1197 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 demonstrates that cross-modal pretraining of a single-cell foundation model with proteomics data yields superior gene-level and cell-level representations and better generalization than simply scaling up RNA-only models, highlighting the value of multimodal training over parameter scaling alone.
This paper introduces "sigRecon," a comprehensive benchmarking framework and open-source R package that systematically evaluates methods for predicting perturbation signatures across diverse biological contexts, revealing that projection and network-based approaches often match or outperform complex deep learning models while highlighting key factors influencing cross-context generalization.
Cell-Hub is a free, open-source, Docker-based graphical interface built on R/Shiny that democratizes end-to-end single-cell RNA sequencing analysis by integrating advanced tools like Seurat 5, CellChat 2, and Monocle 3 into a unified, no-code platform for researchers of all computational backgrounds.
This study demonstrates that using IUPAC consensus references with the ambiguity-aware aligner novoAlign significantly improves short-read variant detection in clinically challenging genomic regions compared to standard linear references, while introducing the open-source BurdenBench framework to better evaluate caller-specific precision-recall trade-offs and net clinical benefit.
The paper introduces "disize," a novel RNA-seq normalization method that leverages experimental design information through a modified generalized linear mixed model to more accurately estimate size factors and improve downstream differential expression analysis, particularly in challenging scenarios with widespread expression changes.
RapidMACS is an open-source, byte-identical alternative to MACS3 for narrow peak calling that achieves 3.5 to 51-fold speed improvements through algorithmic optimizations like parallel processing and in-memory handling, enabling seamless integration into high-throughput pipelines without requiring a Python runtime.
This study benchmarks four large language models on generating phenotype-genotype associations, finding that while they can produce a substantial volume of candidates with moderate to strong external validation support, their accuracy varies significantly by association type and model, underscoring the critical need for rigorous validation pipelines.
This paper introduces the AutoMated Alignment and Projection (AMAP) pipeline to scale the Human Reference Atlas ecosystem by enabling cross-atlas data projection and automated bulk registration of tissue specimens, thereby overcoming manual resource limitations and facilitating the construction of detailed, multi-organ reference maps.
The paper introduces MeSyTo, an open-source, ontology-driven framework that harmonizes metadata for toxicological omics studies by semantically aligning diverse reporting standards to enable consistent annotation, automated validation, and interoperable data exchange across transcriptomics, proteomics, and metabolomics.
This study integrates host transcriptomic and vector small RNA datasets to reveal a tightly synchronized tripartite molecular crosstalk between tomato antiviral immunity, Begomovirus siRNA accumulation, and *Bemisia tabaci* small RNA remodeling, identifying specific cross-kingdom regulatory modules as promising targets for dual-action RNA interference strategies to control virus transmission.