1012 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 methodology to reconstruct the experimental search space of protein-protein interaction (PPI) screens to infer likely non-interacting protein pairs, thereby enabling better error rate estimation, model calibration, and improved machine learning training for a more accurate and complete mapping of the human interactome.
The paper introduces Inverse FoldDir, a controllable inverse-folding method based on Dirichlet flow matching that generates diverse, experimentally validated protein sequences with user-defined constraints by performing iterative denoising on the amino acid probability simplex, achieving state-of-the-art structural recovery metrics and successful functional redesign of an anti-GFP nanobody.
This study introduces the perturbation-aware Neural ODE (pNODE) framework, which outperforms traditional models by accurately forecasting gut microbiome dynamics and predicting gut-borne bloodstream infections in cancer patients undergoing allogeneic hematopoietic cell transplantation through the integration of microbial abundances and time-resolved antibiotic perturbations.
This study systematically benchmarks small variant calling and haplotype phasing pipelines for long-read RNA sequencing across diverse datasets and technologies, revealing that sequencing quality is the primary performance determinant while identifying Clair3-RNA, DeepVariant, and longcallR as top callers and WhatsHap or HapCUT2 as optimal phasing tools depending on the specific context.
This study demonstrates that while deep-learning classifiers for fungal ITS sequences achieve high accuracy on full-length references, they fail dramatically on the commonly used ITS2 subregion by relying on flanking regions rather than the barcode itself, causing classical baseline methods to significantly outperform them in realistic environmental metabarcoding scenarios.
The paper introduces Cavebear, a machine learning framework that leverages reference scRNA-seq time-series data from one species or condition to accurately infer pseudotime and map biological aging or disease progression in query contexts lacking reliable temporal labels.
This research demonstrates that the Hamiltonian Auto Decomposition Optimisation Framework (HADOF) can overcome current NISQ hardware limitations to successfully assemble a 7.1 million base pair *Pseudomonas aeruginosa* genome on real quantum hardware, achieving a 99.348% genome fraction and proving the viability of scalable quantum optimisation for large-scale scientific workloads.
This paper introduces PLI-Parallax, a comprehensive dataset that integrates predicted and experimental protein-ligand structures with calibrated accuracy labels and rigorous evaluation splits, enabling the estimation of prediction reliability through method agreement even in the absence of experimental ground truth.
scAtlasPy is a novel, disk-resident computing framework that overcomes the memory limitations of standard workstations, enabling full-resolution analysis of massive single-cell atlases (e.g., 100 million cells) with significantly lower memory usage and higher processing speeds compared to existing state-of-the-art platforms.
This study identifies a reproducible, higher-order "functional organisation" in vascular disease that transcends individual gene or pathway variations, revealing that cardiovascular remodelling across diverse conditions and cell types is driven by a coordinated, dynamic rebalancing of six conserved biological functions.