http://genomebiology.com/2002/3/7/research/0034.1
co
m
m
en
t
review
s
rep
o
rts
d
ep
o
sited
research
in
teractio
n
s
in
fo
rm
atio
n
refereed
research
Research
Accurate normalization of real-time quantitative RT-PCR data by
geometric averaging of multiple internal control genes

 


G46

 
G46

  !"
"#$% & '(
')(
*+,,,"#
-
.
 G46
./*
 . 
0-..1
Abstract
Background: Gene-expression analysis is increasingly important in biological research, with real-
time reverse transcription PCR (RT-PCR) becoming the method of choice for high-throughput
and accurate expression profiling of selected genes. Given the increased sensitivity,
reproducibility and large dynamic range of this methodology, the requirements for a proper
internal control gene for normalization have become increasingly stringent. Although
housekeeping gene expression has been reported to vary considerably, no systematic survey has
properly determined the errors related to the common practice of using only one control gene,
nor presented an adequate way of working around this problem.
Results: We outline a robust and innovative strategy to identify the most stably expressed
control genes in a given set of tissues, and to determine the minimum number of genes required to
calculate a reliable normalization factor. We have evaluated ten housekeeping genes from different
abundance and functional classes in various human tissues, and demonstrated that the conventional
use of a single gene for normalization leads to relatively large errors in a significant proportion of
samples tested. The geometric mean of multiple carefully selected housekeeping genes was
validated as an accurate normalization factor by analyzing publicly available microarray data.
Conclusions: The normalization strategy presented here is a prerequisite for accurate RT-PCR
expression profiling, which, among other things, opens up the possibility of studying the biological
relevance of small expression differences.
Published: 18 June 2002
Genome Biology 2002, 3(7):research0034.1–0034.11
The electronic version of this article is the complete one and can be
found online at http://genomebiology.com/2002/3/7/research/0034
© 2002 Vandesompele et al., licensee BioMed Central Ltd
(Print ISSN 1465-6906; Online ISSN 1465-6914)
Received: 20 December 2001
Revised: 10 April 2002
Accepted: 7 May 2002
Background
"*2     -


    1-  #. $ -   
2 -  2% -#
2
-3 3
 11#*
    -  %  3 1-  
 

    . 43  %
#  

  1#%-
#
  5 . !  3 # 
     #     -   3  1
  6'7
3# *
4*%  #

 
 
 -2 
 *
 
    

1  -
     *
 1 3#  
 
1     
%1 68*97. # #5  #% # %- 

#-##-#- 


  % 5 
#
 # 
#*1  
 1    
 

2 Genome Biology Vol 3 No 7  



% 4*. %#
 #  3 #
5 # 
    
:   # 

#  
5 .
% %1    1    -*2 *
 
 # #
  -

:*

 
  13   
% %.  - #%1

:# % .$*
   12 -*5
 #
-   
1    :  #

1   
 1  
        

  2
 3# - 3#   .
# 5
:  #

 5
 #1  .4#
% -
  -  #    
  5. 4#
5   #:
*
      . !%
  

      
 1  5  #  

 
2

3#


  %1


    . 11 # -  -*

- #  
  
:
#  #      
   

 3  % #
 .4#
3   % 1  -  
1 13
 
   2
 ;.(< 



 
  6(7. 
  # 1 
#   11-  
-  6=*)7. G46# 31   #     ')  8)

       # 1    

 

  # #-# 1 
 
-
   . 4# 
     
 1 # 1  %  *
4*
  .
4
 - 
  5  

: #
 .4#    *  
  # --* #%#
   % -
  2*


.&3%

  
  # 
 % 2   - 3#  %*
 # 
 1 2 .#*
 #3 ## --2 *#-#
      -%    2

*% 1>%36+*'87?.@#
#    %
 1 -

- *
4*
#
#5
  
-#%1
 - *
-.A#  
32 %%
', 

   # - -   'B  
#
  
      - 

:  1  
  -  

1
: -*2 
. G46#

 #  
: 3  %
  
% 3# 5 

-   -1%1
.
Results
Expression profiling of housekeeping genes

 3 - 

 # -
- >






 





  ? >  41 '   - 

 
1
 
#
 
:

2       -
   #

   C 41 8 
 5 ?.
3  -- #1-
    
3## -    #
# # - 
-# 1 *-. 4# 2 
% # ',- 3 
B9
1 
   >  *
 1  
    ?
 8, #*
 

 11  
  
   %
 'B

 

+
1*
3 

+
#
   
-
>#
 1
  1
 -
 #
 




-

     ?. 4# 3 2 
%   %1    1*
  >  
  ?.
Single control normalization error
4 
 #  1     # 


  -# -- 
:*

3# #  3-
 3   
  > 
 # 
   ? 

