Group Title: Plant Methods 2008, 4:13
Title: Development and evaluation of a high-throughput, low-cost genotyping platform based on oligonucleotide microarrays in rice
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Title: Development and evaluation of a high-throughput, low-cost genotyping platform based on oligonucleotide microarrays in rice
Series Title: Plant Methods 2008, 4:13
Physical Description: Archival
Creator: Edwards JD
Janda J
Sweeney MT
Gaikwad AB
Liu B
Leung H
Galbraith DW
Publication Date: 39597
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Source Institution: University of Florida
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Plant Methods BioMed


Development and evaluation of a high-throughput, low-cost
genotyping platform based on oligonucleotide microarrays in rice
Jeremy D Edwards* Jaroslav Janda2, Megan T Sweeney*2,
Ambika B Gaikwad3, Bin Liu4, Hei Leung5 and David W Galbraith2

Address: 'University of Florida, Gulf Coast Research & Education Center, Wimauma FL, 33598, USA, 2University of Arizona, Department of Plant
Sciences and Bio5 Institute for Collaborative Bioresearch, Tucson AZ, 85721, USA, 3National Research Centre on DNA Fingerprinting, National
Bureau of Plant Genetic Resources, New Delhi, India, 4Guangdong Academy of Agricultural Sciences (GDAAS), Guangdong, Pr China and
5International Rice Research Institute (IRRI), Los Banos, The Philippines
Email: Jeremy D Edwards*; Jaroslav Janda; Megan T Sweeney*;
Ambika B Gaikwad; Bin Liu; Hei Leung;
David W Galbraith
* Corresponding authors

Published: 29 May 2008
Plant Methods 2008, 4:13 doi:10.1 186/1746-4811-4-13

Received: 2 April 2008
Accepted: 29 May 2008

This article is available from:
2008 Edwards et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background: We report the development of a microarray platform for rapid and cost-effective
genetic mapping, and its evaluation using rice as a model. In contrast to methods employing whole-
genome tiling microarrays for genotyping, our method is based on low-cost spotted microarray
production, focusing only on known polymorphic features.
Results: We have produced a genotyping microarray for rice, comprising 880 single feature
polymorphism (SFP) elements derived from insertions/deletions identified by aligning genomic
sequences of the japonica cultivar Nipponbare and the indica cultivar 93-11. The SFPs were
experimentally verified by hybridization with labeled genomic DNA prepared from the two
cultivars. Using the genotyping microarrays, we found high levels of polymorphism across diverse
rice accessions, and were able to classify all five subpopulations of rice with high bootstrap support.
The microarrays were used for mapping of a gene conferring resistance to Magnaporthe grisea, the
causative organism of rice blast disease, by quantitative genotyping of samples from a recombinant
inbred line population pooled by phenotype.
Conclusion: We anticipate this microarray-based genotyping platform, based on its low cost-per-
sample, to be particularly useful in applications requiring whole-genome molecular marker
coverage across large numbers of individuals.

Considerable interest exists in the ability to determine
genotypes within species in a cost-effective manner. Cost-
effectiveness is principally determined by desired out-
come: when the outcome is a complete genotypic descrip-
tion of a single individual (for example a human patient),

the cost is largely defined by healthcare economics, and is
the driving force behind initiatives to minimize the whole
genome costs of sequencing [1]. For outcomes in the agri-
cultural sector, for example ones leading to identification
of genes responsible for desired agronomic traits, geno-
typing is applied to large populations rather than single

Page 1 of 12
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individuals, which considerably changes the economic
considerations. Moreover, since downstream gene map-
ping and identification technologies are increasingly well-
established for different crop species [2], the required res-
olution of such genotyping platforms need not approach
the single-nucleotide level provided by whole genome
sequencing. Consequently, economic considerations and
practical applications of a genotyping technology are
driven largely by cost-per-individual rather than cost-per-

