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SCDevDB: A Database for Insights Into Single-Cell Gene Expression Profiles During Human Developmental Processes

Overview
Journal Front Genet
Date 2019 Oct 16
PMID 31611909
Citations 10
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Abstract

Single-cell RNA-seq studies profile thousands of cells in developmental processes. Current databases for human single-cell expression atlas only provide search and visualize functions for a selected gene in specific cell types or subpopulations. These databases are limited to technical properties or visualization of single-cell RNA-seq data without considering the biological relations of their collected cell groups. Here, we developed a database to investigate single-cell gene expression profiling during different developmental pathways (SCDevDB). In this database, we collected 10 human single-cell RNA-seq datasets, split these datasets into 176 developmental cell groups, and constructed 24 different developmental pathways. SCDevDB allows users to search the expression profiles of the interested genes across different developmental pathways. It also provides lists of differentially expressed genes during each developmental pathway, T-distributed stochastic neighbor embedding maps showing the relationships between developmental stages based on these differentially expressed genes, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes analysis results of these differentially expressed genes. This database is freely available at https://scdevdb.deepomics.org.

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References
1.
Yan L, Yang M, Guo H, Yang L, Wu J, Li R . Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells. Nat Struct Mol Biol. 2013; 20(9):1131-9. DOI: 10.1038/nsmb.2660. View

2.
Gafni O, Weinberger L, AlFatah Mansour A, Manor Y, Chomsky E, Ben-Yosef D . Derivation of novel human ground state naive pluripotent stem cells. Nature. 2013; 504(7479):282-6. DOI: 10.1038/nature12745. View

3.
Allegrucci C, Young L . Differences between human embryonic stem cell lines. Hum Reprod Update. 2006; 13(2):103-20. DOI: 10.1093/humupd/dml041. View

4.
Griffiths J, Scialdone A, Marioni J . Using single-cell genomics to understand developmental processes and cell fate decisions. Mol Syst Biol. 2018; 14(4):e8046. PMC: 5900446. DOI: 10.15252/msb.20178046. View

5.
Wang D, Bodovitz S . Single cell analysis: the new frontier in 'omics'. Trends Biotechnol. 2010; 28(6):281-90. PMC: 2876223. DOI: 10.1016/j.tibtech.2010.03.002. View