1210 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 scTREND, an annotation-free computational framework that integrates single-cell transcriptomics with clinical outcomes to model time-varying, condition-dependent cell-level hazards, thereby enabling dynamic risk assessment across diverse diseases and spatial or bulk data modalities without requiring predefined cell-type labels.
The authors present TLand, a novel machine-learning architecture built upon the RegulomeDB database that outperforms existing models in predicting and prioritizing cell- and organ-specific non-coding regulatory variants, thereby improving the interpretation of GWAS-associated SNPs across the human genome.
This study demonstrates that the correlation between generative model log-likelihoods and antibody binding affinity arises because these models implicitly learn evolutionary priors, performing on par with simple BLOSUM similarity scores calculated against a known parental binder.
This paper introduces a novel PARAFAC2-based analytical framework that overcomes the limitations of standard CP models by explicitly capturing subject-specific temporal variations in longitudinal microbiome data, thereby enabling the discovery of replicable, individualized developmental signatures that were previously overlooked.
The paper introduces SWARM, an AI-based framework that significantly improves the precision of single-molecule RNA modification profiling by overcoming high false-positive rates in nanopore sequencing, thereby enabling the discovery of novel modification sites and revealing new insights into pseudouridine deposition while challenging existing models of epitranscriptomic coordination.
This paper introduces OptMini, an efficient algorithm that computes minimum-density minimizers for large window sizes by overcoming the limitations of brute-force search and integer linear programming, while also providing new insights into the relationship between minimizer density and universal hitting sets.
This paper introduces AniAnn's, an open-source, alignment-free algorithm that leverages fast average nucleotide identity estimates to rapidly and accurately annotate large blocks of tandem repeat arrays and satellite DNA across diverse eukaryotic genomes with significantly reduced runtime compared to existing methods.
The paper introduces the GLYCAM Bacterial Carbohydrate Builder, an enhanced web tool that integrates bacterial monosaccharide parameters and derivatives to facilitate the intuitive generation of 3D structural models and molecular dynamics input files for complex bacterial glycans.
LocAlign is a geometric deep learning framework that enables local protein structural alignment by iteratively predicting atom-level correspondences and 3D superimpositions through weakly supervised training on ligand-bound pairs, effectively identifying functional motifs across diverse protein folds without requiring ground-truth alignments.
This paper presents a novel algorithm that constructs RLBWT-based compressed full text indexes and computes LCP-related information in optimal O(n) time and O(r) space for repetitive datasets, achieving a 12.6x reduction in peak memory usage for terabase-scale pangenome analysis compared to previous methods.