Literature DB >> 27279488

BIG: a large-scale data integration tool for renal physiology.

Yue Zhao1, Chin-Rang Yang1, Viswanathan Raghuram1, Jaya Parulekar1, Mark A Knepper2.   

Abstract

Due to recent advances in high-throughput techniques, we and others have generated multiple proteomic and transcriptomic databases to describe and quantify gene expression, protein abundance, or cellular signaling on the scale of the whole genome/proteome in kidney cells. The existence of so much data from diverse sources raises the following question: "How can researchers find information efficiently for a given gene product over all of these data sets without searching each data set individually?" This is the type of problem that has motivated the "Big-Data" revolution in Data Science, which has driven progress in fields such as marketing. Here we present an online Big-Data tool called BIG (Biological Information Gatherer) that allows users to submit a single online query to obtain all relevant information from all indexed databases. BIG is accessible at http://big.nhlbi.nih.gov/.

Keywords:  BIG data; data science; kidney physiology; systems biology

Mesh:

Substances:

Year:  2016        PMID: 27279488      PMCID: PMC5142235          DOI: 10.1152/ajprenal.00249.2016

Source DB:  PubMed          Journal:  Am J Physiol Renal Physiol        ISSN: 1522-1466


  14 in total

1.  Deep proteomic profiling of vasopressin-sensitive collecting duct cells. I. Virtual Western blots and molecular weight distributions.

Authors:  Chin-Rang Yang; Pumipat Tongyoo; Milad Emamian; Pablo C Sandoval; Viswanathan Raghuram; Mark A Knepper
Journal:  Am J Physiol Cell Physiol       Date:  2015-08-26       Impact factor: 4.249

2.  Deep proteomic profiling of vasopressin-sensitive collecting duct cells. II. Bioinformatic analysis of vasopressin signaling.

Authors:  Chin-Rang Yang; Viswanathan Raghuram; Milad Emamian; Pablo C Sandoval; Mark A Knepper
Journal:  Am J Physiol Cell Physiol       Date:  2015-08-26       Impact factor: 4.249

3.  Systems-level analysis of cell-specific AQP2 gene expression in renal collecting duct.

Authors:  Ming-Jiun Yu; R Lance Miller; Panapat Uawithya; Markus M Rinschen; Sookkasem Khositseth; Drew W W Braucht; Chung-Lin Chou; Trairak Pisitkun; Raoul D Nelson; Mark A Knepper
Journal:  Proc Natl Acad Sci U S A       Date:  2009-02-03       Impact factor: 11.205

4.  Proteome-wide measurement of protein half-lives and translation rates in vasopressin-sensitive collecting duct cells.

Authors:  Pablo C Sandoval; Dane H Slentz; Trairak Pisitkun; Fahad Saeed; Jason D Hoffert; Mark A Knepper
Journal:  J Am Soc Nephrol       Date:  2013-09-12       Impact factor: 10.121

5.  Deep Sequencing in Microdissected Renal Tubules Identifies Nephron Segment-Specific Transcriptomes.

Authors:  Jae Wook Lee; Chung-Lin Chou; Mark A Knepper
Journal:  J Am Soc Nephrol       Date:  2015-03-27       Impact factor: 10.121

6.  Quantitative proteomics identifies vasopressin-responsive nuclear proteins in collecting duct cells.

Authors:  Laura K Schenk; Steven J Bolger; Kelli Luginbuhl; Patricia A Gonzales; Markus M Rinschen; Ming-Jiun Yu; Jason D Hoffert; Trairak Pisitkun; Mark A Knepper
Journal:  J Am Soc Nephrol       Date:  2012-03-22       Impact factor: 10.121

Review 7.  Systems biology in physiology: the vasopressin signaling network in kidney.

Authors:  Mark A Knepper
Journal:  Am J Physiol Cell Physiol       Date:  2012-08-29       Impact factor: 4.249

8.  Phosphorylation of UT-A1 on serine 486 correlates with membrane accumulation and urea transport activity in both rat IMCDs and cultured cells.

Authors:  Janet D Klein; Mitsi A Blount; Otto Fröhlich; Chad E Denson; Xiaoxiao Tan; Jae H Sim; Christopher F Martin; Jeff M Sands
Journal:  Am J Physiol Renal Physiol       Date:  2010-01-13

9.  Phosphoproteomic profiling reveals vasopressin-regulated phosphorylation sites in collecting duct.

Authors:  Amar D Bansal; Jason D Hoffert; Trairak Pisitkun; Shelly Hwang; Chung-Lin Chou; Emily S Boja; Guanghui Wang; Mark A Knepper
Journal:  J Am Soc Nephrol       Date:  2010-01-14       Impact factor: 10.121

10.  Quantitative apical membrane proteomics reveals vasopressin-induced actin dynamics in collecting duct cells.

Authors:  Chin-San Loo; Cheng-Wei Chen; Po-Jen Wang; Pei-Yu Chen; Shu-Yu Lin; Kay-Hooi Khoo; Robert A Fenton; Mark A Knepper; Ming-Jiun Yu
Journal:  Proc Natl Acad Sci U S A       Date:  2013-10-01       Impact factor: 11.205

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  7 in total

1.  Data integration in physiology using Bayes' rule and minimum Bayes' factors: deubiquitylating enzymes in the renal collecting duct.

Authors:  Zhe Xue; Jia-Xu Chen; Yue Zhao; Barbara Medvar; Mark A Knepper
Journal:  Physiol Genomics       Date:  2016-12-30       Impact factor: 3.107

2.  From 20th century metabolic wall charts to 21st century systems biology: database of mammalian metabolic enzymes.

Authors:  Callan C Corcoran; Cameron R Grady; Trairak Pisitkun; Jaya Parulekar; Mark A Knepper
Journal:  Am J Physiol Renal Physiol       Date:  2016-12-14

3.  Identification of β-catenin-interacting proteins in nuclear fractions of native rat collecting duct cells.

Authors:  Jacqueline R Hwang; Chung-Lin Chou; Barbara Medvar; Mark A Knepper; Hyun Jun Jung
Journal:  Am J Physiol Renal Physiol       Date:  2017-03-15

4.  Systems-level identification of PKA-dependent signaling in epithelial cells.

Authors:  Kiyoshi Isobe; Hyun Jun Jung; Chin-Rang Yang; J'Neka Claxton; Pablo Sandoval; Maurice B Burg; Viswanathan Raghuram; Mark A Knepper
Journal:  Proc Natl Acad Sci U S A       Date:  2017-10-02       Impact factor: 11.205

Review 5.  How Omics Data Can Be Used in Nephrology.

Authors:  Eugene P Rhee
Journal:  Am J Kidney Dis       Date:  2018-02-23       Impact factor: 8.860

6.  Modulation of blood pressure regulatory genes in the Agtrap-Plod1 locus associated with a deletion in Clcn6.

Authors:  Christine A Klemens; Lashodya V Dissanayake; Vladislav Levchenko; Adrian Zietara; Oleg Palygin; Alexander Staruschenko
Journal:  Physiol Rep       Date:  2022-08

7.  Epithelial Na+ Channel: Reciprocal Control by COMMD10 and Nedd4-2.

Authors:  Adam W Ware; Tanya T Cheung; Sahib Rasulov; Ezra Burstein; Fiona J McDonald
Journal:  Front Physiol       Date:  2018-06-26       Impact factor: 4.566

  7 in total

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