  # -  
: 
  > 
! 
# ?.G463-
> 13#-  
 ?
 5 '.
A
1 % %  - #' *
# * 2  133


 -  #  # - -
   
:. % 3    9(
3*1*3
1  - )=(3*1*
3 

1 3##%1 
>1 

 11 
 
 1
3  
    
    
 #C # 
   
B)
+8(   ? >G46- '?. A 
 #  

  1 3   1      
    # 
  -. 4# %- ;(#  +,#
 %   B., >- 8.'*B.+?
  =.9 >-
B.,*',.+?
 %.
Gene-stability measure and ranking of selected
housekeeping genes
A -#-*2 %  #1

: 1     1  
-.&3%
%# 
 12 
-%-
3- 1
 

:# -
%  %*
 5.4 # 1
3%*
  -* 1
   
 # 2 
1    -   # 1     *
:
2  % . 4# 
     #  #
# 2     3    -  

co
m
m
en
t
review
s
rep
o
rts
d
ep
o
sited
research
in
teractio
n
s
in
fo
rm
atio
n
refereed
research
http://genomebiology.com/2002/3/7/research/0034.3
 

-  #2
*
   . A #  3
 %   # 2 
    3 *  # - -     # 
#  > 1#?   # -    >?   
2 
 3#  - %    -
 -2  1.G46%-3

 # 3  % 3#  # 
-    #  %   # -#

 
 2  
    # 
 -* 1
    # %- 3 
Table 1
Internal control genes evaluated in this study
Symbol Accession Name Function Localization Pseudo- Primers† Alias IMAGE‡
number gene*
ACTB NM_001101 Beta actin Cytoskeletal structural 7p15-p12 + S 510455
protein
B2M NM_004048 Beta-2-microglobulin Beta-chain of major 15q21-q22 - S 51940
histocompatibility complex
class I molecules
GAPD NM_002046 Glyceraldehyde-3- Oxidoreductase in glycolysis 12p13 + D 510510
phosphate dehydrogenase and gluconeogenesis
HMBS NM_000190 Hydroxymethyl-bilane Heme synthesis, porphyrin 11q23 - D Porphobilinogen 245564
synthase metabolism deaminase
HPRT1 NM_000194 Hypoxanthine Purine synthesis in salvage Xq26 + D 345845
phosphoribosyl-transferase 1 pathway
RPL13A NM_012423 Ribosomal protein L13a Structural component of the 19q13 + D 23 kDa highly -
large 60S ribosomal subunit basic protein
SDHA NM_004168 Succinate dehydrogenase Electron transporter in the 5p15 + D 375812
complex, subunit A TCA cycle and respiratory
chain
TBP NM_003194 TATA box binding protein General RNA polymerase II 6q27 - D 280735
transcription factor
UBC M26880 Ubiquitin C Protein degradation 12q24 - D 510582
YWHAZ NM_003406 Tyrosine 3-monooxygenase/ Signal transduction by 2p25 + S§ Phospholipase 416026
tryptophan 5-monooxygenase binding to phosphorylated A2
activation protein, zeta serine residues on a variety
polypeptide of signaling molecules
*Presence (+) or absence (-) of a retropseudogene in the genome determined by BLAST analysis of the mRNA sequence using the high-throughput
genomic sequences database (htgs) or human genome as database. †Localization of forward and reverse primer in different exons (D) or the same exon
(S). ‡IMAGE cDNA clone number according to [14]. §A single-exon gene.
Table 2
Primer sequences for internal control genes
Symbol* Forward primer Reverse primer
ACTB CTGGAACGGTGAAGGTGACA AAGGGACTTCCTGTAACAATGCA
B2M TGCTGTCTCCATGTTTGATGTATCT TCTCTGCTCCCCACCTCTAAGT
GAPD TGCACCACCAACTGCTTAGC GGCATGGACTGTGGTCATGAG
HMBS† GGCAATGCGGCTGCAA GGGTACCCACGCGAATCAC
HPRT1 TGACACTGGCAAAACAATGCA GGTCCTTTTCACCAGCAAGCT
RPL13A CCTGGAGGAGAAGAGGAAAGAGA TTGAGGACCTCTGTGTATTTGTCAA
SDHA TGGGAACAAGAGGGCATCTG CCACCACTGCATCAAATTCATG
UBC ATTTGGGTCGCGGTTCTTG TGCCTTGACATTCTCGATGGT
YWHAZ ACTTTTGGTACATTGTGGCTTCAA CCGCCAGGACAAACCAGTAT
*TBP primer sequences are described in [24]. †HMBS primer sequences kindly provided by E. Mensink and L. van de Locht (Nijmegen, The Netherlands).