Microarray-based technologies for genotyping have
become increasingly popular since they offer an assay that
is highly multiplexed, and this was immediately recog-
nized as providing a low cost per data point [3]. One of
the earliest reports of microarray-based genotyping
employed high density whole-genome tiling arrays, pro-
duced by photolithographic synthesis (Affymetrix, Santa
Clara, CA), for the simultaneous discovery and assay of
DNA polymorphisms in yeast. In genotyping assays based
on microarrays, allelic variations are detected as differen-
tial hybridization of labeled genomic DNA to individual
probes, or sets of probes, covering identifiable genomic
locations. Using this approach, a large number of single
feature polymorphisms (SFPs) were identified between
two laboratory strains of yeast [4]. In this case, 3,714
markers were identified using microarrays which com-
prised 157,112 overlapping 25-mers spanning all anno-
tated Saccharomyces cerevisiae open reading frames [5]. For
the larger and more complex Arabidopsis genome, tiling
arrays were not available, and hence the first experiments
involved hybridization of labeled genomic DNA using
Affymetrix AtGenomel GeneChips based on available,
expression-based annotation for open reading frames
(ORFs). Despite this ORF-based focus, nearly 4,000 SFPs
were identified between the Columbia (Col) and Lands-
berg erecta (Ler) accessions [6]. In a subsequent study,
more than 8000 SFPs were identified using the ATH1
GeneChip comprising 22,500 probe sets representing
approximately 24,000 genes [7].

High density microarray platforms of this type provide a
very large amount of information from single individuals,
and therefore are ideally suited for polymorphism discov-
ery [8] or for genome-wide association studies [9,10].
However, for genotyping populations, the economic util-
ity of microarray genotyping platforms is a function not
simply of the multiplexing level, but also of the costs asso-
ciated with processing each sample [11]. Affymetrix Gene-
chips have the conspicuous disadvantage of a high cost of
production and hybridization per array, and this limits
their use in situations requiring the genotyping of large
numbers of individuals, such as in plant breeding. In con-
trast, the production of microarray slides through robotic
printing of array elements is relatively inexpensive

[12,13]. For microarrays of this type, the array elements
(probes) are either PCR amplicons [14], or synthesized
single-stranded oligonucleotides [15]. Since very little
DNA is needed for printing each element, beyond the ini-
tial cost of production, the cost per element becomes van-
ishingly small. A further cost-savings is achieved since the
microarrays are conventionally hybridized to mixed pairs
of nucleic acid targets, separately labeled with different
fluorochromes, rather than using one target per hybridiza-
tion as done with Affymetrix Genechips.

Diversity array technology (DArT) is a modification of the
amplified fragment length polymorphism (AFLP) proce-
dure using a microarray platform [16-18]. In DArT, a pool
of DNA fragments is produced from a subset of the
genome by restriction enzyme digestion of genomic DNA
followed by ligation of adaptors and PCR amplification
with adaptor specific primers. Fragments from this pool of
DNA are cloned and spotted on a microarray. Pools of tar-
get DNA are similarly generated from other samples, fluo-
rescently labeled, and hybridized to the arrays. The assay
reveals whether the specific cloned DNA fragments are
present in the queried sample. An advantage of the DArT
technology is that prior genome sequence information is
not required; therefore it can be applied to a large range of
species. A disadvantage is that, similarly to AFLP, the dif-
ferential PCR amplification of specific fragments may vary
between experiments depending on PCR conditions.
Another disadvantage is that the sequence and precise
genomic location of the cloned fragments is not known.
Therefore, with DArT, it is difficult to target specific genes
or genomic regions with higher densities of markers.

Here we describe and validate a method for cost-effective
genotyping using printed microarrays comprising single-
stranded oligonucleotide array elements. The microarrays
were designed to recognize known polymorphic
sequences. Each oligonucleotide probe corresponds to an
insertion/deletion (indel) polymorphism (i.e. a SFP) dis-
covered through the alignment of whole genome
sequences. The DNA sequences used as probes were
selected for uniqueness, and to have a uniform melting
temperature, and a similar length (approximately 70
nucleotides), to ensure specificity of hybridization. Rice
(Oryza sativa) was selected, because of the availability of
whole genome sequences for the highly divergent japonica
(International Rice Genome Sequencing Project http:// and indica [19] cultivars. We
recognized that it should be relatively straightforward to
employ genomic sequence alignment to identify poly-
morphisms. Further, rice has abundant mapping popula-
tions and germplasm collections to which the genotyping
technology can be applied. Finally, rice is considered
world-wide the most important agricultural crop, because
it provides approximately 23% of the caloric requirements

