Oligonucleotide probe design and microarray construction
Supplementary text for: Zhigang Zhang, Ninad D. Pendse¶, Katherine N. Phillips,
James B. Cotner, Arkady Khodursky. 2008. Gene expression patterns of sulfur
deprivation in Synechocystis sp. PCC 6803. BMC Genomics, 2008.
To construct an oligonucleotide-based microarray of the Synechocystis protein coding
genes, we chose an algorithm that allowed selection of representative long
oligonucleotides with high specificity and consistency of hybridization across the set.
In the context of microarray design, specificity refers to the inability of the probe to
bind strongly to non-target sequences during the hybridization and washing. High
specificity can be achieved by avoiding probes with excessive sequence similarity to a
non-target sequence that might be present in a complex pool of cellular targets. The
hybridization consistency depends on uniformity of probe melting temperatures
across the entire collection of the probes, which can be estimated by some
computational prediction methods [1].
A freely available software, ArrayOligoSelector [2] developed by Bozdech et al [3],
was used to design gene-specific oligonucleotide probes for the entire Synechocystis
genome. Previously this program had been successfully used to design genome-wide
array elements for the human malarial parasitic protozoan Plasmodium falciparum
[3], the GC rich opportunistic pathogen Burkholderia cenocepacia [4] and the butanol
biofuel producer Clostridium acetobutylicum [5].
Because it was shown that there is a strong correlation between signal intensity and
oligo length [6], 70-nt-long oligomers were designed for the Synechocystis genome in
order to detect genes expressed at low levels. Frequently, one probe per ORF may be
sufficient to detect changes in the abundance of a transcript [4, 6, 7]. Thus, one 70-1-
mer per gene was finally designed to reduce the cost. Oligomers for every ORF were
selected on the basis of uniqueness within the genome (reduction of crosshybridization potential), avoidance of significant self-binding (secondary structure),
exclusion of low-complexity sequence (minimizing non-specific hybridization), and
balanced base composition (consistency in melting temperature). The collection of all
possible 70-mer oligonucleotides contained in the coding region of an ORF was used
for oligonucleotide selection, which was executed by employing filters for
uniqueness, self-binding and complexity, in parallel. The intersections of the set of all
oligonucleotides passing these filters were further selected for a desired GC content.
We selected oligonucleotides that ranked among the top 5% of unique or almost
unique 70-mers in the entire ORF and that were within 5 kcal/mol of a best candidate
for the ORF. Next, 33% of top-scoring 70-mers passed the self-binding and lowcomplexity filters. The GC content cut-off was set to 50%. These filters were applied
to the entire set of candidate 70-mers to obtain individual outputs. If no common
oligomers were identified, then self binding and complexity filters were relaxed until
an intersection appeared. The sequences of oligo-probes are available in a
supplementary file [see Additional file 5].
Following computational design and selection of the probes, we constructed the
microarray platform in two stages. In the first stage, we produced a 171-probe pilot
array and assessed the specificity of spotted probes along with negative control spots
by doing single-channel or dual-channel genomic DNA hybridizations. In the second
stage, we produced an oligo-array with the genome-wide coverage (representing 3064
out of 3168 protein coding genes) and evaluated its performance based on the ability
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to detect well-established environmental stress transcriptional responses by two-color
hybridization, using the hybridization conditions optimized in the first stage.
Microarray design stage 1: Pilot array design
In stage one, 158 oligonucleotide probes (about 5% of the genome-wide set)
representing well-annotated Synechocystis genes were designed. Thirteen Escherichia
coli K-12 MG1655 oligos were also designed as negative controls. The E. coli genes
used for designing control probes were selected by doing pair wise BLAST of all the
E. coli ORFs against all Synechocystis genes. With an E-value cut-off of 10, 13 E. coli
ORFs (b0215, b0273, b0605, b0814, b1078, b1732, b1887, b2798, b2827, b3701,
b3744, b4024 and b4101), which had a match in Synechocystis sequences of at most
19-nt, were selected for designing 70-mer oligos. These 171 oligomers were spotted
on glass slides in duplicate for evaluating their specificity using either Synechocystis
or E. coli genomic DNA (gDNA) hybridization. Initially, an E. coli cDNA array
hybridization protocol was used [8], but there was significant non-specific crosshybridization as reflected in the ratio of average intensities of target to non-target
probes (data not shown). Hence, the protocol was optimized by varying one parameter
(salt concentration, hybridization temperature or blocking solution) at a time to
minimize the cross-hybridization, along with a slide pre-hybridization step, as
described in Methods. Finally a significant reduction in cross-hybridization was
achieved as the specificity ratio went up from 1.5 to 8-10.
