scRNASeqDB: A Database for RNA-Seq Based Gene Expression Profiles in Human Single Cells
Single-cell RNA sequencing (scRNA-Seq) is rapidly becoming a powerful tool for high-throughput transcriptomic analysis of cell states and dynamics at the single cell level. Both the number and quality of scRNA-Seq datasets have dramatically increased recently. A database that can comprehensively col...
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| Veröffentlicht in: | Genes 2017-12, Vol.8 (12), p.368 |
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| creator | Cao, Yuan Zhu, Junjie Jia, Peilin Zhao, Zhongming |
| description | Single-cell RNA sequencing (scRNA-Seq) is rapidly becoming a powerful tool for high-throughput transcriptomic analysis of cell states and dynamics at the single cell level. Both the number and quality of scRNA-Seq datasets have dramatically increased recently. A database that can comprehensively collect, curate, and compare expression features of scRNA-Seq data in humans has not yet been built. Here, we present scRNASeqDB, a database that includes almost all the currently available human single cell transcriptome datasets (
= 38) covering 200 human cell lines or cell types and 13,440 samples. Our online web interface allows users to rank the expression profiles of the genes of interest across different cell types. It also provides tools to query and visualize data, including Gene Ontology and pathway annotations for differentially expressed genes between cell types or groups. The scRNASeqDB is a useful resource for single cell transcriptional studies. This database is publicly available at bioinfo.uth.edu/scrnaseqdb/. |
| doi_str_mv | 10.3390/genes8120368 |
| format | Article |
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= 38) covering 200 human cell lines or cell types and 13,440 samples. Our online web interface allows users to rank the expression profiles of the genes of interest across different cell types. It also provides tools to query and visualize data, including Gene Ontology and pathway annotations for differentially expressed genes between cell types or groups. The scRNASeqDB is a useful resource for single cell transcriptional studies. This database is publicly available at bioinfo.uth.edu/scrnaseqdb/.</description><identifier>ISSN: 2073-4425</identifier><identifier>EISSN: 2073-4425</identifier><identifier>DOI: 10.3390/genes8120368</identifier><identifier>PMID: 29206167</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Cell lines ; Gene expression ; Ribonucleic acid ; RNA ; Transcription</subject><ispartof>Genes, 2017-12, Vol.8 (12), p.368</ispartof><rights>Copyright MDPI AG 2017</rights><rights>2017 by the authors. 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c478t-82780f9201e76204c3f8213f8da8aef64967a7fae9c595786b0e192cee85d7893</citedby><cites>FETCH-LOGICAL-c478t-82780f9201e76204c3f8213f8da8aef64967a7fae9c595786b0e192cee85d7893</cites><orcidid>0000-0002-3477-0914</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5748686/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5748686/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27903,27904,53769,53771</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29206167$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Cao, Yuan</creatorcontrib><creatorcontrib>Zhu, Junjie</creatorcontrib><creatorcontrib>Jia, Peilin</creatorcontrib><creatorcontrib>Zhao, Zhongming</creatorcontrib><title>scRNASeqDB: A Database for RNA-Seq Based Gene Expression Profiles in Human Single Cells</title><title>Genes</title><addtitle>Genes (Basel)</addtitle><description>Single-cell RNA sequencing (scRNA-Seq) is rapidly becoming a powerful tool for high-throughput transcriptomic analysis of cell states and dynamics at the single cell level. Both the number and quality of scRNA-Seq datasets have dramatically increased recently. A database that can comprehensively collect, curate, and compare expression features of scRNA-Seq data in humans has not yet been built. Here, we present scRNASeqDB, a database that includes almost all the currently available human single cell transcriptome datasets (
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Both the number and quality of scRNA-Seq datasets have dramatically increased recently. A database that can comprehensively collect, curate, and compare expression features of scRNA-Seq data in humans has not yet been built. Here, we present scRNASeqDB, a database that includes almost all the currently available human single cell transcriptome datasets (
= 38) covering 200 human cell lines or cell types and 13,440 samples. Our online web interface allows users to rank the expression profiles of the genes of interest across different cell types. It also provides tools to query and visualize data, including Gene Ontology and pathway annotations for differentially expressed genes between cell types or groups. The scRNASeqDB is a useful resource for single cell transcriptional studies. This database is publicly available at bioinfo.uth.edu/scrnaseqdb/.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>29206167</pmid><doi>10.3390/genes8120368</doi><orcidid>https://orcid.org/0000-0002-3477-0914</orcidid><oa>free_for_read</oa></addata></record> |
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| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central Open Access; MDPI - Multidisciplinary Digital Publishing Institute; PubMed Central |
| subjects | Cell lines Gene expression Ribonucleic acid RNA Transcription |
| title | scRNASeqDB: A Database for RNA-Seq Based Gene Expression Profiles in Human Single Cells |
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