species on NCBI. To assess orthogroup inference, cogeqc calculates a protein domain‐aware orthogroup score that aims at maximising the number of shared protein domains within the same orthogroup. The assessment of synteny detection consists in representing anchor gene pairs as a synteny network and analysing its graph properties, such as clustering coefficient, node count, and scale‐free topology fit.The application of cogeqc to real datasets allowed for an evaluation of multiple parameter combinations for orthogroup inference and synteny detection, providing researchers in need for comparative genomics with guidelines to aid in the selection of the most appropriate tools and parameters for their specific data.We demonstrate that the default parameters in orthogroup identification and synteny detection tools are not always the most suitable, highlighting the importance of performing assessments for each dataset. The assessment metrics provided by cogeqc will help researchers generate more accurate and reliable results. [ABSTRACT FROM AUTHOR]

Summary: Interpreting and visualizing synteny relationships across several genomes is a challenging task. We previously proposed a network-based approach for better visualization and interpretation of large-scale microsynteny analyses. Here, we present syntenet, an R package to infer and analyze synteny networks from whole-genome protein sequence data. The package offers a simple and complete framework, including data preprocessing, synteny detection and network inference, network clustering and phylogenomic profiling, and microsynteny-based phylogeny inference. Graphical functions are also available to create publication-ready plots. Synteny networks inferred with syntenet can highlight taxon-specific gene clusters that likely contributed to the evolution of important traits, and microsynteny-based phylogenies can help resolve phylogenetic relationships under debate., Availability and Implementation: syntenet is available on Bioconductor (https://bioconductor.org/packages/syntenet), and the source code is available on a GitHub repository (https://github.com/almeidasilvaf/syntenet)., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press.)