857 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.
SpatialCompassV (SCOMV) is a novel computational tool that enables de novo classification of cell and gene spatial patterns and the identification of spatially differential genes by quantifying vectorial relationships between transcript locations and regions of interest without relying on prior biological annotations.
This study demonstrates that recent improvements in Oxford Nanopore Technologies (ONT) sequencing accuracy now enable the direct generation of error-free amplicon sequence variants (ASVs) from raw reads across a wide range of amplicon lengths, allowing for high-resolution microbial community profiling without reliance on reference databases, although achieving full resolution for very long amplicons in complex samples currently requires significantly higher sequencing depth compared to PacBio.
This study employs in silico systems biology approaches to identify shared molecular biomarkers, regulatory networks, and therapeutic targets involving autoimmune inflammation and infectious disease pathways across Post-Traumatic Stress Disorder, Schizophrenia, and Bipolar Disorder.
This paper presents a comprehensive benchmarking framework that evaluates computational tools for locus-specific transposable element analysis in single-cell RNA-seq data, revealing that while older insertions can be accurately quantified, young TEs remain difficult to resolve due to mapping ambiguities and recommending unique-mapper strategies or subfamily aggregation as best practices.
Using agent-based simulations, this study on Caribbean *Acropora* restoration concludes that while hybridization poses a low risk of genetic swamping, it offers limited potential for beneficial adaptive introgression, suggesting that hybrid-mediated genetic exchange is unlikely to significantly aid coral recovery within relevant management timescales.
The paper introduces DeepAllele, a novel deep learning framework that leverages paired allele-specific input from unambiguously phased datasets to predict subtle regulatory changes and uncover functionally relevant cis-regulatory motifs with greater accuracy than existing statistical methods.
EGGS is a C-based software tool designed to generalize empirical genotype data with missing sites across replicates while offering diverse functionalities such as format conversion, phase removal, and the simulation of sequencing errors and deamination.
CycleGRN is a novel framework that infers gene regulatory networks from single-cell RNA-seq data by modeling cyclic biological processes as stochastic differential equations and utilizing flow-aligned directed graphs to accurately recover oscillatory and directional gene interactions without requiring temporal binning or splicing dynamics.
MOSAIC is a spectral framework that integrates population-scale single-cell multi-omics data into a high-resolution feature-by-sample joint embedding to enable systems-level phenotypic characterization through differential connectivity analysis, unsupervised subgroup detection, and improved clinical outcome prediction.
The paper introduces MAP, a knowledge-driven framework that integrates a large-scale biological knowledge graph and contrastive learning to generate mechanism-aware embeddings, enabling accurate zero-shot prediction of single-cell responses to unprofiled drugs and improving generalization over existing baselines.