1199 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 introduces a novel, scalable, and globally accessible core genome MLST and LIN coding scheme for *Streptococcus pyogenes*, hosted by PubMLST, to enhance the detection of outbreaks and facilitate international collaboration in tracking genetic variants.
The paper introduces GBZ-base and GAF-base, SQLite-backed indexed file formats that enable efficient, disk-based extraction of local pangenome subgraphs and their corresponding alignments, addressing the limitations of existing batch-processing formats for interactive applications.
Panomap is the first signal-space mapper that utilizes pangenome variation graphs to eliminate reference bias and improve mapping accuracy for diverse nanopore samples while maintaining a compact, scalable index.
This paper introduces a continuous-time protein evolution model parameterized by a generative Potts model to simulate epistatic effects, revealing that while epistasis slows overall evolution and causes systematic underestimation of evolutionary distances, it does not alter average site-specific rates due to context-dependent rate heterogeneity.
This study demonstrates that refining plasma spectral libraries by excluding non-representative peptides significantly enhances the precision, accuracy, and computational efficiency of data-independent acquisition (DIA) proteomics relative quantification without compromising protein identification.
MKMC is a scalable, reference-free toolkit that leverages k-mer statistics to enable robust RNA-seq analysis across model and non-model organisms, successfully detecting biological signals and isoform-specific events that often elude traditional alignment-based methods.
The authors present CircDiscoverer, a comprehensive, user-friendly web resource that integrates manually curated and computationally predicted data to explore circRNA-protein interactions and RNA modification landscapes across multiple species, including humans, mice, fruit flies, and plants.
This paper introduces an interactive framework for benchmarking scientific coding agents using spatial transcriptomics alignment tasks, revealing that while richer environmental context increases tool exploration, it can degrade performance by inducing fragile workflows and unnecessary transformations, thereby highlighting the need to evaluate agent traces alongside final outputs.
LeafRank is a novel phylodynamic framework that leverages single-cell DNA-seq phylogenies and a multi-type branching process model to infer the relative fitness of individual cells in chromosomally unstable tumors, successfully quantifying growth heterogeneity and revealing that fitness in whole-genome duplication lineages is acquired through subsequent alterations rather than immediate advantages.
Rectangle is a robust and scalable Python framework that leverages multiscale deconvolution and explicit modeling of unknown content to accurately resolve cell phenotypes in bulk RNA-seq data using single-cell references, thereby enabling high-resolution cellular profiling at population scales.