850 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 the R-package `sbivar`, which provides a suite of modified statistical tests and variance estimators to rigorously assess bivariate spatial associations between multi-omics modalities with disjoint coordinates while properly accounting for spatial autocorrelation and high-dimensional computational challenges.
This study reveals that current missense pathogenicity predictors, trained primarily on loss-of-function variants, systematically under-score activating cancer drivers due to their distinct structural and evolutionary features, prompting the authors to provide an openly licensed benchmark, per-gene calibration maps, and a recalibrated framework (OncoCal) to improve somatic variant interpretation.
Hobrac is an automated, freely available workflow that streamlines whole-genome comparison and synteny visualization by integrating automated reference genome selection with gene-based structural analysis to overcome challenges in assembly validation and evolutionary studies.
The paper introduces PepCL, a replay-based continual learning framework paired with a new state-of-the-art model called MHCPrime, which enables peptide-MHC predictors to integrate new experimental assay data while preserving prior mass spectrometry knowledge to overcome catastrophic forgetting and improve predictive performance across diverse biological contexts.
This paper introduces a fully de novo method based on "extended t-cores" within compacted De Bruijn graphs to effectively identify and distinguish transposable elements and other inexact repeats directly from short-read RNA-seq data without requiring a reference genome.
This paper benchmarks 62 text summarization methods on 1,000 biomedical abstracts, revealing that general-purpose, medium-sized language models outperform both statistical extractive methods and specialized or frontier-scale models in generating accurate and semantically coherent scientific summaries.
The paper introduces FUSED, a novel multivariate functional representation that jointly models the structural and elemental compositional information of protein ligand binding sites as continuous functions of distance, demonstrating that this approach yields compact, interpretable, and competitive predictive performance for ligand discrimination and binding-site classification compared to existing methods.
This study employs an integrative computational toxicology framework to demonstrate that PFOS and PFHxS exposure drives inflammatory keratinocyte programs that converge with psoriasis pathology, identifying specific molecular effectors and spatial niches through multi-omics integration and virtual perturbation.
This study presents the computational design and validation of a novel multi-epitope vaccine candidate targeting three previously unexplored *Mycobacterium tuberculosis* virulence proteins (EccB3, MycP, and polyketide synthase), demonstrating strong structural stability, effective TLR binding, and robust immune responses through a comprehensive reverse vaccinology pipeline.
This study demonstrates that the choice of taxonomic profiler, reference database, and parameterization significantly influences alpha diversity estimates and statistical conclusions in metagenomic analyses, underscoring the critical need for researchers to conduct sensitivity analyses to ensure robust and reliable scientific findings.