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 study utilizes a cross-dataset integrative transcriptomic analysis to identify oxidative stress and inflammation-related hub genes in Parkinson's disease and reveals how specific bioactive food compounds may modulate these gene networks through nutrigenomic interventions.
This paper introduces **bcrnnoise**, an open-source Python framework that unifies deterministic drift, Gaussian fluctuations, and additive sporadic jumps into a single stochastic differential equation model to efficiently generate accurate transcription traces with up to two orders of magnitude faster computational speed than exact stochastic sampling.
SaVanache is a multi-resolution visualization tool that enables efficient, real-time exploration and intuitive one-to-many comparison of complex pangenome variation graphs by preprocessing GFA files into optimized indexes and using specialized glyphs to highlight structural variations relative to a linear pivot genome.
This paper presents a statistical destriping method for 10x Genomics VisiumHD data that models bin counts using nuclei segmentation and a regularized generalized linear model to effectively remove multiplicative striping artifacts while preserving large-scale biological signals, outperforming existing normalization approaches.
The paper introduces SLiMNet, a deep learning model leveraging protein large language model embeddings and contrastive learning to predict functional similarities between short linear motifs (SLiMs), thereby enabling the functional annotation of previously uncharacterized motifs and providing comprehensive atlases of potential functional pairs for the research community.
ProtSpace is a privacy-preserving, browser-based web application that enables the interactive visualization and systematic exploration of protein language model embedding spaces, revealing complex functional and structural relationships beyond traditional sequence similarity through integrated 3D structure viewing and multi-label annotations.
The paper introduces MANGO, a novel network clustering method that corrects for hub bias in gene set analysis by conditioning on degree distribution, thereby enabling robust detection of biologically meaningful spatial autocorrelation without the false positives inherent in traditional over-representation or naive network-based approaches.
The paper introduces geneSync, an R package that resolves gene symbol inconsistencies in large-scale RNA-seq data through a hierarchical matching strategy and offline databases, significantly improving cross-dataset integration and feature overlap.
This paper demonstrates that conventional baseline methods perform as well as, or better than, protein language models in predicting the effects of mutation-dense protein variants, suggesting that pLMs require integration with biophysical or structural priors to effectively advance protein design.
PhenotypeToGeneDownloaderR is a lightweight, reproducible R/Python pipeline that automates the retrieval, harmonization, and validation of phenotype-associated genes from multiple heterogeneous biological databases, achieving high recall and demonstrating the complementarity of integrated evidence sources for downstream genetic analysis.