1200 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 reveals that TDP-43 regulates gene transcription and facilitates long-range chromatin looping by binding to and stabilizing DNA G-quadruplex structures at chromatin loop anchors, thereby providing a mechanistic explanation for gene dysregulation in diseases associated with TDP-43 dysfunction.
This study introduces a cross-domain deep learning framework that utilizes recurrence-based latent analysis and pathological fragmentation metrics to characterize and compare the spatial organization and remodeling dynamics of tissue repair versus tumor microenvironments across heterogeneous human datasets.
This paper introduces a modular Bayesian framework, exemplified by the Plasmotrack software for *Plasmodium falciparum*, that reconstructs directed transmission networks from polyclonal infections by accommodating multiple genetic sources and unobserved parents to estimate key public health metrics.
This paper presents computational protocols and practical examples for automating viral non-coding RNA annotation and programmatically retrieving Rfam data via its RESTful API, while leveraging R2DT to generate comprehensive 2D structure visualizations for integration into bioinformatics and machine learning workflows.
The paper introduces PXN, a probabilistic machine learning framework that overcomes cross-platform incompatibility in public gene expression data by seamlessly translating diverse datasets (including bridging microarray and RNA-seq technologies) into a unified representation, thereby significantly enhancing the accuracy and statistical power of large-scale integrative biological analyses.
The authors developed TriCyP, a protein language model-based tool that accurately predicts cysteine functional states (disulfide bonding, metal coordination, and free thiols) from predicted structures, enabling a proteome-scale catalog of 2.7 million cysteines across ECOD domains that reveals distinct biological patterns and identifies novel metal-binding families and potential protein-protein interactions.
The paper introduces Aiki-Sol, a protein solubility predictor that overcomes the performance plateau of existing models by explicitly accounting for centrifugation regimes as a critical feature rather than noise, achieving significant accuracy gains on a newly released, stringency-annotated E. coli dataset.
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.