Dataset Information

Defining Housekeeping Genes Suitable for RNA-seq Analysis of the Human Allograft Kidney Biopsy Tissue

ABSTRACT: This study has used nine different normalization methods to derive housekeeping genes from a RNA-seq data set of 47611 transcripts derived from 30 biopsies. Overall design: The biopsies were collected in a variety of clinical settings, with 5 replicates each of the following clinicla settings: normal function, acute tubular injury, acute rejection, interstitial nephritis, interstitial fibrosis/tubular atrophy and polyomavirus nephropathy. Transcripts with coefficient of variation below the 2nd percentile were designated as housekeeping genes, and validated by showing their virtual absence in diseased allograft derived transcriptomic data sets available in the Gene Expression Omnibus.

INSTRUMENT(S): Ion Torrent Proton (Homo sapiens)

ORGANISM(S): Homo sapiens

SUBMITTER: Parmjeet Randhawa

PROVIDER: GSE120495 | GEO | 2019-07-10

REPOSITORIES: GEO

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Dataset's files

Source:

			Action	DRS
	GSE120495_1._a_Raw_reads_57738.xlsx	Xlsx
	GSE120495_2._Library_size_Normalization.xlsx	Xlsx
	GSE120495_3._TPM_Normalization.xlsx	Xlsx
	GSE120495_4._RPKM_Normaliztion.xlsx	Xlsx
	GSE120495_5._Median_Normalization.xlsx	Xlsx

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Publications

Defining housekeeping genes suitable for RNA-seq analysis of the human allograft kidney biopsy tissue.

Wang Zijie Z Lyu Zili Z Pan Ling L Zeng Gang G Randhawa Parmjeet P

BMC medical genomics 20190617 1

<h4>Background</h4>RNA-seq is poised to play a major role in the management of kidney transplant patients. Rigorous definition of housekeeping genes (HKG) is essential for further progress in this field. Using single genes or a limited set HKG is inherently problematic since their expression might be altered by specific diseases in the patients being studied.<h4>Methods</h4>To generate a HKG set specific for kidney transplantation, we performed RNA-sequencing from renal allograft biopsies collec ...[more]

PMID: 31208411

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Project description:BACKGROUND:RNA-seq is poised to play a major role in the management of kidney transplant patients. Rigorous definition of housekeeping genes (HKG) is essential for further progress in this field. Using single genes or a limited set HKG is inherently problematic since their expression might be altered by specific diseases in the patients being studied. METHODS:To generate a HKG set specific for kidney transplantation, we performed RNA-sequencing from renal allograft biopsies collected in a variety of clinical settings. Various normalization methods were applied to identify transcripts that had a coefficient of variation of expression that was below the 2nd percentile across all samples, and the corresponding genes were designated as housekeeping genes. Comparison with transcriptomic data from the Gene Expression Omnibus (GEO) database, pathway analysis and molecular biological functions were utilized to validate the housekeeping genes set. RESULTS:We have developed a bioinformatics solution to this problem by using nine different normalization methods to derive large HKG gene sets from a RNA-seq data set of 47,611 transcripts derived from 30 biopsies. These biopsies were collected in a variety of clinical settings, including normal function, acute rejection, interstitial nephritis, interstitial fibrosis/tubular atrophy and polyomavirus nephropathy. Transcripts with coefficient of variation below the 2nd percentile were designated as HKG, and validated by showing their virtual absence in diseased allograft derived transcriptomic data sets available in the GEO. Pathway analysis indicated a role for these genes in maintenance of cell morphology, pyrimidine metabolism, and intracellular protein signaling. CONCLUSIONS:Utilization of these objectively defined HKG data sets will guard against errors resulting from focusing on individual genes like 18S RNA, actin & tubulin, which do not maintain constant expression across the known spectrum of renal allograft pathology.