1205 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 study reveals that in *Caenorhabditis elegans*, strain-specific structural variant architectures significantly influence mutation retention dynamics following mutagenesis, with higher outcrossing rates paradoxically leading to greater structural variant burdens and reduced purging of deleterious mutations.
This paper introduces the Tiling Algorithm, a reference-free method for characterizing the structural diversity and minor species of Adeno-associated virus (AAV) genomes using accurate long-read sequencing data, thereby overcoming the limitations of alignment-based approaches in handling AAV-specific challenges like inverted terminal repeats and replication artifacts.
RNApdbee 3.0 is a unified web server that provides comprehensive RNA secondary structure annotation by integrating 2D and 3D data to classify diverse nucleotide interactions, handle structural inconsistencies, and generate standardized outputs and visualizations from 3D coordinates.
KyDab is a comprehensive, publicly accessible database that curates over 120,000 paired antibody sequences and full-funnel selection data from standardized Kymouse immunization studies to support the development and evaluation of artificial intelligence models for antibody discovery.
This paper introduces a reinforcement learning framework to fine-tune diffusion-based molecular docking models, significantly enhancing their ability to generate physically valid and interaction-preserving poses without compromising structural accuracy or increasing inference-time computation.
This paper introduces two maximum likelihood methods that incorporate lineage abundance priors—interpreting multifurcations as unresolved signals of common strains or modeling sequencing rates proportional to prevalence—to significantly improve the accuracy of phylogenetic inference for rapidly evolving pathogens like SARS-CoV-2 by prioritizing the placement of sequences onto common ("horse") rather than rare ("zebra") lineages.
The paper introduces Amaranth, a novel single-cell transcript assembler that significantly improves full-length transcript reconstruction accuracy by employing discriminative modeling to address the distinct biological and statistical biases between UMI-linked and internal reads in Smart-seq protocols.
The paper introduces PerturbGraph, a graph neural network framework that integrates biological priors such as protein-protein interaction networks and functional annotations to accurately predict transcriptional responses for unseen gene perturbations, significantly outperforming existing machine learning and deep learning baselines.
Nextstrain is an automated platform that utilizes open genomic data to provide continually updated, real-time phylodynamic surveillance and visualization for 21 viruses and *Mycobacterium tuberculosis*, thereby enabling targeted public health interventions.
FoundedPBI is an ensemble deep learning framework that leverages genomic foundation models and novel long-context aggregation strategies to accurately predict phage-bacterium interactions from DNA sequences, significantly outperforming existing state-of-the-art methods on benchmark datasets.