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Plant Methods 2008, 4:13 /1/13


10 *








Figure I
Positions of SFP markers on the rice pseudomolecule
map. SFPs with oligonucleotide sequences complementary
to Nipponbare are shown as black and those complementary
to 93-1 I are shown in grey. SFPs confirmed by PCR are
shown in blue (Nipponbare) and red (93-1 I). The positions
of the centromeres are indicated with an "X".

of humans and up to 60% of the calories in countries that
rely on rice as the main staple [20]. Because most of the
rice improvement efforts occur in developing countries; a
low-cost and robust method would be particularly impor-
tant for breeding institutions with modest levels of
research infrastructure.

This low-cost, focused method of genotyping, using
printed long-oligonucleotide microarrays, will be particu-
larly useful for applications that require high-density
molecular marker coverage of entire genomes for large
numbers of samples. Such applications include quantita-
tive trait locus (QTL) mapping, genetic diversity and pop-
ulation structure studies, association mapping, molecular
breeding, polymorphism surveys, and marker assisted
selection. In this study, we describe the development and
use and validation of the genotyping microarrays, and
their utilization in the assessment of the levels of poly-
morphism and genetic relationships within a collection of
diverse rice accessions, and to map a major gene confer-
ring resistance to the rice blast pathogen (.\ 1. pI;l., ip, rbi.' gri-
sea) in a segregating recombinant inbred line (RIL)
population. Finally, since this method of genotyping is
general in scope and can be implemented in other species,

5 6 7 8 9 10 11

S s *
i :

SJaponica-derived probes :

Indica-denved probes
e I ; $


Japonica derived probes
Indica-denved probes
Japonica derived probes
with pnmers
Indica-denved probes
with pnmers

Page 3 of 12
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provided that sufficient genomic sequences from multiple
individuals are available for the identification ofSFPs, we
describe a bioinformatics pipeline that has been devel-
oped for this purpose.

Polymorphism discovery
To create probes that can detect the presence or absence of
indel sequences in a genomic DNA sample, the probes
need to be complementary to a unique single copy
sequence. To identify suitable indels, the genome
sequences of the japonica rice cultivar Nipponbare and the
indica cultivar 93-11 were aligned. Whole-genome align-
ments were done using MUMmer and NUCmer 3.18 [21-
23]. The indel sequences were masked for simple repeats
and for known rice repetitive elements [24]. Indels with at
least 29 nucleotides of unique sequence were considered
for probe design. The alignment of cv. Nipponbare and cv.
93-11 genomic sequences revealed 880 indel loci where
oligonucleotide probes could be designed with specifica-
tions suitable for hybridization (Additional File 1). The
indels used for probe design ranged from 29 to 426 nude-
otides in length, with an average indel size of 76 nucle-
otides. Of the 880 probes designed, 423 are
complementary to the Nipponbare allele and 457 are
complementary to the 93-11 allele. The order and posi-
tions of the resulting SFP markers was determined based
their positions in the cv. Nipponbare pseudomolecule
assembly (Figure 1). The median distance between SFP
markers is 234 kilobases, and the largest gaps occur
around the locations of the centromeres [25] and in peri-
centromeric regions.