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Table 1 Statistical tests of specificity of hybridization for pilot oligonucleotide
arrays of Synechocystis.
(A) Two class t-test.
E. coli DNA
Synechocystis
DNA
Array ID
s-23
s-27
s-29
Average
s-24
s-26
s-28
Average
T statistic
4.44
5.29
6.605
6.308
-12.79
-15.1659
-9.399
-15.2287
P-value
4.07E-03
5.91E-05
6.05E-06
1.04E-05
4.23E-29
6.01E-34
5.58E-15
5.06E-31
(B) Mann-Whitney U test.
Organism
E. coli DNA
Synechocystis DNA
Z statistic
-4.12
-3.76
P-value
<0.0002
<0.0002
(C) Two class t-test on data generated by random sampling.
Organism
E. coli
Synechocystis
T statistic
-23.74
29.35
P-value
1.72E-49
2.33E-50
Subsequent to optimization of the hybridization protocol, we used several statistical
tests to assess the performance of the designed oligos in gDNA hybridization
experiments. The purpose was to determine whether the intensity of hybridization of
target probes was significantly higher than that of non-target probes. First, 6 singlechannel genomic DNA hybridizations were performed, 3 with fluorescently labelled
E. coli DNA and 3 with fluorescently labelled Synechocystis DNA. Spots with signal
to noise ratios less than two standard deviations away from the background mean
were excluded from the analysis. Resulting intensities were subjected to a parametric
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two-class t-test (Table 1A) and non-parametric Mann-Whitney U test (Table 1B). We
concluded from both tests that the intensity of hybridization of target oligo-probes
was significantly higher than that of non-target probes. Because there was an order of
magnitude difference in the sample size for target and non-target oligos, 25 random
samplings were performed to create two populations of equal sample size from the
existing populations. The two-class t-test was carried out on this dataset based on the
rank of oligo intensity (Table 1C). The average ranks of the two populations were
different (both p-values were less than 1E-48) and hence, the signals obtained by
annealing to the probes from one population had a significantly higher intensity than
the other. Figure 1 shows the distribution of mean ranks of intensities from a
randomly sampled dataset for E. coli (A) and Synechocystis (B) DNA hybridization. It
is clear that the distribution of average ranks was not overlapping and therefore, the
oligos had high selectivity. Finally, two-channel comparative hybridizations (one
channel for E. coli DNA and the other Synechocystis DNA) were done and the
log2(ratio) for each oligo was calculated. As can be seen in Figure 1C, there was a
clear segregation between E. coli and Synechocystis oligo-probes. Based on these
results, the oligo design was deemed to be specific, with no or minimal crosshybridization.
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Figure 1 Pilot array design to assess probe specificity.
Distribution of mean ranks from randomly generated datasets for single-channel
hybridizations with E. coli (A) and Synechocystis (B) genomic DNA. (C) Twochannel DNA hybridization demonstrates clear separation of the distributions of
signals coming from E. coli and Synechocystis probes based on log2(ratio).
Microarray design stage 2: Genome-wide array construction
Because sub-genome arrays performed well in our pilot experiments, we concluded
that the oligo selection criteria could be used for a large-scale design. We attempted to
design 70-mers for all ORFs in the Synechocystis genome. Due to significant amount
of sequence similarity between some ORFs, we were able to select representative
oligonucleotides for 3064 out of 3168 protein coding genes (96.7% coverage). The
-6-
excluded ORFs (104 total) corresponded to duplicate genes and genes coding for
transposases and some other hypothetical proteins [see Additional file 5]. A stringent
criterion of no more than 30% identity with the non-target sequence was applied to all
oligos. Oligo-probes were also designed for 3 rRNA genes in Synechocystis. The
layout of the spots was made in such a way as to have 4 control spots, 3 positive and 1
negative, in each of the 16 blocks of the microarray spotted using an in-house robotic
printer. A representative genome-wide microarray image is shown in Figure 2.
Figure 2 Genome-wide Synechocystis oligonucleotide (70-mer) DNA microarray.
The artificial 16-bit TIFF image of a representative two-color hybridization.
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Next, we assessed the performance of the genome-wide array by examining wellknown transcriptional responses. First, cells from the mid-exponential growth phase
were heat shocked for 1 hour by increasing incubator temperature from 26oC to 40 oC.
