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scPCIP annotates plant cell types across species using conserved gene programs, outperforming marker-based and deep-learning methods in held-out datasets.

brisc is a multithreaded CPU toolkit for single-cell analysis that is faster and more reproducible than existing tools like Scanpy and Seurat.

This paper evaluates a deep learning model trained on axillary lymph nodes for predicting metastasis in lymph nodes from different anatomical regions across cancers.

The paper uses computational methods to analyze antioxidant effects on stress response mechanisms in Candida albicans, identifying naringenin and quercetin as potential modulators of Sir2/SIRT3 pathways.

SQANTI-evidence annotates genomes from long-read RNA-seq without a reference, achieving high precision in gene and isoform prediction across species.

This paper compares neutrophil transcriptomic profiles in rheumatoid arthritis and COVID-19 to identify shared immune pathways and disease-specific features.

PixAQ improves single-cell protein quantification in imaging mass cytometry by assigning pixel signals to specific markers using spatial and intensity models.

NORMA 3.0 provides enhanced visualization and analysis of annotated biological networks with improved group representation and layout algorithms.

The paper benchmarks cryo-EM re-refinement selection methods against no operation using a fixed cohort and scoring tools, finding increased disagreement with higher-resolution references.

TensorPLS is an R package for analyzing longitudinal omics data using PLS-DA, handling missing data and providing temporal insights through VIP scores and cross-validation.

This paper introduces an end-to-end in silico framework to validate and test clinical genome sequencing pipelines from simulated reads to reportable findings using a fixed truth set.

This paper evaluates optimiser repairs in living-cell self-driving labs, showing when they improve performance and when constraints make them redundant.

CRISP identifies and localizes interactions between biological signals in single-cell and spatial transcriptomic data, revealing their effects on tissue function and gene regulation.

GOParsimony filters GO terms to identify the most parsimonious functional annotations for gene/protein sets, outperforming existing methods in yeast and human data.

This study evaluates Sourmash's ability to profile microbial strains from long-read metagenomes, showing accurate gene content recovery and abundance estimation.

This paper benchmarks single-cell foundation models against baselines for annotation, perturbation and regulatory inference using diverse datasets.

vafSOMA detects cell-layer-fixed somatic mutations in plants using tissue composition, improving mutation detection and rate estimation in bulk sequencing data.

SeCAT is a Nextflow pipeline that harmonizes 16S rRNA metabarcoding datasets by identifying a consensus sequence window and testing its impact on community structure before merging datasets.

SPaRTA enables non-experts to query spatial-omics data via natural language, achieving 63-77% accuracy with fine-tuning on cancer tissue datasets.

The paper evaluates an eight-gene module associated with trogocytosis in CRC, finding its correlation with T-cell fraction is typical of random modules, suggesting it reflects cellular composition rather than trogocytosis.