Genetic screening in immune cells enables systematic, high-throughput investigation of gene function and the identification of regulators of immune behaviors such as tumor cell killing and cell persistence. The authors report the development of the Functional Immunogenomics and Transcriptomics Database (FITdb), a curated, freely accessible platform designed to address common barriers to integration and reuse of functional genomics data in immunology.
The source emphasizes that many existing datasets are generated to address specific biological questions, rely on targeted gene panels, and are produced under heterogeneous experimental conditions. These factors, combined with limited accessibility of raw and processed data in some cases, hinder cross-study comparisons and limit the potential for new discoveries. FITdb was built to harmonize disparate functional genetics datasets and provide standardized outputs and intuitive tools to accelerate discovery in immune regulatory programs.
FITdb currently aggregates data from 43 independent functional genetics screens drawn from the literature and community resources. The collection comprises 32 pooled screens and 11 single-cell screens. Across these integrated datasets, FITdb covers 20,696 mouse genes and 22,293 human genes.
The aggregated data span 199 distinct immune cell types and experimental conditions, reflecting a broad sampling of immune contexts. The authors indicate that all included datasets have been re-analyzed uniformly to enable meaningful cross-study comparisons; specific provenance and links to supplementary materials and code are provided from the preprint record and the FITdb web portal.
To enable cross-study comparisons, FITdb applies a uniform re-analysis pipeline to all integrated datasets. The source states this harmonization is intended to overcome variability introduced by differing experimental designs, targeted panels, and original analysis approaches. Uniform re-processing allows users to compare gene-level effects across screens and species with reduced confounding from inconsistent analysis methods.
The database exposes data at multiple granularities, including gene-level summaries and sgRNA-level information where applicable. This layered access supports both broad, gene-centric queries and in-depth inspection of screen-specific guide-level performance and statistics.
FITdb provides intuitive, gene-centric visualizations to help users rapidly interpret where and how a gene modulates immune cell phenotypes across the integrated collection. The platform also supports detailed exploration of individual screens, enabling users to view screen-specific results, metadata, and the underlying sgRNA-level data.
All datasets and visualizations described are available through the FITdb web portal at https://fitdb.lji.org. The preprint notes that supplementary material and links to data and code accompany the manuscript on the bioRxiv page; users seeking raw files or analysis scripts should consult those resources and the FITdb site.
The database includes two interactive tools to help users place their results in the context of the aggregated resource. “Compare MyGeneSet” identifies statistically significant overlaps between a user-provided gene list and functional gene sets contained in FITdb. This facilitates rapid hypothesis generation and validation by highlighting congruence between external gene lists and prior functional screens.
“Compare MyScreen” enables direct comparison of user-generated screening data with datasets stored in FITdb. According to the source, this tool supports researchers who wish to benchmark their screen results against existing data, identify shared hits, and assess reproducibility or context-specific effects.
Both tools are part of the FITdb portal and operate on the uniformly re-analyzed collection to ensure consistent comparison metrics across studies.
FITdb is freely available at https://fitdb.lji.org. The work describing FITdb is presented as a preprint on bioRxiv (doi: https://doi.org/10.64898/2026.07.28.741304) and has not been certified by peer review; readers should take that into account when interpreting claims.
The source lists competing interests for two authors: one author is reported as co-founder and SAB member of TCura Biosciences, and a second author has the same disclosed relationship. Funding sources declared in the preprint include several foundations and organizations noted in the article metadata. The database and manuscript are distributed under the licensing and copyright terms indicated on the bioRxiv record; supplementary materials, data, and code links are provided through the preprint page.
For full dataset details, methods, supplementary files, and access to the FITdb tools, consult the FITdb portal and the bioRxiv entry referenced above.