%     - 3#  #  - .
" 3##3  % #%#
  12 *
.  
- # #  -    *-
3  2    # - 3# # #-#   %
     
1   3  % 2 
# --  ##% #
  1 2  
#   
 .4
- # -
1  *

3 #%3      >? 
!  /2*
-
*#
*
  #-* 1
     -  
-%    
  >-
    %1 
 #
# 5 ?.4#-
1 
 #
3 * - # - - ># 
 #  3# #
#-#  %?     3 %   #

- - .$ - #  
3  # 
- # %   -#
2  1 >G46- 8
41 B?. A
 #  *

%3  #3 %

4*2
 # 
-
 *
-##
#
:
%.
Normalization factor calculation based on the
geometric mean of multiple control genes
@  #   
  2  %

 
: 1
 # - -
       5.  5
  
:
1 #2 %  #1 * 
-
# -- 
 1.G46%-*
-   #  -
 3      # -


  ##


 # 

1   1 - %   1  *
  13 #   - . 4# 
1   -
  -
%-- * 13
    . A   1%  #  

:  #  # # 1
- #31 % 
  .D###
 
  % 
  5 
  2

-#- 3# 3-- 1

 3# 

 
      %*
1.G46#

 3    5 

-  #      -   % 1
2     # 
:     - *
 #- 3##  
 -   .
4-  #    
 3 

 #


  ##
  1-
     4* 
:  >G46


 E B?
  3     
  -  
# > F '?# - #   -  1  #
3
: >G46
F'
?.4

#  1      -
 # #
-   
:
 # 3  % 
GF'
3
13#3 5
: 
>G46

G46
F'
?  
 3## 
 
>3#

EG46




EG46
F'

 #
1 -  
 
: >B

+?
   # 
 2C 
/5 8B! 
# ?.-%*

 ##-#  -  
#  1 1       1
4 Genome Biology Vol 3 No 7  


Figure 1
Single control normalization error values (E) were calculated as the ratio of the ratio of two control genes in two different samples (see Materials and
methods), and summarized here as cumulative distribution plots for the different tissue panels, pointing at considerable variation in housekeeping gene
expression.
Neuroblastoma
Normal pool
Leukocyte
Fibroblast
Bone marrow
Systematic error
Single control normalization error E
Cu
m
m
u
la
tiv
e
d
ist
rib
u
tio
n
(%
)
20
40
60
80
100
2 4 6 8 10 120

 3## # -

 3   >G46
H,.;
?.
#
:  >G46
(
G46
H,.;
?3 #3
1 
#

: 
>G46- 91?.
Tissue-specific housekeeping gene expression
4 
 #  -*2  %  3# #
#- - 'B  
# 
 
 -  # 1    
:.@ #*
#-

  2- #
33## 
#  -  

# 3 - 3# # #-#  %3# #  
 ># 


 
 
