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.
Looplook is an open-source R package that integrates chromatin conformation data with transcriptomic information through connected components clustering and expression-aware refinement to accurately assign distal regulatory elements to target genes and reduce false positives in functional genomics.
XAttn-DTA is a sequence-driven framework that leverages Graph Attention Networks for drug encoding and ESM2-derived residue graphs for protein representation, fused via bidirectional cross-attention, to achieve state-of-the-art drug-target affinity prediction and superior generalization in cold-start scenarios without relying on experimental structural data.
The authors developed the BayesMonSTR algorithm to detect mosaic microsatellite mutations at single-cell resolution, revealing that aging human neurons accumulate a higher burden of longer STR deletions enriched at regulatory regions compared to other tissues.
This paper demonstrates that a widespread practice of applying supervised feature screening before cross-validation causes severe data leakage in cancer drug response prediction, systematically inflating reported accuracy and corrupting biomarker discovery by introducing statistical artifacts that mimic biological signals.
The authors present VDJcraft, a novel pipeline specifically designed for accurate V(D)J recombination analysis using long-read transcriptomic data, which outperforms existing methods in gene detection and recombination accuracy while enabling the discovery of novel gene subclasses and disease-associated immune signatures.
This paper introduces three new repeat-robust k-mer-based estimators for mutation rates that overcome the limitations of existing sketch-based methods on highly repetitive sequences, demonstrating superior performance on alpha satellite data.
This study presents an interpretable, attention-based deep learning framework that integrates mRNA, miRNA, and DNA methylation data to significantly improve prognostic accuracy for hepatocellular carcinoma patients compared to existing models, while identifying biologically relevant biomarkers and demonstrating robust performance on external validation cohorts.
The paper introduces PanTEon, a cross-kingdom deep learning framework comprising a harmonized database and a modular benchmarking platform that enables reproducible, standardized training and evaluation of transposable element classifiers across diverse eukaryotic lineages.
Correlate is a freely accessible, browser-based web application that enables data-driven exploration of gene dependencies and functional links across over 1,000 cancer cell lines by analyzing CRISPR screen data from the Cancer Dependency Map, offering a complementary alternative to knowledge-based network tools.
This paper introduces Nettle, an open-source tool that improves quantitation in data-independent acquisition proteomics by imputing peptide retention time boundaries to integrate chromatographic signals, thereby reducing missing data limitations and enhancing accuracy compared to traditional methods.