Temperature up-shift usually triggers a heat-shock response [9], which at least in part
can be characterized by transcriptional induction of genes encoding for molecular
chaperones and proteases. By examining transcriptional responses in 3 independent
biological cultures that were shifted to a higher temperature, we identified five known
molecular chaperone genes, including groEL, groEL-2, groES, hspA, htpG, among the
top 20 induced genes (mean fold change > 1.5, t-test P < 0.05). Induction of those
transcripts has been also reported in the studies done by other groups which used
cDNA microarrays [10-12]. Second, we tested the array under the condition of salt
stress, by subjecting a bacterial culture for 35 min to NaCl at a final concentration of
0.5M. The top 100 induced genes (mean fold change > 1.5, P < 0.05) included genes
encoding heat shock proteins (groEL-2, hspA), proteases (htrA, clpB, ctpB, ftsH),
glucosylglycerol synthetase(ggpS), ribosomal proteins (rps21, rpl3), a sigma factor
(rpoD), and a high-light-inducible protein (hliA). All of them were reported to be
inducible by high salt. 11 common genes reported in three different studies were also
identified in this study [13-15]. Since the genome-wide original intensity data
reported in the literatures are not accessible, more systematic comparisons (overall
scatter plot correlation between platforms, overlapping between differentially
expressed genes identified by both platforms, etc) cannot be performed.
We also compared the magnitude of change of top ranked common differentially
expressed genes identified between this study and literature reports, as shown in Table
2. Literature reports using cDNA microarray platform consistently produced larger
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values of transcriptional change，from 2 to 30 fold higher than our custom designed
long oligonucleotide microarray. The experimental conditions had no significant
differences. The main reasons for the magnitude differences could be attributed to
differences in array cross-hybridization and data processing. cDNA microarrays use
longer and double-strand DNA fragments, which are more prone to non-specific
hybridization and can cross-hybridize more easily to similar sequences. This may
result in higher background signal than that of oligonucleotide microarrays. On the
other hand, those literature reports used local background-subtracted intensities for
data normalization. However, there is no theoretical basis for background subtraction.
Moreover this procedure may remove some spots with lower spot intensities than
background from follow-up analysis, including significantly differentially expressed
genes. Based on our own experiences on cDNA microarray data analysis, we prefer to
use non-background subtracted intensity data for microarray data preprocessing.
Though the magnitude of a particular expression ratio can significantly differ between
our oligonucleotide microarray and reported cDNA microarray platform, the relative
expression, in terms of rank or “direction” of expression change, appears to be well
correlated (Table 2).
Table 2 Comparison of magnitude of induction folds of common genes between
this study and literature reports. (A) Heat shock. Experimental conditions: this
study, 26 to 40°C for 60min; Li et al 2004, 35 to 45°C for 15 min; Suzuki et al
2004&2005, 34 to 44°C for 60min. (B) Salt stress. Experimental conditions: this
study, 0.5 M NaCl for 35 min; Kanesaki et al 2002, 0.5 M NaCl for 30 min; Marin et
al 2004, 0.684M NaCl for 0.2-24hr; Shoumskaya et al 2005, 0.5 M NaCl for 20 min.
Data were presented in mean.
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(A)
Locus(gene)
slr2075 (groES)
slr2076 (groEL)
sll1514 (hspA)
sll0430(htpG)
sll0416 (groEL-2)
This study
4.4
2.7
1.7
1.6
1.5
Li et al 2004
8.7
8.2
5.1
9.9
11.6
Suzuki et al 2005&2006
15.5
16.4
21.6
3.7
6.5
(B)
Locus(gene)
This study
sll1863
sll1862
sll1514(hspA)
sll0528
sll1566(ggpS)
slr1687
ssr2595
slr1544
slr0967
ssl2542(hliA)
sll1085(glpD)
13.3
7.3
5.1
3.7
3.3
3.0
2.7
2.4
2.0
1.9
1.8
Kanesaki et al
2002
52.7
93.8
56.2
40.0
10.7
9.4
13.4
20.3
16.0
5.0
11.8
Marin et al
2004
265.2
231.5
23.9
50.3
7.6
3.1
3.0
2.1
25.3
2.1
3.5
Shoumskaya et al
2005
106.5
152.4
49.7
74.4
13.2
16.0
15.1
23.2
32.3
9.8
9.7
There are discrepancies among the reports on agreement of inter-lab and interplatform comparisons of DNA microarray data. Some studies suggest significant
disagreement between platforms [16-21], and others show general agreement [22-35].
