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The assay for transposase accessible chromatin (ATAC-seq) is a method for mapping genome-wide chromatin accessibility. Coupled with high-throughput sequencing, it enables integrative epigenomics analyses. ATAC-seq requires direct access to cell nuclei, a major challenge in non-model species such as small invertebrates, whose soft tissue is surrounded by a protective exoskeleton. Here, we present modifications of the ATAC-seq protocol for applications in small crustaceans, extending applications to non-model species. For complete information on the use and execution of this protocol, please refer to Buenrostro et al. (2013).
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http://dx.doi.org/10.1016/j.xpro.2021.100341 | DOI Listing |
Genetics
September 2025
Institute of Ecology and Evolution, School of Biological Sciences, The University of Edinburgh, Edinburgh, EH9 3FL, United Kingdom.
Recent advances in methods to infer and analyse ancestral recombination graphs (ARGs) are providing powerful new insights in evolutionary biology and beyond. Existing inference approaches tend to be designed for use with fully-phased datasets, and some rely on model assumptions about demography and recombination rate. Here I describe a simple model-free approach for genealogical inference along the genome from unphased genotype data called Sequential Tree Inference by Collecting Compatible Sites (sticcs).
View Article and Find Full Text PDFPLoS Genet
September 2025
Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, Minnesota, United States of America.
MicroRNAs (miRNAs) are essential regulators of gene expression, yet few comprehensive databases exist for miRNA expression in non-model species, limiting our ability to characterize their roles in gene regulation, development, and disease. Similarly, isomiRs - length and sequence isoforms of canonical miRNAs with potentially altered regulatory targets and functions - have received even less attention in non-model species, including the horse, leaving a critical gap in our understanding of their biological significance. To address these challenges, we developed an open-source, containerized pipeline for identifying and quantifying miRNAs and isomiRs (FARmiR: Framework for Analysis and Refinement of miRNAs), and an associated interactive browser (AIMEE: Animal IsomiR and MiRNA Expression Explorer).
View Article and Find Full Text PDFNAR Genom Bioinform
September 2025
Ecologie Société et Evolution, CNRS, Universite Paris-Saclay, AgroParisTech, 91198 Gif-sur-Yvette, France.
New reference genomes and transcriptomes are increasingly available across the tree of life, opening new avenues to tackle exciting questions. However, there are still challenges associated with annotating genomes and inferring evolutionary processes and with a lack of methodological standardisation. Here, we propose a new workflow designed for evolutionary analyses to overcome these challenges, facilitating the detection of recombination suppression and its consequences in terms of rearrangements and transposable element accumulation.
View Article and Find Full Text PDFAnimals (Basel)
August 2025
Laboratory of Animal Biotechnology, Federal Rural University of the Semi-Arid, Mossoró 59625-900, RN, Brazil.
The successful application of assisted reproductive technologies (ARTs), such as in vitro maturation (IVM) and artificial oocyte activation, requires species-specific adaptations. Although these methods are routinely used in laboratory rodents, their use in wild or non-model species remains limited, such as the Spix's yellow-toothed cavy, a Neotropical species of ecological and reproductive interest. This study evaluated the effects of different concentrations of epidermal growth factor (EGF; 10 or 50 ng/mL) on IVM (Experiment 1) and of 6-dimethylaminopurine (6-DMAP) on artificial oocyte activation (Experiment 2).
View Article and Find Full Text PDFBrief Bioinform
July 2025
Faculty of Science, University of Melbourne, Grattan Street, Parkville, 3010, VIC, Australia.
Repetitive DNA sequences underpin genome architecture and evolutionary processes, yet they remain challenging to classify accurately. Terrier is a deep learning model designed to overcome these challenges by classifying repetitive DNA sequences using a publicly available, curated repeat sequence library trained under the RepeatMasker schema. Poor representation of taxa within repeat databases often limits the classification accuracy and reproducibility of current repeat annotation methods, limiting our understanding of repeat evolution and function.
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