Discover, rate, and discuss biology preprints.

STARgrid is an open-source pipeline that processes Visium HD sequencing data to generate count matrices, showing high agreement with Space Ranger while improving efficiency and resolution.

AnnZarro enables interactive exploration of cell-by-cell and gene-by-gene relationships in single-cell data through a browser-based viewer linked to AnnData objects.

The paper assesses covariance stationarity in spatial transcriptomics and identifies spatially varying genes using non-stationary covariance models.

UPLiFT improves parameter efficiency in fine-tuning protein language models by optimizing activation space instead of network weights, achieving competitive performance with fewer trainable parameters.

The paper improves a tool for sorting DNA reads in xenograft models, enhancing efficiency for epigenetic analysis.

CLARA uses a language model to make strategy decisions in compound library active learning, outperforming random sampling across multiple protein targets.

The paper evaluates whether molecular models can causally represent chemical variables by comparing intervention effects with random controls and pretraining advantages.

rhythmCI provides an R package for detecting circadian rhythms using harmonic regression confidence intervals, improving rhythmicity analysis in small clinical datasets.

The paper extends Ramachandran diagrams by modeling amino acid and pair torsion-angle distributions conditioned on secondary structure elements, enabling more accurate conformational predictions.

The paper investigates how specific stalk-derived peptides can reactivate signaling in disease-associated PC1 mutants and uses computational simulations to support these findings.

The paper harmonizes Indian gut microbiome cohorts to create a reference atlas and the Indian Gut Health Index, revealing geographic and cohort-specific microbial patterns.

This study evaluates LLMs' ability to predict mutation effects based on structural data without using dedicated predictors, finding comparable accuracy to existing methods.

This paper evaluates the performance of intermediate layers in single-cell foundation models for cell-type and perturbation prediction, finding that some layers perform as well as final layers.

The paper benchmarks single-cell foundation models for cell-type and disease-state prediction, finding they outperform baselines in low-data regimes.

This paper introduces HARMONIC, a framework that improves cell-cell communication inference in spatial transcriptomics by incorporating histology data, showing it better captures anatomical context.

AlphaFold3 pLDDT matches AlphaFold2 at calling disordered residues (Matthews correlation 0.693 vs 0.701); on disorder-containing complexes mean DockQ is similar, but interface features explain less variance (42% vs 58%).

The paper develops a Bayesian gene interaction network to identify 13 genes for PD classification, achieving high AUC in a blood-based transcriptomic analysis of the PPMI cohort.

Tree models on RDKit/Morgan features predict M.tb activity and MIC endpoints for drug-susceptible and resistant isolates, slightly beating pretrained embeddings; released as a Colab tool.

This study shows that selecting optimal bin sizes improves tumor cell phylogeny inference from low-coverage single-cell sequencing data.

The paper shows that horizontal gene transfers can reveal information about extinct and unsampled lineages in species trees through gene tree analysis.