Systematic analyses indicated that all three platforms (cDNA, long or short
oligonucleotide) can give similar and reproducible results if the criterion is the
direction of change in gene expression (up-regulation or down-regulation) and
minimal emphasis is placed on the magnitude of change [35]. This is also consistent
with the notion that microarray experiments are superior in identification of
regulatory pattern in genome-wide gene expression rather than giving the researcher
quantitative expression values for individual genes [36].
In summary, the extent of concordance between the sets of genes identified in our
study and the earlier studies employing PCR-based arrays was consistent with what
can be expected from microarray results obtained on different platforms and by
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different groups. Based on these results, we concluded that the genome-wide array
was ready for studying transcriptional responses of the Synechocystis genome to
poorly understood environmental challenges.
Methods
Probe design and microarray production
The 70-nt oligomers were designed using ArrayOligoSelector [2] as detailed in the
text. The oligos were synthesized by Invitrogen Corp. (Carlsbad, CA, USA),
resuspended in 5µl of 3×SSC to a final concentration of 66.7 pmol/µl and spotted
onto poly-L-lysine coated microscopic glass slides using OmniGrid Microarray
printer (GeneMachines, San Carlos, CA, USA), as described previously [37]. All
oligo sequences are provided [see Additional file 5].
Genomic DNA preparation and fluorescent labelling
Genomic DNA was isolated from stationary phase cultures of E. coli K-12 MG1655
and Synechocystis sp. PCC 6803, respectively, using standard phenol-chloroform
extraction method [38, 39] with minor modifications. Sucrose was supplemented at a
final concentration of 0.5M to 1×TE buffer (10mM Tris, 1mM EDTA, pH 8) to
efficiently disrupt Synechocystis cell envelop. Purified genomic DNA was sheared by
sonication, yielding 300-1000bp long DNA fragments for direct labelling using
Klenow fragment of DNA polymerase I. The labelling reaction consisted of 2-5µg of
genomic DNA, 5 μg of random hexamer (pdN6), 1.5 μl of dNTP mix (0.5 mM dATP,
0.5 mM dCTP, 0.5 mM dGTP and 0.2 mM dTTP), 0.2 mM Cy3- or Cy5-dUTP (GE
Healthcare, Piscataway, NJ, USA), 1× Klenow buffer and 6-10 units of Klenow.
Reactions were incubated at 37°C for 2 hrs.
- 11 -
Heat and salt treatment experiment
Synechocystis sp. PCC 6803 was grown in BG-11 medium at 26oC supplemented with
8mM NaHCO3 and buffered with 10mM HEPES-NaOH to a final pH of 7.4. The cells
were grown in 0.5L conical flask with continuous shaking exposed to a full spectrum
luminescent lamp with a photon flux density of 25µmol photons m-2 s-1 in 14:10 light
dark cycles, continuously supplied with sterile air containing 1% (v/v) CO2. For heat
shock experiment, 50ml of mid-exponentially growing cells (OD730nm = 0.6) were
collected as control and then heat shock response was induced by increasing the
incubator temperature to 40oC. Cells were harvested after 60 min at the elevated
temperature. For salt stress experiment, 5M NaCl was added to the mid-exponential
growth phase cell culture to a final NaCl concentration of 0.5M. The cell samples
were taken just before (control) and 35min after NaCl addition. All the experiments
were done in biological triplicates. Total RNA preparation, cDNA fluorescent
labelling and microarray hybridization were the same as presented in the Method
section of the main text. The microarray data were processed and analyzed as
described in the supplementary text above.
References
1.
2.
3.
4.
5.
SantaLucia J, Jr.: A unified view of polymer, dumbbell, and oligonucleotide
DNA nearest-neighbor thermodynamics. Proc Natl Acad Sci USA 1998,
95(4):1460-1465.
ArrayOligoSelector: [http://arrayoligosel.sourceforge.net/]
Bozdech Z, Zhu J, Joachimiak M, Cohen F, Pulliam B, DeRisi J: Expression
profiling of the schizont and trophozoite stages of Plasmodium falciparum
with a long-oligonucleotide microarray. Genome Biol 2003, 4(2):R9.
