1197 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.
Pop-Corn is a novel computational method that directly predicts how genetic perturbations reshape cell-type and cell-state compositions in both single-cell and spatial contexts, outperforming traditional gene-expression-based approaches and enabling the prioritization of perturbations for experimental follow-up.
The paper introduces Haruka, a spatially aware contrastive learning framework that disentangles condition-specific microenvironmental changes from shared tissue architecture to systematically analyze perturbation-driven heterogeneity across diverse spatial omics platforms and disease contexts.
The paper introduces seqsizzle, an open-source, cross-platform command-line tool written in Rust that facilitates the visualization, quality control, and troubleshooting of long-read sequencing data by enabling fuzzy primer matching, customizable highlighting, and built-in k-mer enrichment analysis.
The paper introduces Adaptive-Banding Needleman-Wunsch (AB-NW), a method that leverages protein language model confidence to dynamically prune the search space of dynamic programming alignment, achieving near-exact accuracy while significantly reducing computational complexity and enabling high-throughput processing of large, challenging protein sequences.
The paper introduces EISCA and EISTA, standardized, modular, and scalable Nextflow pipelines built on the nf-core framework that provide comprehensive, end-to-end solutions for analyzing single-cell and high-resolution spatial transcriptomics data, respectively, to accelerate reproducible biological discovery.
This study demonstrates that a purpose-trained fungal ITS embedding does not outperform a correctly configured alignment-based approach in open-world evaluation, revealing that methodological flaws such as heuristic defaults, missing coverage filters, and data leakage—not the representation itself—were responsible for previously reported advantages of learned embeddings.
This study proposes an attention-based graph neural network framework that leverages multi-environment crop trial data to predict yield while simultaneously generating interpretable, environment-specific marker rankings to uncover context-dependent genomic signals and weather-driven genetic interactions.
The paper introduces DELPHI, an open-source platform that enables laboratories to train, evaluate, and interpret sequence-based antibody developability predictors using rigorous cross-validation, achieving high performance on both proprietary and public datasets without requiring access to proprietary training data.
The paper introduces FedEdgeR, a secure multi-party computation-based federated learning framework that enables privacy-preserving differential gene expression analysis using edgeR, demonstrating performance equivalent to centralized analysis and superior to traditional meta-analysis methods across diverse RNA-seq datasets.
This paper introduces "phylogenetic parallelograms," an open-source interactive tool that visualizes and compares any number of discordant phylogenetic trees within a common network scaffold to better reveal their topological similarities and conflicts than traditional tanglegrams.