Experimental verification of SFPs
To validate the genotyping protocol and the computation-
ally predicted SFPs, we hybridized Cy5 and Cy3 labeled
genomic DNA of cv. Nipponbare and cv. 93-11 to four
microarrays, with each of the probes printed in triplicate.
To be useful as molecular markers, the SPFs should be reli-
able and have a fold change between samples that is read-
ily detectable. At a p-value of 0.05 or less, 676 probes
(76.8%) had significant color-ratio fold changes in the
predicted direction, and only four probes with fold
changes opposite to that predicted (Table 2, Figure 2). We
designed primers around the probes which showed fold
changes in the opposite direction and were able to show
that the predicted indel was present in each case. Of the
880 probes, 115 were unusable because they were consid-
ered to be "not found" based on more than 50% of the
spots having a signal to noise ratio < 1 across all replicates.
A further 19 probes were unusable because the signal
intensities were saturated in more than 50% of the spots
across replicates. The probes considered as being both
found and unsaturated had a mean GC content of 46.8%.
The probes considered saturated had a significantly higher

Plant Methods 2008, 4:13

Table I: Average pairwise percent of polymorphic markers
between accessions belonging to the five rice subpopulations.
The percent of polymorphic markers is calculated using four
accessions per sub-population.


Temperate Japonica X Temperate Japonica
Temperate Japonica X Tropical japonica
Temperate Japonica X Aromatic
Temperate Japonica X Aus
Temperate Japonica X Indica
Tropical japonica X Tropical Japonica
Tropical japonica X Aromatic
Tropical Japonica X Aus
Tropical japonica X Indica
Aromatic X Aromatic
Aromatic X Aus
Aromatic X Indica
Aus X Aus
Aus X Indica
Indica X Indica

Mean Polymorphism


(p-value= 1.86E-07) GC content of53.6% and the probes
considered not found had a significantly lower (p-value =
6.57E-09) GC content of 43.6%. The SPFs were also vali-
dated on slides configured in the 24-plex format using the
restriction-ligation labeling procedure. Using this
method, across eight arrays, 30 probes were considered to
be not found, and 35 were considered to be saturated.

To determine if the SFPs identified in this study could be
used as individual PCR based markers, we attempted to
design primers surrounding a selected subset (72) of the
indels, 36 having deletions in Nipponbare and 36 having
deletions in 93-11 (Figure 1, Additional File 1). Two of
SFPs were located in highly repetitive regions and no
unique primer sequences could be created to amplify only
these loci. One SFP was surrounded by sequence that was
highly diverged between Nipponbare and 93-11 such that
no primers complementary to sequences conserved
between Nipponbare and 93-11 could be created. We suc-
cessfully designed primers surrounding the remaining 69
SFPs and, using these primers on Nipponbare and 93-11
genomic DNA, demonstrated that these loci could be
individually assayed using PCR. This enables researchers
to assay population polymorphisms more efficiently,
either using a single hybridization to the genotyping
microarray to define the polymorphic markers, and then
employing only these markers for PCR-based analyses of
populations, or screening large fine-mapping populations
with only those markers flanking a QTL previously identi-
fied by microarray genotyping. Several of the amplicons
showed a larger difference in allele size than predicted. We
determined that this was due to the presence of highly
repetitive sequence within the indel which was masked
during the SFP discovery process.

Polymorphisms within 0. sativa
To be generally useful genetic markers, the SFPs between
Nipponbare and 93-11 should be polymorphic between
other 0. sativa varieties. The genotyping microarrays were
used to assess the polymorphisms across 20 diverse O.
sativa varieties representing five sub-populations as deter-
mined through STRUCTURE [26] analysis with 169 mic-
rosatellite markers [27]. The genotype scores are listed in
Additional File 2. Average levels of polymorphism for
pairs of varieties were calculated within and between sub-
populations (Table 1). The highest levels of polymor-
phism (66.2%) were found between the temperate japonica
and indica sub-populations. This is expected, given that
the sequenced varieties used for SFP discovery, Nippon-
bare and 93-11, belong to the temperate japonica and indica
sub-populations respectively. The lowest level of poly-
morphism was within the temperate japonica sub-popula-
tion (10.4%) which is also the least diverse sub-
population according to microsatellite markers [27].
Using the SFP genotype data, a neighbor-joining tree was
constructed to examine the genetic relationships between
the five subpopulations (Figure 3). The relationships
according to the SFP analysis are concordant with previ-
ous studies [27-30] with extremely high bootstrap sup-
port. Model-based clustering using STRUCTURE was used
to calculate site-by-site probabilities of sub-population
origin of alleles across the twelve chromosomes for each
of the 20 0. sativa varieties (Figure 4). The clustering using
the SFP data is consistent with the clustering using micro-
satellite markers. Further, the use of high density SFP
markers resolves large blocks of chromosomes with ances-
try that differs from the overall sub-population assign-
ment of the individuals.

