1205 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.
DualLoc is a novel multi-label predictor that employs full-parameter fine-tuning of a cascaded dual-transformer architecture to achieve state-of-the-art accuracy in predicting protein subcellular localization across ten compartments, effectively capturing complex multi-compartment localization patterns and biologically relevant organelle couplings.
This paper introduces mean evidence strength (MES), a novel metric based on ACMG/AMP guidelines and Bayesian calibration, to evaluate computational predictors and multiplexed assays of variant effect by quantifying their clinical evidence yield rather than relying solely on traditional discrimination metrics like AUROC.
ALPINE is a scalable, reproducible Python-based pipeline that leverages long-read amplicon sequencing to comprehensively classify and quantify diverse gene-editing outcomes, including complex viral vector integrations and structural variants, addressing key limitations of existing short-read tools for therapeutic development.
By constructing a transcriptomic atlas of 3,713 samples to identify four distinct molecular subtypes of sepsis, this study elucidates subtype-specific pathophysiological mechanisms and proposes targeted drug repurposing strategies, such as corticosteroids for C1 and methylene blue for the high-mortality C4 subtype, to advance precision medicine and explain the failure of previous broad-spectrum clinical trials.
Panmap is a scalable tool that utilizes a phylogenetically compressed k-mer index to efficiently align, genotype, and place sequencing reads onto mutation-annotated pangenomes containing millions of genomes, significantly reducing computational time and storage requirements compared to existing methods.
By leveraging high-quality long-read assemblies from the Vertebrate Genomes Project, this study establishes a scalable framework to map promoter methylomes across 82 vertebrate species, revealing a conserved transcription start site-centered hypomethylation signature alongside lineage-specific patterns that closely track phylogenetic relationships.
DeepBranchAI introduces a novel cascade workflow that overcomes the annotation bottleneck in 3D branching network segmentation by iteratively refining sparse labels through a positive feedback loop of random forests and expert input, ultimately enabling the training of a robust, topology-preserving 3D nnU-Net model that achieves high accuracy across diverse biological and medical datasets while significantly reducing manual annotation time.
This paper introduces a dynamic, run-length-compressed skiplist data structure for the graph Burrows-Wheeler transform (GBWT) that enables time and space-efficient pangenome operations on syncmer graphs, successfully building a 5.8 GB lossless representation of 92 human genomes in under an hour and supporting rapid sequence matching.
This paper introduces Openfish, an open-source GPU-accelerated decoding library, and Slorado, a fully open-source basecalling framework, to provide a hardware-independent, scalable, and accurate alternative to Oxford Nanopore Technologies' proprietary Dorado software, thereby eliminating hardware lock-in and enhancing accessibility for nanopore sequencing.
scMagnifier is a consensus clustering framework that leverages gene regulatory network-informed in silico perturbations and a novel visualization method to amplify subtle transcriptional differences, thereby enabling the high-resolution identification and spatial mapping of fine-grained cell subtypes in both single-cell and spatial transcriptomics data.