Renal Gene Expression Database (RGED): a relational database of gene expression profiles in kidney disease

Zhang, Qingzhou; Yang, Bo; Chen, Xujiao; Xu, Jing; Mei, Changlin; Mao, Zhiguo

doi:10.1093/database/bau092

Cited by 19 publications

(10 citation statements)

References 24 publications

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“…TCGA (Weinstein et al, 2013) is another resource on cancer genome data containing an array of molecular alterations underlying 206 cases of adult soft tissue sarcomas. RGED (Zhang et al, 2014) is a database of gene expression profiles in kidney disease which covers 55 RNA-sequence data, 5,299 DNA microarray, 101 cell lines, and 5,253 tissues. Connectivity Map (Lamb et al, 2006) is a very large genome-scale library of cellular signatures that catalogues transcriptional responses to chemical, genetic, and disease perturbation.…”

Section: Resultsmentioning

confidence: 99%

In Silico Toxicology Data Resources to Support Read-Across and (Q)SAR

et al. 2019

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A plethora of databases exist online that can assist in in silico chemical or drug safety assessment. However, a systematic review and grouping of databases, based on purpose and information content, consolidated in a single source, has been lacking. To resolve this issue, this review provides a comprehensive listing of the key in silico data resources relevant to: chemical identity and properties, drug action, toxicology (including nano-material toxicity), exposure, omics, pathways, Absorption, Distribution, Metabolism and Elimination (ADME) properties, clinical trials, pharmacovigilance, patents-related databases, biological (genes, enzymes, proteins, other macromolecules etc.) databases, protein-protein interactions (PPIs), environmental exposure related, and finally databases relating to animal alternatives in support of 3Rs policies. More than nine hundred databases were identified and reviewed against criteria relating to accessibility, data coverage, interoperability or application programming interface (API), appropriate identifiers, types of in vitro, in vivo ,-clinical or other data recorded and suitability for modelling, read-across, or similarity searching. This review also specifically addresses the need for solutions for mapping and integration of databases into a common platform for better translatability of preclinical data to clinical data.

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Section: Resultsmentioning

confidence: 99%

In Silico Toxicology Data Resources to Support Read-Across and (Q)SAR

et al. 2019

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“…A similar database derived from data mining of GEO database is the Renal Gene Expression Database (RGED), which allows querying expression profiling of genes in the topic of kidney diseases32. The majority of these databases are only focused in one or maximum two ‘omic platforms, lacking a multi-omics database that unifies the bulk of the scattered single ‘omics repositories.…”

Section: Discussionmentioning

confidence: 99%

Establishment of a integrative multi-omics expression database CKDdb in the context of chronic kidney disease (CKD)

Fernandes

Husi

2017

Sci Rep

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Complex human traits such as chronic kidney disease (CKD) are a major health and financial burden in modern societies. Currently, the description of the CKD onset and progression at the molecular level is still not fully understood. Meanwhile, the prolific use of high-throughput omic technologies in disease biomarker discovery studies yielded a vast amount of disjointed data that cannot be easily collated. Therefore, we aimed to develop a molecule-centric database featuring CKD-related experiments from available literature publications. We established the Chronic Kidney Disease database CKDdb, an integrated and clustered information resource that covers multi-omic studies (microRNAs, genomics, peptidomics, proteomics and metabolomics) of CKD and related disorders by performing literature data mining and manual curation. The CKDdb database contains differential expression data from 49395 molecule entries (redundant), of which 16885 are unique molecules (non-redundant) from 377 manually curated studies of 230 publications. This database was intentionally built to allow disease pathway analysis through a systems approach in order to yield biological meaning by integrating all existing information and therefore has the potential to unravel and gain an in-depth understanding of the key molecular events that modulate CKD pathogenesis.

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“…We searched the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database [ 15 ] for gene expression profiling experiments using the following keywords: scRNAseq, single-cell RNA-Seq, and single-cell transcriptome. Such strategies have been successfully utilized in our previous work and in a wide range of databases developed by our group [ 16 , 17 , 18 ] and by others [ 19 , 20 , 21 , 22 ]. A detailed description is provided in Table S1 .…”

Section: Methodsmentioning

confidence: 99%

scRNASeqDB: A Database for RNA-Seq Based Gene Expression Profiles in Human Single Cells

Cao

Zhu

Jia

et al. 2017

Genes

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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 (n = 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 .

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Renal Gene Expression Database (RGED): a relational database of gene expression profiles in kidney disease

Cited by 19 publications

References 24 publications

In Silico Toxicology Data Resources to Support Read-Across and (Q)SAR

In Silico Toxicology Data Resources to Support Read-Across and (Q)SAR

Establishment of a integrative multi-omics expression database CKDdb in the context of chronic kidney disease (CKD)

scRNASeqDB: A Database for RNA-Seq Based Gene Expression Profiles in Human Single Cells

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