Epithelial Systems Biology Laboratory

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Proteomic and Transcriptomic Databases

Scientists in the Epithelial Systems Biology Laboratory, headed by Mark Knepper, have generated a large number of online databases making proteomic and transcriptomic findings available to the general public. In addition, ESBL personnel have helped a number of extramural scientists to curate their own data sets to create online resources. These data sets are hosted by NIH (Helix Systems, High-Performance Computing at the NIH). Click here to access.

References:

  1. Clark JZ, Chen L, Chou CL, Jung HJ, Lee JW, Knepper MA. Representation and relative abundance of cell-type selective markers in whole-kidney RNA-Seq data. Kidney Int. 2019; 95:787-796. PMID: 30826016.

  2. Medvar B, Sarkar A, Knepper M, Pisitkun T. Sequence-based searching of custom proteome and transcriptome databases. Physiol Rep. 2018; 6:e13846. PMID: 30230259.

  3. Chen L, Lee JW, Chou CL, Nair AV, Battistone MA, Paunescu TG, Merkulova M, Breton S, Verlander JW, Wall SM, Brown D, Burg MB, Knepper MA. Transcriptomes of major renal collecting duct cell types in mouse identified by single-cell RNA-seq. Proc Natl Acad Sci USA. 2017; 114:E9989-E9998. PMID: 29089413.

  4. Corcoran CC, Grady CR, Pisitkun T, Parulekar J, Knepper MA. From 20th century metabolic wall charts to 21st century systems biology: database of mammalian metabolic enzymes. Am J Physiol Renal Physiol. 2017; 312: F533-F542. PMID: 27974320.

  5. Zhao Y, Yang CR, Raghuram V, Parulekar J, Knepper MA. BIG: a large-scale data integration tool for renal physiology. Am J Physiol Renal Physiol. 2016; 311:F787-F792. PMID: 27279488.