Discover, rate, and discuss biology preprints.

A 100-million-cell atlas: 50 cancer lines pooled as spheroid villages, treated with 379 drugs at several doses, profiled by combinatorial-barcoding scRNA-seq and embedded with a 10-dimensional scVI model.

This study shows that reducing nutrients increases beneficial bacteria in tomato roots, improving growth through microbial interactions.

This paper introduces SBX, a single-molecule sequencing technology that improves nanopore sequencing accuracy and scalability by encoding DNA into Xpandomers with high signal-to-noise reporter codes.

This study identifies somatic repetitive element insertions as new biomarkers in pan-cancer by analyzing 325 cancer genomes using long-read sequencing.

The paper develops a method to map 5'-aldehyde termini in DNA at single-nucleotide resolution, revealing a preference for adenine bases and linking these lesions to genomic features and chromatin accessibility.

This paper presents the first chromosome-level genome assembly of the Greenland shark, identifying genetic features associated with its extreme longevity and deep-sea adaptations.

The paper assesses MS-based identifications of unannotated human microproteins, finding large variation in reported proteins and PSM quality across studies.

This study examines how training data composition affects deep learning models in single-cell transcriptomics, finding poor generalization to new cell types and benefits from including embryonic stem cell differentiation data.

This paper refutes claims of strong natural selection during the Black Death by showing statistical and technical flaws in the original analysis.

The paper characterizes new Pseudobdellovibrionaceae strains, revealing genomic features and evolutionary insights into bacterial predation modes.

This paper introduces SpatialDIVA, a method to disentangle spatial transcriptomics and histopathology imaging data, showing it outperforms baselines in variation separation and batch effect removal.

The paper identifies genomic features and signatures associated with increased segmental duplication rates through network analysis of duplication events.

A 40B-parameter DNA model trained on 9.3 trillion tokens scores coding and noncoding variants zero-shot, exposes interpretable sequence features, and writes chromosome-scale sequences.

NAVIP predicts the functional impact of sequence variants by considering neighboring variants, improving accuracy over single-variant tools like SnpEff.

This study compares the genome of the temperate coral Astrangia poculata with the tropical coral Acropora millepora to identify gene family expansions related to winter quiescence, innate immunity, and sexual reproduction.

The paper predicts the regulatory effects of non-coding variants across brain and heart cellular contexts using deep learning, identifying rare variants with significant developmental impacts.

This study identifies cis-regulatory roles of transposable elements in livestock genomes using multi-omics data and a novel computational framework.

The paper develops PiCTURE and machine learning models to predict RNA off-target effects of CRISPR base editors in tissue-specific transcriptomes, finding higher risks in colon and lungs compared to brain and ovaries.

The paper develops genomic and non-genomic datasets from TCGA to predict PolyPhen and SIFT scores using machine learning models, finding genomic data provides more accurate predictions.