Leiske D, Karimpour-Fard A, Hume P, Fairbanks B, Gill R: A comparison of
alternative 60-mer probe designs in an in-situ synthesized oligonucleotide
microarray. BMC Genomics 2006, 7(1):72.
Paredes CJ, Senger RS, Spath IS, Borden JR, Sillers R, Papoutsakis ET: A
general framework for designing and validating oligomer-based DNA
- 12 -
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
microarrays and its application to Clostridium acetobutylicum. Appl Envir
Microbiol 2007, 73:4631-4638.
Hughes TR, Mao M, Jones AR, Burchard J, Marton MJ, Shannon KW,
Lefkowitz SM, Ziman M, Schelter JM, Meyer MR et al: Expression profiling
using microarrays fabricated by an ink-jet oligonucleotide synthesizer.
Nat Biotechnol 2001, 19(4):342-347.
Relogio A, Schwager C, Richter A, Ansorge W, Valcarcel J: Optimization of
oligonucleotide-based DNA microarrays. Nucleic Acids Res 2002,
30(11):e51.
Khodursky AB, Bernstein JA, Peter BJ, Rhodius V, Wendisch VF, Zimmer
DP: Escherichia coli spotted double-strand DNA microarrays: RNA
extraction, labeling, hybridization, quality control, and data management.
Methods Mol Biol 2003, 224:61-78.
Segal GIL, Ron EZ: Regulation of heat-shock response in bacteria. Annals
N Y Acad Sci 1998, 851(1):147-151.
Li H, Singh AK, McIntyre LM, Sherman LA: Differential gene expression in
response to hydrogen peroxide and the putative PerR regulon of
Synechocystis sp. strain PCC 6803. J Bacteriol 2004, 186(11):3331-3345.
Suzuki I, Kanesaki Y, Hayashi H, Hall JJ, Simon WJ, Slabas AR, Murata N:
The histidine kinase Hik34 is involved in thermotolerance by regulating
the expression of heat shock genes in Synechocystis. Plant Physiol 2005,
138(3):1409-1421.
Suzuki I, Simon WJ, Slabas AR: The heat shock response of Synechocystis
sp. PCC 6803 analysed by transcriptomics and proteomics. J Exp Bot
2006, 57(7):1573-1578.
Kanesaki Y, Suzuki I, Allakhverdiev SI, Mikami K, Murata N: Salt stress and
hyperosmotic stress regulate the expression of different sets of genes in
Synechocystis sp. PCC 6803. Biochem Biophys Res Commun 2002,
290(1):339-348.
Marin K, Kanesaki Y, Los DA, Murata N, Suzuki I, Hagemann M: Gene
Expression Profiling Reflects Physiological Processes in Salt Acclimation
of Synechocystis sp. Strain PCC 6803. Plant Physiol 2004, 136(2):32903300.
Shoumskaya MA, Paithoonrangsarid K, Kanesaki Y, Los DA, Zinchenko VV,
Tanticharoen M, Suzuki I, Murata N: Identical Hik-Rre systems are
involved in perception and transduction of salt signals and hyperosmotic
signals but regulate the expression of individual genes to different extents
in Synechocystis. J Biol Chem 2005, 280(22):21531-21538.
Kothapalli R, Yoder S, Mane S, Loughran T: Microarray results: how
accurate are they? BMC Bioinformatics 2002, 3(1):22.
Kuo WP, Jenssen T-K, Butte AJ, Ohno-Machado L, Kohane IS: Analysis of
matched mRNA measurements from two different microarray
technologies. Bioinformatics 2002, 18(3):405-412.
Li J, Pankratz M, Johnson JA: Differential gene expression patterns
revealed by oligonucleotide versus long cDNA arrays. Toxicol Sci 2002,
69(2):383-390.
Lenburg ME, Liou LS, Gerry NP, Frampton GM, Cohen HT, Christman MF:
Previously unidentified changes in renal cell carcinoma gene expression
identified by parametric analysis of microarray data. BMC Cancer 2003,
3:31.
- 13 -
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
Tan PK, Downey TJ, Spitznagel EL, Jr., Xu P, Fu D, Dimitrov DS, Lempicki
RA, Raaka BM, Cam MC: Evaluation of gene expression measurements
from commercial microarray platforms. Nucl Acids Res 2003, 31(19):56765684.
Mah N, Thelin A, Lu T, Nikolaus S, Kuhbacher T, Gurbuz Y, Eickhoff H,
Kloppel G, Lehrach H, Mellgard B et al: A comparison of oligonucleotide
and cDNA-based microarray systems. Physiol Genomics 2004, 16:361 370.
