1012 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 LYM-1, the first large-scale, multi-crop model trained on over 4.7 million observations to predict and optimize crop yields across the Canadian prairies by analyzing complex interactions between genetics, environment, and management.
This study evaluates the performance of six open-source somatic variant callers on high-coverage tumour-only amplicon sequencing data using a commercial reference standard, identifying FreeBayes, VarScan, MuTect2, and Pisces as the top-performing tools despite FreeBayes' tendency to generate more artifacts.
The paper introduces IDiom, an autoregressive protein language model trained on millions of intrinsically disordered region sequences that enables the generative design of diverse, biologically relevant disordered proteins and regions conditioned on structural context or subcellular localization.
The paper introduces SimpleFold-Turbo, an open-source method that adapts TeaCache to achieve up to 14-fold inference acceleration in flow-matching protein structure prediction by skipping approximately 93% of redundant neural network evaluations without retraining or significant quality loss.
This study demonstrates that while irreducible batch effects and outlier removal can introduce errors, preprocessing steps like regularization to comprehensive databases do not significantly alter unsupervised classification patterns, suggesting that non-repeatability in high-content assays is an uncorrectable feature that does not necessarily compromise classification outcomes.
This study presents a robust, multi-criteria computational framework integrating meta-ensemble QSAR, structure-based docking, and molecular dynamics to identify and validate novel BACE1 inhibitors, ultimately prioritizing Mol-2 as a promising lead for Alzheimer's disease therapy with confirmed stability and favorable CNS drug-like properties.
This paper introduces TCMCard, a high-confidence digital infrastructure that utilizes a Multi-Dimensional Evidence Integration framework to filter low-quality data and provide a reliable, interactive platform for analyzing the synergistic mechanisms of Traditional Chinese Medicine.
This study addresses the lack of curated trnL reference databases by systematically comparing OBITools3/ecoPCR, RESCRIPt, and MetaCurator to generate and evaluate high-quality plant DNA metabarcoding resources, demonstrating that the optimal curation tool varies depending on the specific trnL region analyzed.
The paper introduces Immunotype, a deep learning-based ensemble predictor that accurately determines HLA class I allotypes directly from complex mass spectrometry-based immunopeptidomics data, thereby enabling rapid and cost-effective HLA typing for large-scale immunotherapy research.
This study systematically benchmarks six ambient RNA removal tools across diverse single-cell platforms, revealing that while CellBender and SoupX offer reliable denoising, tools like scAR and CellClear critically fail by generating artificial counts and spurious cell types, thereby establishing count matrix integrity as a paramount criterion for tool selection.