 
? > 
41 B?."%#-%   
#  #

 -  

  %
   3

 13#  
 .$#
 
   5  # #  %   5
 
1    -
  
  #
  1   # %   -   1

. G46- ( #3  # #    -  1- 
% 1 
3#2
9,,* 
2    13 #
  1 >
?
 #   >
?  . #-# # %
1 -%-#   
% 

3  1 %  *   2 *
  
  2


 2  % 
''8*  #-#    
   1
 

#3   2      88*  13
11  # .A  # 
-
#%% 2 %> 2



 ?
# 2 
 # > 2


 
?.
Discussion
 
:   -*2  %    
1 5  1 
 3##
1- -   1-*2   
.
 # 1#  

 
: #
#*
  .G464*
# -    -
-- 
:  .
#*
%       2    # 3 1*
 #  #-#*
 I  - '+++  #


')8)3   -
-   
: 
 # +,<      6''7. 
8 Genome Biology Vol 3 No 7  


Figure 5
Logarithmic histogram of the expression levels of 10 internal control genes determined in 13 different human tissues, normalized to the geometric mean
of 6 control genes (GAPD, HPRT1, SDHA, TBP, UBC, YWHAZ). An approximately 400-fold expression difference is apparent between the most and least
abundantly expressed gene, as well as tissue-specific differences in expression levels for particular genes (for example, B2M).
0.001
0.01
0.1
1
10
100
ACTB GAPD UBC B2M YWHAZ RPL13A SDHA HPRT1 TBP HMBS
Brain
Fetal brain
Heart
Kidney
Liver
Lung
Mammary gland
Trachea
Uterus
Leukocyte
Fibroblast
Neuroblastoma
Bone marrow
R
el
at
ive
e
xp
re
ss
io
n
le
ve
ls
(a
rbi
tra
ry
u
ni
ts
)


   ## --2 
% 16=
+*'87
#% # 
 #-##.
1
#%    # 
  -  # 2*

  
 
 3 
1    . 4#    # 1 # 3#


  #%2  #
 - 3# #
 - 3
  . 
 
#1 
# #   % 3 3# #  


1 #  
 #
-
- .   # 
  
%      
 1    # 1 %

113
  .
A #-  1-
3


-# #


-  
:. A #  
 3 %
% #  % 
: - 1 
 -# --  
:*
   B.,*  =.9*   8(<  ',<   #  
 %
3#    #3-% 1%
8,. 4#     #3 #  3  -  3
 1  -    2   

 
   %1 #  
(.+ 9.(
 # +,#* -  
: 
%  1 
 3# #
 -   

> #3?.4#  -- 3## *
2  
 1 

 *
-# -   #
 6')7.4#
* 1 %      # 1 
2  #
1 # 
#*
-3# 
  
#  
%  # #-     
   

  # #
 % #
-
  
 .A# -
# # 1 # -
-  %

3#3-
3#    # --6+7.
4# -
:% #*
       2  %    # 
-
 -3### 11#-*
  3# % # - 3
 
    1
  .-
3     )
,,,*   #   % 1
   3   =, 6'97
  -  5  
:>*
-
 
?3  %2 1;*
 8B*  6+7. 4 -#
    #  
 #3 #   %   -   
2 .4# 3 # #  12 - 
# 2
  
   # %
   

: -.
4%#2  1 # -
3# 
 
  :
3# 
  #13 # 3
*
:2  %   3-
3## #1  - .@
1 %
 #3%
 # #  - 13 #




2

2  %
-
%
 #%       #    #
-  
   5  # %   #
  . 4# 
    

     1  #       .
@#%# %3 1
 
1 
 #  # # 2     3 
-  #1   

 -
  # 2
    
 3#  -
 %  -   - 2  *
1 >1#? # - .4#  *
 %   -* 
  -     
1 
  #%133-
      
   5
   
 
#
     #   . G46#

 #

       # 1  
13#-
 5 1-
2
>#
   
3#3#-#
% 2 
     1 -
3--#%5% 3 
% ?. J-#
  
   #   
5  

  1   #  
:
 -51 % >1  - ?
-%#% .  
# *
 %   -* 
       #
%  -* 
% 
3##

  # 
 #  %
1
3- .
&%- 1 #1 
   #%
2  3-
3 1 5 
-* 1
   #%-3 %
13-#- .$ -
   -
 % *# 
 3  


   # - -  1--   
1  -#2 *
 1 %   .4# 
   #
-- - 3 2*
   # 3 * - -        #
1   -*   2   
   #
# 
- -  
 1 2  # #
2- . 
    #3 % 
 
3##    # 2     

12 - > 2




   ?
      
