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.
The paper introduces resolveS, a fast and lightweight tool that accurately infers RNA-seq library strandedness using universal rRNA alignments, thereby eliminating the need for organism-specific reference genomes and facilitating large-scale data reanalysis.
This paper benchmarks ligand-conditioned protein generation using sequence-only data, revealing a fundamental trade-off where models trained on sparse ligand-protein pairs produce foldable but less diverse sequences, while those trained on abundant pairs yield more diverse but less reliably foldable candidates, ultimately highlighting dataset redundancy and incompleteness as critical bottlenecks.
This paper presents a multi-agent large language model system that automates the extraction and optimization of protein purification protocols by analyzing literature and cross-referencing successful and failed methods, significantly reducing manual analysis time while highlighting the need for open access to primary scientific citations.
This study proposes a novel zoological framework that maps small self-cleaving ribozymes to primitive marine animal body plans to model their ecological interactions, evolutionary history, and modularity, revealing a predator-prey dynamic in the RNA world where hammerhead ribozymes acted as generalist apex predators.
This study challenges the conventional focus on maximizing static binding affinity for Nipah virus neutralization by analyzing 1,194 computational binders and proposing a "Kinetic Compatibility Hypothesis" that prioritizes specific architectural patterns, structural flexibility, and terminal motifs over ultra-tight binding to effectively distinguish functional neutralizers from non-neutralizers.
The paper introduces MESSI, a reproducible Nextflow-based framework that standardizes the benchmarking of multimodal integration methods across diverse biomedical datasets, revealing that while no single method is universally optimal, choices should balance predictive accuracy, biological interpretability, and computational efficiency.
This study reveals that in vivo transcription factor binding sites are consistently surrounded by a broad (1000–1500 bp) region of elevated GC content and specific sequence distortions, suggesting these non-consensus flanking sequences act as statistical beacons to facilitate a coarse scanning mechanism for target recognition.
This paper presents a stochastic, iterative framework leveraging MSA-Transformer, beam search, and sparse autoencoders to generate biologically plausible hybrid proteins that successfully integrate functional and structural features from distinct source and target sequences.
The paper introduces GNExT, a scalable web-based platform that bridges genome-wide association studies and network medicine by integrating tools like MAGMA and Drugst.One to transform genetic variant data into coherent network modules for disease mechanism mining and drug repurposing.
The paper introduces scProfiterole, a computational framework that leverages Arnoldi orthonormalization to implement polynomial interpolations of spectral graph filters within a graph contrastive learning paradigm, thereby enhancing the robustness and accuracy of cell type identification in noisy single-cell proteomic data.