856 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 **rnaends**, an R package designed to facilitate the analysis of exact RNA ends at nucleotide resolution by providing a comprehensive workflow for processing, quantifying, and interpreting RNA-end sequencing data to study diverse aspects of RNA metabolism such as transcription start sites, degradation dynamics, and post-transcriptional modifications.
This paper introduces Coherent Denoising, a novel ensemble-based diffusion framework that synthesizes missing biomedical modalities from available data to overcome dataset sparsity, thereby enabling high-fidelity multimodal integration, robust predictive modeling, and counterfactual analysis for precision oncology using a large-scale TCGA cohort.
PRIZM is a data-efficient, two-phase workflow that leverages small experimental datasets to select optimal zero-shot foundation models for ranking and prioritizing protein variants, successfully identifying improved thermostability and activity in enzyme engineering case studies.
The paper introduces FM-GPT, a novel Bayesian fine-mapping method that leverages gene-guided dimension reduction to accurately prioritize causal genes across multiple correlated phenotypes with mixed outcome types, successfully uncovering shared biological mechanisms and pleiotropic effects in large-scale phenome-wide studies using UK Biobank data.
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.