1180 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 systematically benchmarks ten unsupervised circadian phase inference methods across 25 datasets, identifying CHIRAL as the top performer with the lowest mean error, while demonstrating that method accuracy varies by dataset characteristics and that top methods effectively recover rhythmic gene patterns even when sample collection times are unknown.
The paper introduces Dipler, a rapid, reference-independent two-step method that combines group-specific k-mer identification with targeted PacBio de novo assembly to successfully resolve and assemble structurally complex, divergent alleles in eukaryotic genomes at low sequencing coverage.
The study introduces DeepMM, a deep learning tool that detects and corrects interspecies chimeric contigs in metagenome-assembled genomes prior to binning, thereby significantly reducing reference-discordant gene and virulence-factor annotations that are often missed by standard marker-based quality controls.
The paper introduces vampW, a novel fully parametric Bayesian method based on Vector Approximate Message Passing and a Weibull model, which outperforms traditional marginal testing and other joint models by significantly reducing false discoveries and improving disease onset prediction through the effective joint modeling of correlated multi-omics biomarkers and time-to-event data.
This study revisits a 2003 nearest-neighbor toxicity prediction method using updated public data to validate its accuracy for rat oral LD50 and 28 other endpoints, while demonstrating that a reverse-screening approach measuring a compound's structural reach across protein families significantly improves toxicity prediction by capturing promiscuity beyond standard physicochemical properties.
DNA-LLM2Vec introduces a staged framework that converts directional decoder-based DNA foundation models into bidirectional encoders, demonstrating that the optimal adaptation strategy for downstream genomic tasks depends on both the specific task structure and the underlying model architecture.
The paper introduces E3 Ligase Ligandalyzer, a freely accessible, structure-first web platform that enables the comparative analysis of experimentally resolved E3 ligase-recruiter complexes through integrated cheminformatics and structural workflows to facilitate downstream degrader design.
The authors present NCI-Profiler, a free automated tool that detects and visualizes nine types of protein-ligand non-covalent interactions directly from structural coordinates with higher accuracy and speed than existing tools like PLIP, while offering enhanced coverage for complex cases such as metal coordination and macrocyclic ligands.
This study reveals that while AlphaFold3 reliably predicts intrinsically disordered monomers by learning from training data, its multimer predictions remain unpredictable due to rigid structural priors that override input information, highlighting the critical need for disorder-enriched datasets and ensemble sampling over mere architectural innovation.
LOCALE introduces a noise-robust DNA search framework that recasts sequence retrieval as dense retrieval using an InfoNCE-trained encoder to generate variation-tolerant embeddings, significantly outperforming state-of-the-art k-mer indexes and existing embedding methods in accuracy and scalability across petabase-scale repositories.