1178 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.
Pycea is an open-source Python toolkit that introduces the TreeData format to seamlessly integrate lineage tree data with single-cell measurements within the scverse ecosystem, enabling scalable analysis, visualization, and statistical inference for cellular lineage tracing.
This study employs an offline structure prediction and search pipeline to annotate the uncharacterized salivary extracellular vesicle cargo of the medicinal leech *Hirudinaria manillensis*, successfully assigning a Ly-6/uPAR three-finger fold to one candidate while identifying two structurally unresolved proteins and validating a conserved trypsin-like gene model.
This longitudinal proteomic reanalysis of tuberculosis patients reveals that while treatment drives a broad recovery of inflammatory and nutritional-metabolic protein systems in both undernourished and normal-weight individuals, baseline BMI-defined undernutrition does not significantly alter the trajectory of this recovery, though the study remains underpowered to definitively establish equivalence between the groups.
The paper introduces **smoothclust**, an R package that provides a spatial-smoothing-based clustering workflow and a novel boundary-density metric to generate interpretable spatial domain labels and quantify tissue architecture organization in spatial transcriptomics data.
CellMethMarker is a comprehensive, curated database that integrates hundreds of thousands of de novo and literature-derived DNA methylation markers to enable cell- and tissue-specific identity inference, visualization, and improved deconvolution of methylomes.
Neopep is a sequence-conditioned diffusion model that overcomes the data scarcity in non-canonical peptide structure prediction by leveraging reinforcement learning to fine-tune a generative model using machine-learned force field energy, thereby achieving higher accuracy than existing state-of-the-art methods.
GLOM-TGITO is a multiscale protein design framework that integrates global-local fluorescence prediction with multiview landscape-guided optimization to efficiently discover high-activity, reliable, and novel protein variants, outperforming existing methods in both predictive accuracy and the identification of structurally stable candidates.
The NK-RCPP workflow demonstrates that integrating receptor-matched on-target scores with structural-neighbour comparisons significantly refines the prioritization of neurokinin receptor peptide candidates, yielding a distinct shortlist that differs from selections based solely on on-target activity.
The paper proposes MPFIL, a multi-level feature integration framework that combines structural and sequential information to effectively predict mutation-induced changes in protein-protein binding affinity, demonstrating competitive performance across multiple benchmark datasets.
This paper presents Omnibenchmark, a reproducible and standardized framework that unifies the evaluation of single-cell RNA sequencing preprocessing pipelines to address inconsistencies in existing benchmarks and facilitate more flexible, extensible method comparisons.