ReviewGenome biology2025
Insights, opportunities, and challenges provided by large cell atlases.
Review in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Castration-resistant prostate cancer as an adaptive tumor ecosystem: coupling tumor evolution with microenvironmental reprogramming.Cancer metastasis reviews · 2026Review
- HuBMAP Data Portal: a resource for multimodal spatial and single-cell data of healthy human tissues.ArXiv · 2026Article
- The Single Cell Notebooks for inclusive and accessible training in single-cell and spatial omics.Nature genetics · 2026Article
- Integration of large, complex single-cell datasets with Harmony2.bioRxiv : the preprint server for biology · 2026Article
- Parameter-free representations outperform single-cell foundation models on downstream benchmarks.ArXiv · 2026Article
- Parameter-free representations outperform single-cell foundation models on downstream benchmarks.bioRxiv : the preprint server for biology · 2026Article
- Defining the role of natural killer cells in acute myeloid leukemia through the lens of single-cell omics.Frontiers in immunology · 2026Review
- CIA: unveiling cellular identities with cluster-independent annotation in single-cell RNA sequencing data for comprehensive cell type characterization and exploration.BMC bioinformatics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
32 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The field of single-cell biology is growing rapidly, generating large amounts of data from a variety of species, disease conditions, tissues, and organs. Coordinated efforts such as CZI CELLxGENE, HuBMAP, Broad Institute Single Cell Portal, and DISCO allow researchers to access large volumes of curated datasets, including more than just scRNA-seq data. These resources have created an opportunity to build and expand the computational biology ecosystem to develop tools necessary for data reuse and for extracting novel biological insights. We highlight achievements made so far, areas where further development is needed, and specific challenges that need to be overcome.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.