309 papers
Genomics is the study of an organism's complete set of DNA, offering a deep dive into the biological instructions that shape life. This field explores how genetic information influences traits, health, and evolution, moving beyond single genes to understand the complex interplay within entire genomes. From uncovering the roots of disease to mapping biodiversity, genomics provides the foundational data for many modern medical breakthroughs.
At Gist.Science, we process every new preprint in this category as it appears on bioRxiv, ensuring you stay ahead of the curve. Each paper is accompanied by both a clear, plain-language overview and a detailed technical summary, making cutting-edge research accessible to everyone regardless of their background. Below are the latest papers in genomics, freshly summarized and ready for you to explore.
This study demonstrates that while the yeast helicase Ded1 is critical for translating mRNAs with structured 5'UTRs, its primary mechanism of action is the unwinding of secondary structures rather than the widespread suppression of upstream ORF translation.
This paper introduces EVEE, an interactive web resource that leverages embeddings from the Evo 2 genomic foundation model to achieve state-of-the-art, interpretable pathogenicity predictions for diverse genetic variants across a unified framework, transforming interpretability from a trade-off into a complementary product of learned biological structure.
This study utilizes genomic epidemiology and phylodynamic modeling to track the 2017–2023 *Mycoplasma bovis* outbreak in New Zealand, demonstrating how movement restrictions and culling successfully reduced transmission and eliminated major lineages by 2020, while highlighting the critical role of integrated genomic surveillance in guiding eradication efforts.
This study establishes high-quality genomic and transcriptomic resources for the invasive tunicate *Ascidiella aspersa* and conducts a comprehensive comparative analysis across 35 other tunicate species to reveal class-level genomic features, evolutionary diversification patterns, and a new phylogenetic hypothesis, all accessible through the newly constructed TUNOME database.
Deep-Plant is a supervised foundation model trained on diverse plant chromatin data that outperforms self-supervised language models in speed, accuracy, and interpretability for predicting regulatory genomic activity across species like Arabidopsis and rice.
This study identifies a second thyroglobulin-like gene, HaVipR2, as a novel mediator of Vip3Aa resistance in *Helicoverpa armigera*, demonstrating that a ~16 kb transposable element insertion disrupts this gene to confer high-level resistance and highlighting the critical role of long-read sequencing in detecting such adaptive mutations.
This study analyzes whole-genome data from wild and captive cheetahs to reveal that deleterious mutations are significantly enriched in sperm-related genes, likely contributing to poor sperm quality, while confirming that captive breeding programs have successfully maintained genetic diversity comparable to wild populations.
By analyzing single-molecule duplex sequencing data from normal kidney and blood samples across 10 countries, researchers discovered that kidney proximal tubule cells accumulate high levels of somatic mutations from both known exogenous carcinogens, such as aristolochic acids, and unidentified systemic mutagens, revealing these cells as highly sensitive sentinels for detecting widespread environmental exposures.
This study demonstrates that while graph-based pangenomes and linear references yield similar Mendelian inheritance error rates, pangenome approaches significantly improve the reconstruction of parental haplotype blocks in citrus breeding, offering a superior framework for benchmarking SNP-calling accuracy in non-model systems by mitigating reference bias in diverged genomic regions.
This study systematically evaluates genomic features to determine their ability to distinguish functional genes from non-genic regions, concluding that evolutionary conservation and transcriptional activity are the most robust and consistent indicators of both protein-coding and non-coding gene function.