Mapping Identifiers for the Integration of Genomic Datasets with the R/Bioconductor package biomaRt
By S. Durinck, P. Spellman, Ewan Birney, Wolfgang Huber. Nature Protocols, 2009
3,694 citations recorded by the source corpus.
Genomic experiments produce multiple views of biological systems, among them are DNA sequence and copy number variation, and mRNA and protein abundance. Understanding these systems needs integrated bioinformatic analysis. Public databases such as Ensembl provide relationships and mappings between the relevant sets of probe and target molecules. However, the relationships can be biologically complex and the content of the databases is dynamic. We demonstrate how to use the computational environment R to integrate and jointly analyze experimental datasets, employing BioMart web services to provide the molecule mappings. We also discuss typical problems that are encountered in making gene-to-transcript–to-protein mappings. The approach provides a flexible, programmable and reproducible basis for state-of-the-art bioinformatic data integration.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/e3271609bd0814e337af0e5693f4471c5a392c5b
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In Molecular Biology
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Citations in the corpus
- TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment (2020, background): “0 (38), which enables the gene search across different species in TISCH.”
- Jetset: selecting the optimal microarray probe set to represent a gene (2011, background): “In this case, a probe set with an expression pattern of interest can be mapped to a particular gene or set of transcripts using annotations from the manufacturer or from others [1-4].”
- RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR (2016, neutral): “## [1] "Xkr4" "Rgs20" "Cpa6" "Sulf1" "Eya1" ## [6] "Msc" "Sbspon" "Pi15" "Crispld1" "Kcnq5" ## [11] "Ptpn18" "Arhgef4" "2010300C02Rik" "Aff3" "Npas2" ## [16] "Tbc1d8" "Creg2" "Il1r1" "Il18r1" "Il18rap"”
- A Living Biobank of Breast Cancer Organoids Captures Disease Heterogeneity. (2018, positive): “We first mapped the Ensembl identifiers to gene symbols using the biomaRt R package (Durinck et al., 2009).”