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Editorial
. 2018 May;29(5):1351-1353.
doi: 10.1681/ASN.2018030288. Epub 2018 Apr 11.

Using Large Datasets to Understand CKD

Thomas A Drysdale  1
Affiliations
Editorial

Using Large Datasets to Understand CKD

Thomas A Drysdale. J Am Soc Nephrol. 2018 May.
No abstract available

Keywords: Bioinformatics; SHROOM3; chronic kidney disease.

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Figures

Figure 1.
Figure 1.
Large datasets can be fed into a bioinformatics pipeline to generate insights into the biology of a candidate gene. This study shows an integrated analysis of very large datasets of genomic data. In this case, the candidate gene was SHROOM3, but any gene could be analyzed in this manner. The data generated uncovered novel genetic variants, important insights into known variants, and potential novel interactions at the genomic and protein level for SHROOM3. ChIP-Seq, Chromatin Immunoprecipitation followed by next generation sequencing; HiChIP, Protein-Centered Chromosome Conformation Capture; RNA-seq, RNA sequencing; SNP, single-nucleotide polymorphism.
See this image and copyright information in PMC

Comment on

  • Characterization of Coding/Noncoding Variants for SHROOM3 in Patients with CKD.
    Prokop JW, Yeo NC, Ottmann C, Chhetri SB, Florus KL, Ross EJ, Sosonkina N, Link BA, Freedman BI, Coppola CJ, McDermott-Roe C, Leysen S, Milroy LG, Meijer FA, Geurts AM, Rauscher FJ 3rd, Ramaker R, Flister MJ, Jacob HJ, Mendenhall EM, Lazar J. Prokop JW, et al. J Am Soc Nephrol. 2018 May;29(5):1525-1535. doi: 10.1681/ASN.2017080856. Epub 2018 Feb 23. J Am Soc Nephrol. 2018. PMID: 29476007 Free PMC article.

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