1210 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 manually curated database and a computational pipeline that enable the precise, scalable annotation of halogenating enzymes across genomic and metagenomic data, thereby facilitating the discovery of nature's untapped reservoir of microbial halogenated natural products.
This paper presents Ultra-Content Screening (UCS), a novel high-dimensional single-cell proteomics method that uses cyclic immunostaining to analyze up to 40 markers in 100,000 blood cells, demonstrating its potential to reveal disease-specific immune profiles in acute myeloid leukemia for improved diagnostics and personalized medicine.
The paper introduces **cyto**, an open-source, ultra high-throughput processor for 10x Genomics Flex single-cell sequencing that achieves a 16.5-fold speedup and significantly reduced resource usage compared to CellRanger by utilizing direct k-mer lookup and novel binary formats, while maintaining 99.85% concordance with standard outputs to enable scalable, cost-effective billion-cell atlas construction.
The paper introduces the `distortions` software package, an interactive visualization tool designed to measure and display local metric distortions in nonlinear dimensionality reductions like UMAP and t-SNE, thereby helping researchers identify artifacts, tune hyperparameters, and select appropriate methods for analyzing high-dimensional genomics data.
The paper introduces dna-parser, a high-performance Python library written in Rust that efficiently encodes DNA and RNA sequences into numerical features for machine learning by leveraging parallel processing and offering broad compatibility with the Python ecosystem.
RAGulate is a retrieval-augmented generation framework that combines hybrid literature retrieval with large language models to accurately assess and explain transcription factor-target interactions in specific biological contexts, thereby reducing hallucinations and accelerating experimental prioritization for biologists.
By developing the variational Bayesian hierarchical model eTRex and applying it to 4,819 ATAC-seq datasets, this study constructs a comprehensive, context-specific pan-cancer atlas of functional transcriptional regulator profiles that reveals both common and cancer-specific oncogenic regulatory programs, validated through independent genomic screens and accessible via an interactive web portal.
HERMES is a fast, structure-based neural network model that predicts mutational effects on protein stability and binding affinity by leveraging local 3D atomic environments and an amortized fine-tuning strategy to overcome size-based biases, thereby enabling accurate, computationally efficient vaccine design.
This study integrates whole-genome analysis with 3D protein structure modeling to demonstrate how genetic variations in virulence factors, particularly the intimin-receptor complex, can be functionally characterized to improve risk assessment and diagnostics for diverse non-O157 Shiga toxin-producing *E. coli*.
The paper introduces VAIruScope, a deep learning-based pipeline that enables the automated, label-free identification and quantification of virus-infected cells across diverse cell models and clinically relevant RNA, DNA, and retroviruses by recognizing cytopathic effects in light microscopy images with up to 96% accuracy.