StemDriver: a knowledgebase of gene functions for hematopoietic stem cell fate determination
Year: 2023
Type: article
Abstract: StemDriver is a comprehensive knowledgebase dedicated to the functional annotation of genes participating in the determination of hematopoietic stem cell fate, available at http://biomedbdc.wchscu.cn/StemDriver/. By utilizing single-cell RNA sequencing data, StemDriver has successfully assembled a comprehensive lineage map of hematopoiesis, capturing the entire continuum from the initial formation of hematopoietic stem cells to the fully developed mature cells. Extensive exploration and characterization were conducted on gene expression features corresponding to each lineage commitment. At the current version, StemDriver integrates data from 42 studies, encompassing a diverse range of 14 tissue types spanning from the embryonic phase to adulthood. In order to ensure uniformity and reliability, all data undergo a standardized pipeline, which includes quality data pre-processing, cell type annotation, differential gene expression analysis, identification of gene categories correlated with differentiation, analysis of highly variable genes along pseudo-time, and exploration of gene expression regulatory networks. In total, StemDriver assessed the function of 23 839 genes for human samples and 29 533 genes for mouse samples. Simultaneously, StemDriver also provided users with reference datasets and models for cell annotation. We believe that StemDriver will offer valuable assistance to research focused on cellular development and hematopoiesis.
Source: Nucleic Acids Research
DOI: 10.1093/nar/gkad1063
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,630
Awards: G1437124406, G2286337162, G271885892, G291053052, G4912938464, G4937952549, Functional annotation of new genes aided by deep learning, G6919112675, Developing novel machine learning approaches to studying cell development, Developing Random Field based novel approaches for spatial transcriptomics, G8296126340, Multiscale Resolution and Deep Network Approaches for Deconvolving Different Cell Types in Bulk Tumor using Single-cell Sequencing Data (scDEC), G9000065253, G9000339991
OpenAlex ID: W4388615933
