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Statistical modeling for selecting housekeeper genes. Genome Biol 2004;5(8):R59



Pubmed ID


Pubmed Central ID




Scopus ID

2-s2.0-15444373286   154 Citations


There is a need for statistical methods to identify genes that have minimal variation in expression across a variety of experimental conditions. These 'housekeeper' genes are widely employed as controls for quantification of test genes using gel analysis and real-time RT-PCR. Using real-time quantitative RT-PCR, we analyzed 80 primary breast tumors for variation in expression of six putative housekeeper genes (MRPL19 (mitochondrial ribosomal protein L19), PSMC4 (proteasome (prosome, macropain) 26S subunit, ATPase, 4), SF3A1 (splicing factor 3a, subunit 1, 120 kDa), PUM1 (pumilio homolog 1 (Drosophila)), ACTB (actin, beta) and GAPD (glyceraldehyde-3-phosphate dehydrogenase)). We present appropriate models for selecting the best housekeepers to normalize quantitative data within a given tissue type (for example, breast cancer) and across different types of tissue samples.

Author List

Szabo A, Perou CM, Karaca M, Perreard L, Palais R, Quackenbush JF, Bernard PS


Aniko Szabo PhD Professor in the Institute for Health and Equity department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

ATPases Associated with Diverse Cellular Activities
Adenosine Triphosphatases
Breast Neoplasms
Cell Line, Tumor
DNA, Complementary
DNA-Binding Proteins
Gene Dosage
Gene Expression
Gene Expression Profiling
Genes, Essential
Mitochondrial Proteins
Models, Genetic
Oligonucleotide Array Sequence Analysis
Proteasome Endopeptidase Complex
RNA Splicing Factors
RNA, Messenger
RNA-Binding Proteins
Reference Standards
Reverse Transcriptase Polymerase Chain Reaction
Ribonucleoprotein, U2 Small Nuclear
Ribosomal Proteins
Transcription Factors
jenkins-FCD Prod-482 91ad8a360b6da540234915ea01ff80e38bfdb40a