Bulked Segregant Analysis with SFPs
The SFP genotyping microarrays were used in a bulk seg-
regant analysis [31] experiment with a RIL population seg-
regating for resistance to a single isolate (IsoIV) of rice
blast disease. Pools of 73 resistant and 73 susceptible lines
were hybridized to six slides using a balanced dye-swap
design. SFPs that are linked to the gene(s) conferring
resistance should display significant differences in color
ratios reflecting differences in allele frequencies between
the two pools. SFPs that are unlinked should have bal-
anced color ratios. The ANOVA method was used to calcu-
late p-values for each SFP marker. The log transformed
fold changes of the SFPs and the SFPs positions were then
plotted on the pseudomolecule assembly (Figure 5). In
this figure, we employed the convention that the p-values
were plotted in a positive direction if the direction of the
ratio signified greater representation of the SHZ (resistant
parent) in the resistant pool, and in the negative direction
for SFPs signifying greater representation of the LTH (sus-
ceptible parent) alleles in the resistant pool. A cluster of
SFPs with significant p-values was found on chromosome

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Plant Methods 2008, 4:13

1 E-08

1 E-03 -- "---
1 E-02 *
1 E-01
A 9311
1 E+00--
1 -08 -06 -04 -02 0 02 04 06 08 1
Log-fold change

Figure 2
A volcano plot comparing Nipponbare and 93-1 I
genomic DNA hybridizations with the 880 SFPs. SFPs
with oligonucleotide sequences complementary to Nippon-
bare are shown as black and those complementary to 93-1 I
are shown as grey.

12, indicating that SHZ alleles in this chromosomal
region may confer resistance. The top 10 most significant
SFPs all fall within the same region of chromosome 12,
and are ordered by most to least significant (Table 4) with
Benjamini and Hochberg [32] adjusted p-values. The gene
conferring resistance to the blast IsolV isolate designated
as Pi-GD-3(t) has previously been mapped using micros-
atellite markers in the same RIL lines [33]. The resistance
gene was most closely linked to microsatellite marker
RM179 on chromosome 12 close to the most significant
SFP (adjusted p-value 5.06E-09) at position 13266396.
Thus, the SFP bulked segregant results are consistent with
previous genetic mapping based on conventional micros-
atellite markers.

Alignment of genomic sequence was demonstrated to be
an accurate method for in silico prediction of SFPs. Addi-
tional SFP markers could be obtained using the same
pipeline for indel discovery and probe design with the
input of genomic sequences of other rice varieties or
related species as they become available. SFPs may also be
discovered in other rice varieties through hybridization of
genomic DNA to tiling arrays [6]. The sequences of the
probes identified as polymorphic on the tiling arrays
could be used to design 70-mer probes to be included on
the lower cost spotted microarrays. Using this approach,
efforts are currently underway to expand the number and
varietal sources of SFP markers on the genotyping micro-
array. The on-going whole-genome SNP discovery project
in rice is expected to generate information on distribution
of SNP across 20 lines using Nipponbare sequence as the
reference [34]. Although the Perlegen hybridization

Temperate Japonica

Tropical Japonica





Figure 3
Neighbor Joining tree using the SFP marker data to
show the genetic relationships between the five sub-
populations of rice. Neighbor joining trees were con-
structed using four accessions per subpopulation. The boot-
strap values are out of 10,000.

approach will primarily yield SNP data, results from Ara-
bidopsis suggest that small to medium size indels could
also be inferred from the hybridization data file, provid-
ing a rich source of deletion sites across diverse germ-
plasm for designing SNP markers [35] and (D. Weigel,
personal communication). The described methods for
SFP discovery and the microarray-based genotyping assay
could also be implemented in any other species having
genomes small enough (e.g., medicago, sorghum, soy-
bean, and tomato) to permit adequate levels of hybridiza-
tion with high specificity to the spotted probes. However,
cross-hybridization problems may prevent the use of a
microarray-based genotyping methods in polyploids or
species with large genomes. The microarray-based geno-
typing platform is particularly useful for genetic mapping
applications requiring whole-genome scans.

