1018 papers
Bioinformatics sits at the exciting intersection where biology meets data science, using powerful computer tools to decode the vast complexity of living systems. From mapping the human genome to tracking how viruses evolve, this field transforms raw biological information into actionable insights that drive modern medicine and research forward without requiring a supercomputer to understand the basics.
On Gist.Science, we ensure you never miss a breakthrough by processing every new preprint in this category directly from bioRxiv. Our team provides both plain-language explanations and detailed technical summaries for each paper, making cutting-edge discoveries accessible to everyone regardless of their background.
Below are the latest bioinformatics papers added from bioRxiv, ready for you to explore with clarity and depth.
This paper introduces MutationNetwork, a graph-based framework that integrates long-range intrachromosomal interactions with local genomic overlaps to construct mutation-centric networks, enabling the effective clustering of breast cancer subtypes and the prioritization of non-coding driver mutations through network-level impact analysis.
VICAST is an integrated software toolkit designed to streamline viral passage studies by combining semi-automated genome annotation with manual curation and optimized low-frequency variant calling, offering superior speed and accuracy compared to existing tools for diverse viral families.
This paper introduces SLAB, an efficient sweep line algorithm based on PBWT that identifies width-maximal haplotype block cores to reveal population genetic insights and selection signals complementary to traditional IBD rate analyses.
OmicClaw is an executable natural-language framework built on the unified OmicVerse ecosystem and J.A.R.V.I.S. runtime that transforms fragmented multi-omics analysis into reproducible, traceable workflows by converting user requests into validated actions over live data objects.
This study develops a phenotype-structured mathematical model to demonstrate how intra-tumor proliferation heterogeneity and life-history trade-offs drive eco-evolutionary dynamics, revealing that while all treatments slow tumor growth, they induce distinct evolutionary trajectories by selectively enriching either fast- or slow-proliferating clones depending on the specific therapeutic targeting strategy.
This paper introduces 3D-Manhattan, a browser-based interactive tool that visualizes multiple GWAS results in a unified 3D coordinate system to facilitate the comparative analysis of genetic associations across time, traits, or experimental conditions.
This study presents an integrated artificial intelligence and quantum chemistry framework to rationally design and computationally validate "Solres," a novel antibacterial agent targeting key virulence proteins in *Ralstonia solanacearum* to combat antimicrobial resistance in agriculture.
RNAElectra is a novel RNA foundation model that leverages ELECTRA-style replaced-token detection pretraining on diverse non-coding RNAs to achieve superior cross-task generalization and interpretability in RNA regulatory inference compared to traditional masked language modeling approaches.
The paper introduces ATOMICA, a geometric deep learning model trained on over two million complexes to generate universal, multiscale atomic representations of intermolecular interfaces across five molecular modalities, demonstrating superior performance in structure-function benchmarks and successfully predicting functional ligands for previously uncharacterized "dark" protein pockets.
This study presents AFESM, a comprehensive dataset of 820 million predicted protein structures derived from AlphaFold2 and ESMfold, which reveals millions of structural clusters, uncovers novel domain folds and combinations, and highlights the critical role of metagenomic data and prediction quality in expanding our understanding of the protein structure universe.