Kane MD, Jatkoe TA, Stumpf CR, Lu J, Thomas JD, Madore SJ: Assessment
of the sensitivity and specificity of oligonucleotide (50mer) microarrays.
Nucl Acids Res 2000, 28(22):4552-4557.
Taniguchi M, Miura K, Iwao H, Yamanaka S: Quantitative assessment of
DNA microarrays - Comparison with Northern blot analyses. Genomics
2001, 71:34 - 39.
Guckenberger M, Kurz S, Aepinus C, Theiss S, Haller S, Leimbach T,
Panzner U, Weber J, Paul H, Unkmeir A et al: Analysis of heat shock
response of Neisseria meningitides with cDNA- and oligonucleotide-based
DNA microarrays. J Bacteriol 2002, 184:2546 - 2551.
Yuen T, Wurmbach E, Pfeffer RL, Ebersole BJ, Sealfon SC: Accuracy and
calibration of commercial oligonucleotide and custom cDNA microarrays.
Nucleic Acids Res 2002, 30:e48.
Barczak A, Rodriguez MW, Hanspers K, Koth LL, Tai YC, Bolstad BM,
Speed TP, Erle DJ: Spotted long oligonucleotide arrays for human gene
expression analysis. Genome Res 2003, 13:1775 - 1785.
Wang HY, Malek RL, Kwitek AE, Greene AS, Luu TV, Behbahani B, Frank
B, Quackenbush J, Lee NH: Assessing unmodified 70-mer oligonucleotide
probe performance on glass-slide microarrays. Genome Biol 2003, 4:R5.
Bloom G, Yang IV, Boulware D, Kwong KY, Coppola D, Eschrich S,
Quackenbush J, Yeatman TJ: Multi-platform, multi-site, microarray-based
human tumor classification. Am J Pathol 2004, 164:9 - 16.
Jarvinen AK, Hautaniemi S, Edgren H, Auvinen P, Saarela J, Kallioniemi OP, Monni O: Are data from different gene expression platforms
comparable? Genomics 2004, 83:1164 - 1168.
Lee HS, Wang J, Tian L, Jiang H, Black MA, Madlung A, Watson B, Lukens
L, Pires JC, Wang JJ et al: Sensitivity of 70-mer oligonucleotides and
cDNAs for microarray analysis of gene expression in Arabidopsis and its
related species. Plant Biotech J 2004, 2:45 - 57.
Parmigiani G, Garrett-Mayer ES, Anbazhagan, Gabrielson E: A cross-study
comparison of gene expression studies for the molecular classification of
lung cancer. Clin Cancer Res 2004, 10:2922 - 2927.
Thompson KL, Afshari CA, Amin RP, Bertram TA, Car B, Cunningham M,
Kind C, Kramer JA, Lawton M, Mirsky M et al: Identification of platformindependent gene expression markers of cisplatin nephrotoxicity. Environ
Health Perspect 2004, 112:488 - 494.
Ulrich RG, Rockett JC, Gibson GG, Pettit SD: Overview of an
interlaboratory collaboration on evaluating the effects of model
hepatotoxicants on hepatic gene expression. Environ Health Perspect 2004,
112:423 - 427.
- 14 -
34.
35.
36.
37.
38.
39.
Wang H, He X, Band M, Wilson C, Liu L: A study of inter-lab and interplatform agreement of DNA microarray data. BMC Genomics 2005,
6(1):71.
Petersen D, Chandramouli GVR, Geoghegan J, Hilburn J, Paarlberg J, Kim C,
Munroe D, Gangi L, Han J, Puri R et al: Three microarray platforms: an
analysis of their concordance in profiling gene expression. BMC Genomics
2005, 6(1):63.
Dharmadi Y, Gonzalez R: DNA microarrays: experimental Issues, data
Analysis, and application to bacterial systems. Biotechnol Prog 2004,
20(5):1309-1324.
Eisen MB, Brown PO: DNA arrays for analysis of gene expression.
Methods Enzymol 1999, 303:179-205.
Golden SS, Brusslan J, Haselkorn R: Genetic engineering of the
cyanobacterial chromosome. Methods Enzymol 1987(153):215-231.
Sambrook J, Russell DW: Molecular Cloning: A Laboratory Manual.,
Third edn. Cold Spring Harbor, NY: CHSL Press; 2001.
- 15 -