856 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 presents a context-specific framework that validates stress-response gene sets by leveraging protein-protein interaction networks restricted to differentially expressed genes, demonstrating that biologically supported "Principal Response" genes form significantly interconnected subnetworks across temperature conditions.
The authors developed "plant," a de novo computational method analogous to chromatography that enables the comparison, annotation, and quantification of protein domains across non-model organisms' transcriptomes without requiring a reference genome, as demonstrated through an analysis of *Selaginella* species using 1KP RNA-seq data.
This paper demonstrates that an improved generic data schema successfully ensures high-fidelity linkage and robust sample traceability across complex, multi-team, multi-stage malaria vector studies in Tanzania, achieving near-perfect data integration from field collection through insectary rearing and laboratory analysis.
This study reveals that both archaellum-based motility and F1-type chemosensory systems were likely transferred from Archaea to Chloroflexota bacteria via horizontal gene transfer, establishing a shared evolutionary trajectory between these distinct domains.
CardioSafe is a multi-task neural network that integrates chemical and transcriptomic features to predict cardiac ion channel activity, demonstrating superior performance over existing methods once a reverse-leak audit revealed and removed training-data contamination that had previously inflated benchmark results for Nav1.5 and Cav1.2 channels.
This study utilizes unbiased molecular dynamics simulations to quantify the insertion energetics of 28 amino acid analogs in a POPC bilayer, generating depth-dependent potentials of mean force that successfully reproduce experimental hydrophobicity scales and elucidate the thermodynamic roles of protonation states and aromatic orientation.
CausalKnowledgeTrace is a scalable, Python-based computational framework that automates the construction of evidence-based causal graphs from biomedical literature to systematically identify confounders and bias structures for improved causal inference in observational studies.
The paper introduces MucOneUp, a specialized simulation framework designed to generate benchmark datasets for evaluating the performance of variant calling tools in detecting MUC1-VNTR frameshift mutations across multiple sequencing platforms.
This study demonstrates that the lack of standardized methodology in antibiotic resistance gene detection leads to massive discrepancies among pipelines, causing the same metagenomic data to yield conflicting biological interpretations and underscoring the need for researchers to carefully justify and communicate their chosen analytical approaches.
This study demonstrates that open-weight large language models, particularly Qwen2.5-32B-Instruct, can effectively predict synthetic lethal interactions by leveraging pre-trained biological knowledge to outperform random chance and non-LLM methods, offering a scalable and interpretable alternative for prioritizing novel therapeutic targets in cancer.