 # -
 
:     >  1%?. 4#
%-- 1%  #
-- -
3  
   #   1  -   
   *   
3#1
3
#    
    #%- # 3   #-# 
% 2  %
  %. 4#    
 
-%#- #-# .
co
m
m
en
t
review
s
rep
o
rts
d
ep
o
sited
research
in
teractio
n
s
in
fo
rm
atio
n
refereed
research
http://genomebiology.com/2002/3/7/research/0034.9

4#5  3###1 %#-#% *
1 
    *#
 -
2      # % -. G46

#   
     # #   %  
- 1  .

 1
# 3 * - -
 3# 


  

1#1 %-
#1--
# 
  1-    %   .
&3%
 #  -:  # 1  3#
.
    1    #   1 
-
1 %# -# 
:
2 % .4# 
  #
# # --  #-#
#        % -. 4#    %


 3#  - #      
1  
- 13   .
4# -2   13 #     

 
   
3    
-
:  #3   :*
.A -
#1 % *  2 
#   -     -3# # 3  
# #-#
 2 
3#
   
I *  

  % 3 *

  
2  #  
3##
 
 
 -.
A%3 ##%2  # *
- -
 3 

 #        # 
 -   -  
: 
 
 
3##
*
  
 #3
 *  -  3 5  1

:.4#    #3## 1
-    
:  

3#%32 %
3# # 
  5  #
  %   #  - 
#1 %%.
4#  
: 
%# 
- *
 > # 55?*
 -*  %.G46 
-
# % #1

.4%
#-* 1
  #-
-#

 #
  1-    

3
#% #-*  % #-
#    % 
: 2  % .
4# 
#32 % 3 :
 
: 
 #-

#
 
 

    1  -  >

1-
?.4# #    #
 
:  1     
-  # 
%     %. A  
 1  -   
 
% # *
 %
%
%
 -
- * -*   %   #  
- .4#   
 #
 
   % 3  
% 3# #
  1
- > 
1-
?3  *

:
 3## %  # # % 1
 
 - # %3# 1
  - 2   #        1
  .
G46#%
 ##-


  -   
 #

   
   
 1 
 
3# 5 
 
:  .
#-#1#4*
:  1 -*

%-- % 

 #1 
  %  -  ># 
G46
H,.;
?   -#

5%  #
* -  . 43  1
2  # 1 %.G46 
# %

  1  -    
 1 -
 
1 3 
 >#
B*1 *

(*1  
?
 #
4*
3#+B9 
 3 
 -

1 
 #2  1.
#  ##1:
   2  1 *    
 *
%    % % .  1*
-   5 


-#
#%1 1    *
1   
 6'+*8'7. 4# #-# %1  *
3##1:  
-## 2#
 
  -   
: # .%*
#
 #    #3 #
:1 
 # -

       -
    5%   
 
  
*

  
3##%    
4* 
:. A 
 #
#  
 1 
:-*2  %
 - 

#1:2

3#


1 -  
- 

# -- .
A  
 3  1  %   
 #
  1- -%   


   
 # 
 
1   -
5  1 
:   4* . 4#
- 1
1
-    
  # 3
  -*
2  -.4#   
 
  *
- # 1- -    1 2 
 
  
G2 
 
  1
  4*.
Materials and methods
Sample preparation
4#* 1 
 3-3 1*
-    .3
  -# !>K-?-#

 L   . 
  

10 Genome Biology Vol 3 No 7  




 #
    >#
 1
  1
 -
#
 



 -

   
 ?3 1 
#.   11 
1  31 
 
## *
% .4# 
 3  
(

 #1 -K-L  #   1 .G4611*
   
8,*
 1  3 
 #
>B*9 - ?#%  1 
  16887.
3 
 31 

 3##
-
-.4
  
 11   1
3 
  3
2 -4: >A%-?
-  #
*
L   .
Real-time RT-PCR
 

 # 

 -M
"A4*3  168B7.A1
8 -   #   
 3   3# # K'
 *  -#
 L   *
  >
-?. 4  
  3   >
%  %  
- 
? 1   # 
 - !*',,  
  >!?. G46 * 
3  # : -
#2
 *
AA %     -  #
 *
L   >A%-?
 1 53#
 *  3 >-
?  '8.( -G . 4*

 
2  >8( ?  8( - 


82M"A! !21 >'8.( ?>
  
?B,, ! 3% 
.*
 3   A A!(;,, 5
>  
?.4# -   
  ',


 %+(N9, +(N 
'(  =,N =, ./#    >*
? %     **
&  A!*B8  >-- 
 (, -  (, -?
 
