Discover, rate, and discuss biology preprints.

DupyliCate is a Python tool for analyzing gene duplications across species, demonstrating its use on plant and microbial datasets.

BINSEQ introduces binary formats for sequencing data to enable faster parallel processing, reducing analysis time for genome and transcriptome studies.

PFES quantifies the protein context of missense variants, enabling mechanistically transparent interpretation and reclassification of variants of uncertain significance.

C4TUNE predicts photosynthesis kinetic parameters from response curves, enabling efficient simulation and identification of limiting factors in maize genotypes.

This study identifies accelerated aging patterns in 3D genome organization and transcriptome in schizophrenia using Hi-C data and transcriptomic analysis.

CARIBOU is a multi-agent AI system that autonomously performs bioinformatics analyses using HPC environments, adapting and refining workflows across datasets.

Deep-Palm is a deep learning framework that accurately predicts S-palmitoylation sites by integrating sequence, physicochemical, embedding, and structural features, outperforming existing methods.

The paper introduces a computational framework to identify antimicrobial targets by analyzing protein dynamics, showing that few protein-level mechanisms account for most antimicrobial effects.

PhyloStage infers multi-stage pathway-level cancer progression models from tumor phylogenies, aligning with known AML progression and stratifying NSCLC patients by tumor size.

UcTCRp profiles MAIT- and iNKT-associated immune states using TCRβ sequences without requiring single-cell data or antigen-specific markers.

The paper develops an AI-driven platform for rational PROTAC design, enabling the optimization of a KAT6A degrader with potent in vivo activity.

This paper maps GWAS risk variants to relevant cellular contexts using deep learning and biological ontologies to interpret genetic associations.

TIMS-Bench evaluates three TIMS feature-finding tools using public and benchmark datasets, identifying strengths and weaknesses in feature detection and annotation.

Spatial-DC improves spatial proteomics deconvolution by using deep learning to infer cell-type composition and reconstruct proteomic profiles from low-resolution data.

STID provides a standardized framework for analyzing spatial transcriptomic data in infectious diseases, enabling pathogen background correction, niche identification, and multi-sample comparative analyses.

The paper introduces efficient set operations for SBWT-encoded k-mer spectra, enabling memory-efficient processing of large genomic datasets.

RAP-DB curates gene annotations and integrates omics data for rice, supporting functional genomics and molecular breeding through expert curation and community resources.

This study identifies shared and complication-specific dysregulation of ten metabolic transporters across five diabetic complications using a multi-phase bioinformatics analysis.

This paper introduces SBB principles to evaluate virtual cell perturbation models, showing that complex methods often fail to outperform simple baselines.

This paper introduces MLMGCN-CVD, an annotation-free framework for predicting atherosclerotic cardiovascular disease from gut metagenomic sequences, achieving high accuracy and uncovering novel microbiome functions.