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.
The paper introduces a fast k-mer based algorithm that accelerates genetic code inference by over 100-fold, enabling the analysis of thousands of bacterial and archaeal genomes to identify new code variations, including the first documented sense codon reassignment in archaea.
NovoTax is an end-to-end pipeline that enables strain-level identification of prokaryotic organisms directly from raw mass spectrometry-based proteomics data by combining de novo peptide sequencing with optimized genome database searching, thereby facilitating downstream proteome analysis without prior knowledge of sample composition.
This study utilizes multistage machine learning on chick models to identify a critical circadian window (ZT8–ZT12) that drives coordinated retina-choroid gene expression programs in myopia, revealing conserved molecular mechanisms that translate to complex human regulatory networks.
The paper introduces sctrial, an open-source Python framework that addresses the inferential challenges of longitudinal single-cell experiments by performing participant-level differential analysis to prevent pseudoreplication-driven false positives and ensure rigorous, reproducible biological interpretation across diverse clinical contexts.
Halo is a pretrained, generalizable deep learning model that accurately reconstructs whole-cell boundaries in spatial transcriptomics by integrating nuclear morphology with RNA transcript distributions, outperforming traditional nuclear expansion methods across diverse tissue types without requiring dataset-specific training.
This paper introduces GenoJEPA, a genomic foundation model that leverages a joint-embedding predictive architecture to shift from computationally expensive nucleotide reconstruction to efficient semantic alignment in latent space, thereby achieving robust generalization across 55 downstream tasks with reduced parameters and enabling lightweight, GPU-free classification.
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.