*

8(- # .
   3 1% +(<. 5   3
>% '.B?>  
?  32
1*
 2    
  !   /2
 #   . 4#
     %
>1   5 ?    
 3
=<.
Single control normalization error E
G46  -% $   

 *
 4* -*
2 % 

  - 
 .
G46 % 
1   3   
  !  %
 
%
1 3-   
#
-
: 3 >/5
'?.4#    # 2  13 
 !
% 3#
:# --.
> 
  6'
7
 !
%  6'
$7
  !  %?

!%
&OO

%

%
 OO

!

!
> H'
# E
*'
  E? >'?
Internal control gene-stability measure M
G46%
1 3-  
  

 $ 
     3##    
-
8
* 
2   

'

>/58?.@
 #3 %

#-  
  # %  #


  >/5B?.
4#-* 1
 

-  ##*


 3 % 

>/59?.
> 
  6'
7  ?


E


-
8

OO

'

'

-
8

OO

8

8

...
-
8

OO

$

$

E


-
8

OO





E'$
>8?


E#(
)>

?>B?


&OOOOO

E'



 *'
>9?
Normalization of array data
1 %1 3
  6'97 3 3*
   1*
  . /-# #1:  
3 
   
 !  /2
 3  #
 >!G46;
 $*'9(
 ;)=*,
8
(=8
(9+
$8('
*D*B?.G46##1:*

  3#B(   
13 # %- % 1- %    *
 %3  . 1 5
   1*
-   #  3  . 43 
 3 # #1   


:>
 '? 

:>5: 
      
1## ?.# -- 3 1
J4 
3 #- #1 
  #> A!"/  
41 '?.
Additional data files
4# 3 2  %   %1    1*

3##%  # .
Acknowledgements
We thank H. De Preter for writing the Visual Basic application for
Microsoft Excel, G. Berx (Ghent, Belgium) for critically reading the manu-
script, and M. Vidaud (Paris, France) and E. Mensink and A. van de Locht
(Nijmegen, The Netherlands) for providing us with TBP and HMBS primer
sequences respectively, L. Nuytinck for the fibroblast RNA samples, and
G. De Vos and P. Degraeve (Ghent, Belgium) for culturing the cell lines.
K.D.P. and B.P. are supported by a grant from the FWO. N.V.R is a post-
doctoral researcher from the FWO. This study was also supported by the
Flemish Institute for the Promotion of Scientific Technological Research in
Industry (IWT), FWO-grant G.0028.00, GOA-grant 12051397 and BOF-
grants 011B4300 and 011F1200.
co
m
m
en
t
review
s
rep
o
rts
d
ep
o
sited
research
in
teractio
n
s
in
fo
rm
atio
n
refereed
research
http://genomebiology.com/2002/3/7/research/0034.11