Of the various possible applications to gene mapping, we
successfully demonstrated bulked-segregant analysis
where the use of genotyping microarrays is advantageous
because it provides a quantitative assessment of allele fre-
quencies between groups of pooled samples. By pooling
genotypes of two phenotypic extremes, these experiments
can be accomplished rapidly using a small number of
microarrays. We showed that we could pool large number
of genotypes per extreme (73 in our case), thereby defin-

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Plant Methods 2008, 4:13

ing a narrow genetic window. The median spacing of SFP
markers on the chromosomes is 234 kb. Assuming a 50%
polymorphism, BSA mapping provides resolution of
about 0.5 Mb or approximately 2 cM. Once the location is
mapped, simple sequence repeat (SSR) markers can be
used to saturate the region. Applying the same approach,
we were able to rapidly define the chromosomal location
of a mutation (M. Bemardo and H. Leung, unpublished

For conventional QTL mapping, the low cost per individ-
ual of this assay enables genotyping of large segregating
populations. Our current protocols, including replication,
have reduced the cost per genotype to less than $18 (Low
cost labeling method presented in Additional File 4). The
rice genotyping microarrays will be available for distribu-
tion through the Galbraith lab
~dgalbrai/ High density molecular marker coverage of
QTL mapping populations will delimit recombination
breakpoints with greater precision and potentially
enhance the mapping resolution. Molecular breeding
applications are also likely to benefit from microarray-
based genotyping. In backcrossing experiments, the
whole-genome coverage would allow genotypic positive
selection for the desired alleles at specific loci and nega-
tive selection against "background" donor alleles at all
other loci throughout the genome [36]. The whole-
genome coverage also provides the ability to construct
"graphical genotypes" to more efficiently pyramid desired
alleles at multiple loci [37]. Microarrays may also be used
to genotype introgression lines derived from wide crosses.
Tracking of introgressed chromosomal segments with
high precision in introgression lines combined with phe-
notypic analysis can be used to establish phenotypic
effects associated with particular introgressions through
advanced backcross QTL analysis [38] and provide oppor-
tunities for cloning of the underlying genes. Collections of
introgression lines genotyped at a high resolution will
facilitate more efficient utilization of genetic resources
[39,40]. While SNP platforms for rice are being developed
which will allow the generation of large amounts of data
for pennies a data point, they are still more than a hun-

dred dollars per sample, which limits their use in map-
ping populations and other applications in which a large
number of samples need to be run.

A method similar to the one described here has been
implemented in Arabidopsis (Salathia et al, 2007). Our
methods offer the advantage of a labeling procedure with
a substantial reduction in the per-sample cost, and we
provide a flexible platform for SFP identification and
probe design that can be used with genome sequences in
any other species.

The ability to detect polymorphisms across diverse rice
varieties makes the microarray-based genotyping platform
useful for population genetics studies. Domesticated
crops have complicated genetic histories and individual
can have a complex network of genetic relationships. The
microarray platform can produce a density of genotypic
data that is sufficient to track sub-population ancestry
across chromosome segments using structure analysis
[26]. The resulting population structure information
could be used to a framework for association mapping
with diverse lines [41-43] or elite lines [19,44]. Microar-
ray-based genotyping could also be used in plant variety
protection as a method to identify and distinguish
between released varieties with the robustness afforded by
large numbers of molecular markers.

Variations in DNA sequence are a mixture of SNPs and
indels. Indels are generated by different mechanisms than
SNPs. Indels may arise through transposon mediated rear-
rangements [45,46] and genomic expansion and contrac-
tion [47]. Indels within or spanning genes or regulatory
regions can be a significant component of intraspecific
genetic variation and a potential source of hererosis [48].
The flexibility of the spotted arrays also makes it relatively
simple to add more SFP markers over time to increase the
coverage of individual chromosomes. The results gathered
so far suggest that this SFP-based platform should provide
a very useful complement to SNP terminologies for asso-

Table 2: Validation of SFPs for significant fold change (< 0.05), fold change in the predicted direction, and detection or saturation of
hybridization signal.

