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 jazzPanda, an R Bioconductor package that employs a hybrid binning approach to identify spatially specific marker genes in imaging-based transcriptomics data by integrating spatial coordinates with linear models, thereby outperforming existing methods in specificity and spatial correlation.
The authors introduce Pioneer and Altimeter, open-source tools that enable fast, high-confidence DIA proteomics analysis by explicitly modeling narrow isolation window effects and deisotoped fragment intensities, achieving 2–6x speed improvements while maintaining rigorous false-discovery rate control.
This paper introduces edgePython, a Python port of the widely used edgeR package that extends its capabilities with a negative binomial-gamma mixed model and empirical Bayes shrinkage to facilitate differential expression analysis of single-cell genomics data within the Python ecosystem.
The paper introduces NanoHIVSeq, a UMI-free and reference-free bioinformatics pipeline that leverages Oxford Nanopore duplex sequencing to accurately recover full-length HIV-1 Env variants from bulk PCR amplicons with >99.9% accuracy, offering a high-throughput, reproducible, and simplified alternative to traditional sequencing methods.
This study benchmarks various large language models for predicting antisense oligonucleotide efficacy, demonstrating that few-shot prompting with DNA sequences and target gene information outperforms molecular embedding-based fine-tuning, with GPT-3.5-Turbo achieving the highest predictive accuracy across multiple datasets.
ModCRElib is a standalone software package that leverages structural information to model cis-regulatory elements, enabling users to predict transcription factor binding motifs and sites, generate affinity profiles, and simulate higher-order regulatory complexes through a flexible and customizable analysis pipeline.
The paper introduces RIMA, a rigorous computational method for quantitatively matching single-cell transcriptomic atlases across species, which successfully reveals conserved developmental principles and temporal shifts during gastrulation in mouse, rabbit, and macaque while enabling cross-species gene expression prediction and broader applications to diverse biological datasets.
This paper introduces a model-guided virtual screening (MGVS) pipeline that overcomes the synthesizability limitations of generative structure-based drug design models by efficiently identifying synthesizable analogs in ultra-large databases, thereby achieving a 25-fold improvement in screening efficiency compared to standard virtual ligand screening.
The paper introduces UnivAIRRse, a unified hierarchical framework that organizes adaptive immune receptor repertoire simulators across five operational levels to enable systematic comparison, identify current limitations, and guide the development of future digital-twin-ready immune simulation tools.
This paper proposes a novel graph-based framework that integrates structured statistical distributions into omics data representations to achieve competitive predictive performance and enhanced biological interpretability for identifying regulatory modules across multiple cancer types.