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.
The paper introduces emb2dis, a novel deep learning tool that combines protein language models with residual networks and dilated convolutions to accurately predict protein disorder, achieving top rankings in the CAID3 benchmark and offering a freely accessible web interface.
The authors present VicMAG, an open-source visualization tool designed to comprehensively display circular metagenome-assembled genomes (cMAGs) with annotations for virulence factors, antimicrobial resistance genes, and mobile genetic elements, thereby facilitating holistic surveillance of bacterial pathogen spread in clinical and environmental settings.
Baktfold is a new, ultra-sensitive, and taxon-independent command-line tool that leverages protein structure information via Foldseek and ProstT5 to significantly improve the functional annotation of hypothetical proteins across the microbial tree of life compared to existing methods like Bakta and Prokka.
The paper introduces PSALM, a novel protein domain annotation method that integrates a pretrained protein language model (ESM-2) with a structured probabilistic decoder to achieve domain detection sensitivity and specificity comparable to traditional HMMER tools while offering improved coverage at relaxed confidence thresholds.
EnzyGen2 is a 730-million-parameter protein foundation model that enables the rapid, simultaneous co-design of sequence and structure for functional de novo enzymes by leveraging multi-task learning on protein-ligand pairs, achieving superior performance and experimental validation across multiple enzyme families compared to state-of-the-art methods.
The paper introduces Flipper, a robust framework that adapts the DESeq2 statistical model to overcome limitations in current tools by integrating input controls and hierarchical normalization, thereby enabling more accurate and sensitive identification of differential RNA binding behavior in eCLIP data.
The paper introduces WayFindR, an R package that converts pathway data from WikiPathways and KEGG into graph structures to reveal that negative feedback loops are significantly underrepresented in current biological databases, highlighting the need for improved data curation to better understand regulatory dynamics.
The paper introduces MoCoO, a modular framework combining Variational Autoencoders, Neural ODEs, and Momentum Contrast with Phase-2 Flow Matching refinement, which systematically outperforms existing methods in capturing both discrete cell-type identities and continuous developmental trajectories across 20 scRNA-seq datasets.
This study demonstrates that simple, interpretable cell-type composition metrics, particularly centered log-ratio-transformed proportions, outperform complex computational methods for unsupervised patient stratification in single-cell RNA-seq cohorts by capturing clinically relevant variation driven by cellular heterogeneity, and introduces the open-source R package scECODA to facilitate this approach.
This study introduces protein language model-based decoys for proteomics target-decoy competition and benchmarks them against classical methods, finding that while they offer superior sequence-level indistinguishability and diagnostic value, they currently do not outperform traditional reverse decoys in overall search performance.