857 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.
CLAMP is a scalable, biologically informed latent variable analysis tool that overcomes the speed and memory limitations of existing methods like PLIER by utilizing a two-phase algorithm and memory-mapped data handling, thereby enabling the efficient extraction of interpretable regulatory networks from large-scale transcriptomic compendia.
GraTools is a fast, user-friendly, and open-source command-line tool that streamlines the manipulation and analysis of pangenome variation graphs directly from GFA files by enabling efficient subgraph extraction, sequence retrieval, and diverse genomic analyses through a modular architecture that integrates with existing bioinformatics workflows.
By applying a machine learning ensemble to retinal gene expression data from spaceflown mice, this study reveals that oxidative lipid peroxidation and apoptotic cell death are distinct molecular pathways driving spaceflight-associated neuro-ocular syndrome, offering a framework for developing biomarkers and therapeutic targets to protect astronaut vision.
This paper demonstrates that a parameter-efficient nested birth-death process model, grounded in molecular evolutionary theory, achieves competitive performance with massive neural networks in modeling protein evolution, suggesting that incorporating CTMC-based structures into future neural phylogenetic approaches could yield more realistic and efficient models.
The paper introduces SCOTCH, a platform-independent pipeline that leverages long-read single-cell RNA sequencing to robustly characterize gene expression at the isoform level by modeling non-overlapping sub-exons, thereby improving the quantification of known isoforms and the reconstruction of novel, cell-type-specific transcripts compared to existing methods.
This paper presents an end-to-end workflow that integrates long-read RNA sequencing with mass spectrometry to construct haplotype-resolved, sample-specific proteome databases, enabling the detection of allele-specific protein isoforms and linked variants that are missed by traditional reference-based approaches.
The paper introduces nVenn2, an improved algorithm for generating interpretable quasi-proportional Venn diagrams with large numbers of sets by optimizing computation time based on the number of non-empty regions rather than the total number of sets.
This paper presents a community benchmark of 11 generative models for synthetic bulk RNA-seq data, revealing that while deep learning models offer high utility, they often face greater privacy risks compared to differentially private or simpler statistical approaches, highlighting the need to balance utility, biological fidelity, and privacy based on specific use cases.
This paper introduces Kg4j, a computational framework that generates tailored, small-scale knowledge graphs from a large-scale biomedical database to integrate experimental data and generate hypotheses, demonstrating its efficacy in uncovering novel links between persistent organic pollutants and endometriosis while validating the approach through literature comparison and precision optimization.
This study applies Multi-Omics Factor Analysis (MOFA) to integrate genomic, epigenomic, and transcriptomic data from glioma patients, revealing distinct molecular subtypes, novel prognostic biomarkers, and a neural development-associated transcriptional profile to guide more personalized treatment strategies.