References
1. Schena M, Shalon D, Davis RW, Brown PO: Quantitative moni-
toring of gene expression patterns with a complementary
DNA microarray. Science 1995, 270:467-470.
2. Fink L, Seeger W, Ermert L, Hanze J, Stahl U, Grimminger F,
Kummer W, Bohle RM: Real-time quantitative RT-PCR after
laser-assisted cell picking. Nat Med 1998, 4:1329-1333.
3. Heid CA, Stevens J, Livak KJ, Williams PM: Real time quantitative
PCR. Genome Res 1996, 6:986-994.
4. Higuchi R, Fockler C, Dollinger G, Watson R: Kinetic PCR analy-
sis: real-time monitoring of DNA amplification reactions.
Biotechnology 1993, 11:1026-1030.
5. Solanas M, Moral R, Escrich E: Unsuitability of using ribosomal
RNA as loading control for Northern blot analyses related
to the imbalance between messenger and ribosomal RNA
content in rat mammary tumors. Anal Biochem 2001, 288:99-
102.
6. Spanakis E: Problems related to the interpretation of autora-
diographic data on gene expression using common constitu-
tive transcripts as controls. Nucleic Acids Res 1993,
21:3809-3819.
7. Johnson ML, Redmer DA, Reynolds LP: Quantification of lane-to-
lane loading of poly(A) RNA using a biotinylated oligo(dT)
probe and chemiluminescent detection. Biotechniques 1995,
19:712-715.
8. Warner JR: The economics of ribosome biosynthesis in yeast.
Trends Biochem Sci 1999, 24:437-440.
9. Warrington JA, Nair A, Mahadevappa M, Tsyganskaya M: Compari-
son of human adult and fetal expression and identification of
535 housekeeping/maintenance genes. Physiol Genomics 2000,
2:143-147.
10. Thellin O, Zorzi W, Lakaye B, De Borman B, Coumans B, Hennen G,
Grisar T, Igout A, Heinen E: Housekeeping genes as internal
standards: use and limits. J Biotechnol 1999, 75:291-295.
11. Suzuki T, Higgins PJ, Crawford DR: Control selection for RNA
quantitation. Biotechniques 2000, 29:332-337.
12. Bustin SA: Absolute quantification of mRNA using real-time
reverse transcription polymerase chain reaction assays. J Mol
Endocrinol 2000, 25:169-193.
13. Maris JM, Matthay KK: Molecular biology of neuroblastoma. J
Clin Oncol 1999, 17:2264-2279.
14. Ross DT, Scherf U, Eisen MB, Perou CM, Rees C, Spellman P, Iyer V,
Jeffrey SS, Van de Rijn M, Waltham M, et al.: Systematic variation
in gene expression patterns in human cancer cell lines. Nat
Genet 2000, 24:227-235.
15. Quackenbush J: Computational analysis of microarray data.
Nat Rev Genet 2001, 2:418-427.
16. Hess KR, Zhang W, Baggerly KA, Stivers DN, Coombes KR:
Microarrays: handling the deluge of data and extracting reli-
able information. Trends Biotechnol 2001, 19:463-468.
17. Duggan DJ, Bittner M, Chen Y, Meltzer P, Trent JM: Expression
profiling using cDNA microarrays. Nat Genet 1999, 21:10-14.
18. Cooper MJ, Hutchins GM, Mennie RJ, Israel MA: Beta 2-
microglobulin expression in human embryonal neuroblas-
toma reflects its developmental regulation. Cancer Res 1990,
50:3694-3700.
19. Tseng GC, Oh MK, Rohlin L, Liao JC, Wong WH: Issues in cDNA
microarray analysis: quality filtering, channel normalization,
models of variations and assessment of gene effects. Nucleic
Acids Res 2001, 29:2549-2557.
20. Yang MC, Ruan QG, Yang JJ, Eckenrode S, Wu S, McIndoe RA, She
JX: A statistical method for flagging weak spots improves
normalization and ratio estimates in microarray. Physiol
Genomics 2001, 7:45-53.
21. Brown CS, Goodwin PC, Sorger PK: Image metrics in the statis-
tical analysis of DNA microarray data. Proc Natl Acad Sci USA
2001, 98:8944-8949.
22. Nuytinck L, Narcisi P, Nicholls A, Renard JP, Pope FM, De Paepe A:
Detection and characterisation of an overmodified type III
collagen by analysis of non-cutaneous connective tissues in a
patient with Ehlers-Danlos syndrome IV. J Med Genet 1992,
29:375-380.
23. Vandesompele J, De Paepe A, Speleman F: Elimination of primer-
dimer artifacts and genomic coamplification using a two-
step SYBR Green I real-time RT-PCR. Anal Biochem 2002,
303:95-98.
24. Bieche I, Laurendeau I, Tozlu S, Olivi M, Vidaud D, Lidereau R,
Vidaud M: Quantitation of MYC gene expression in sporadic
breast tumors with a real-time reverse transcription-PCR
assay. Cancer Res 1999, 59:2759-2765.
12 Genome Biology Vol 3 No 7