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.
This study introduces protein language model-based decoys for proteomics target-decoy competition and benchmarks them against classical methods, finding that while they offer superior sequence-level indistinguishability and diagnostic value, they currently do not outperform traditional reverse decoys in overall search performance.
By applying a pan-repository data mining strategy with MassQL filtering to LC-MS/MS data across major public databases, this study systematically mapped the carnitine landscape to generate a comprehensive library of over 34,000 unique MS/MS spectra representing nearly 3,000 atomic compositions, thereby enabling the discovery of novel carnitine conjugates and advancing the understanding of their roles in host metabolism, diet, and disease.
The paper introduces Carafe2, a deep learning-based tool that generates high-quality, experiment-specific in silico spectral libraries directly from native timsTOF DIA raw data by fine-tuning retention time, fragment ion intensity, and ion mobility prediction models, thereby outperforming existing DDA-trained models and enabling superior peptide detection across diverse proteomic applications.
This paper introduces Parallel-REM, a scalable Python-based pipeline that utilizes batched parallelization to overcome the computational bottlenecks of traditional Random Effects Models, achieving a 26.1x speedup in inferring microbial interaction networks from large-scale metagenomic data while maintaining high statistical concordance with existing R implementations.
The paper introduces BCAR, a fast and general-purpose barcode-sequence mapper that leverages quality scores and comprehensive evidence for error correction to generate high-accuracy maps, outperforming existing homology-based aligners in both simulated and experimental datasets.
The paper proposes scTGCL, a novel Transformer-based graph contrastive learning framework that integrates multi-head self-attention with robust augmentation strategies to achieve superior accuracy, interpretability, and computational efficiency in clustering high-dimensional single-cell RNA-seq data compared to existing state-of-the-art methods.
This paper introduces a linear-time algorithm that orients bidirected pangenome graphs to efficiently identify ultrabubbles, achieving speedups of up to 25x over existing tools and enabling scalable analysis of large-scale human pangenomes.
GraphBG is a unified, scalable Bayesian framework that leverages spectral graph convolutions to accurately detect spatial domains in large-scale, multi-slice, and multi-modal spatial transcriptomics data, significantly outperforming existing methods in speed, coherence, and biological interpretability.
KuafuPrimer is a machine learning-based tool that designs optimized 16S rRNA primers using few-shot learning to significantly reduce amplification bias and improve taxonomic accuracy across diverse environments, longitudinal studies, and clinical diagnostics compared to traditional universal primers.
MetaGEAR Explorer is a freely available web platform that enables rapid, interactive, and programmatic cross-cohort analysis of over 33 million microbial gene families across 9,053 metagenomic samples to facilitate the identification of disease-associated microbial genes in inflammatory bowel disease and colorectal cancer.