368 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 integrates meta-QTL analysis and functional genomics to identify shared and stress-specific genomic pathways, including key candidate genes and physiological mechanisms, that underlie rice adaptation to aluminum, cadmium, and manganese toxicities in acidic soils.
This study presents the SheepGTEx resource, a comprehensive multi-tissue atlas derived from 6,761 RNA-sequencing samples across 51 tissues and multiple sheep breeds, which identifies millions of regulatory variants to elucidate the genetic mechanisms underlying complex traits, population divergence, and adaptive evolution in sheep.
This study reveals that the autophagy protein Atg8/LC3 orchestrates stress-responsive transcriptional programs in both immunity and metabolism by directly interacting with the NF-κB transcription factor Dif via conserved motifs to regulate its nuclear accumulation and chromatin occupancy, thereby enabling cells to coordinate defense and metabolic adaptation during infection and nutrient surplus.
The paper introduces PRISMA, a novel tensor-based framework that deconstructs complex-disease GWAS signals into tissue-resolved genetic trajectories by integrating summary statistics with multi-tissue eQTL data, successfully revealing distinct vascular, immune, and neurodegenerative axes in diabetic retinopathy that traditional methods fail to capture.
This study presents a comprehensive multi-omics resource, including whole-genome sequencing, proteomics, and metabolomics data from three coral genera in Ulithi Atoll, to investigate the molecular mechanisms by which human disturbance impacts coral health and resilience.
This study demonstrates that standard polyA-enriched RNA-seq datasets can be repurposed to quantitatively profile metabolically active phyllosphere fungal communities, revealing widespread and biologically structured host genetic control over these microbial assemblages across multiple crop species.
This study demonstrates that active learning significantly improves the data efficiency of sequence-to-expression models by identifying informative sequences with distinct biological signatures, establishing it as a practical tool for iterative lab-in-the-loop refinement.
This study systematically characterizes genomic scars from alternative end joining and single-strand annealing across 2,157 tumors, revealing that while alternative end joining is the predominant backup repair in homologous recombination-deficient cancers, both pathways are active beyond HR deficiency and are shaped by distinct local genomic and transcriptional contexts.
The paper introduces Carbon, a family of efficient, domain-adapted generative DNA language models that utilize non-overlapping 6-mer tokenization and specialized training objectives to achieve competitive performance and significantly faster inference compared to existing large-scale genomic models, thereby demonstrating the importance of aligning model design with the unique statistical and biological properties of DNA.
The WaveSeekerNet model accurately predicts influenza A virus host origins and reveals distinct genomic adaptation patterns between avian and mammalian hosts, offering a quantitative framework for assessing zoonotic risk and identifying key adaptive mutations.