Complementary to 93-1 I

Complementary to Nipponbare


Correct-not significant
Incorrect-not significant
Not found


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Plant Methods 2008, 4:13 /1/13

citing DNA sequence polymorphisms with phenotypic

Polymorphism discovery
The genotyping platform employs oligonucleotide probes
to detect the presence or absence of indel sequences in a
genomic DNA sample. Each indel must therefore contain
a stretch of unique, single copy sequence so that this is the
only sequence in the genome that can hybridize to its
complementary probe. To identify suitable indels, the
genome sequences of the japonica rice cultivar Nippon-
bare and the indica cultivar 93-11 were aligned. The
sequences used in the alignment are the pseudomolecules
of cv. Nipponbare assembled by TIGR (version 3) [49],
based on the International Rice Genome Sequencing
Project (IRGSP) finished quality sequence httip:/ and the contigs from the
whole-genome shotgun sequence of cv. 93-11 [19].
Whole-genome alignments were done using MUMmer
and NUCmer 3.18 [21-23], with a 1000 base alignment
extension and 1000 base maximum gap length. Indels
shorter than 30 bases were excluded from further analysis,
because they do not meet the probe length requirements.
Using Perl scripts, the indel sequences were first masked
for simple repeats. Next, the indel sequences were masked
for complex repeats following Blast analysis [50] against a


collection of known rice repetitive elements [24]. Indels
with sufficiently long stretches of unique sequence (at
least 30 nucleotides) were considered for probe design.

Oligonucleotide probe design
Oligonucleotide probes were designed to be complemen-
tary to the indel sequences. Long oligonucleotides (68-70
bases) were used to ensure sufficient signal intensities [51]
following hybridization. For uniformity in hybridization,
probes were selected to have a balanced GC content with
an optimum melting temperature (Tm) of 83C and a
range between 78 C and 88 C calculated using the "irre-
versible" formula for oligonucleotides greater than 50
bases [52]. To ensure that the probes do not cross-hybrid-
ize with any other sequences in the genome, potential
probes were excluded that had greater than 70% identity
across the entire probe, or stretches of contiguous
sequence longer than 20 bases with 100% identity to
another sequence in the genome [53]. The probes were
designed to overlap a maximum of 20 bases of sequence
extending beyond the indel on each side. Therefore, for a
70 mer, the minimum indel size is 30 bases. Perl scripts
were employed for processing the indel sequences for oli-
gonucleotide probe design based on the established
parameters. Potential probe sequences were checked
using Blast searches of the whole genome sequences of
Nipponbare and 93-11 to exclude those with hits having

1 2 3 4 5 6 7 8 9 10 11 12

IIM lTemperate

111111i @ ~1 r11 Ji mIu N 1I 11 Ill 11 1 11Iii IIiJaponica I I
Ill I III| I II I I 11111 I II I I I I101 I I II M 1 I I I Ill 1 I Tropic

S110111 *Ml 111011 10 1111111 * 111111111 1111 111 0 1111011011110 1111111 J Aus

I 11 1111 1 1 11111 1 q 11 11111M11 M111 1 Mi ll HAMilic III 11111 1 111i111 1111 p
11 11 1 111 1 i111 11 m 11111111 IN ii 1111 iiiuii i I II Ji i
III II I I II I I I I I I I I I I I I1 1 1 Indica
111111 1I I 11111 i IIII 11 1 I II I IIliii II 1 1 1 111 1111 liii III 111 1 1111111111 I
I Indica

Figure 4
Site-by-site probabilities for the population of origin of alleles across the twelve chromosomes of each acces-
sion. There are four accessions per sub-population plus Nipponbare and 93-1 I reference sequences. Calculations are done
using the linkage model of STRUCTURE.

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Plant Methods 2008, 4:13

I Chromosome 1


SChromosome 2

SChromosome 3

Chromosome 4

: Chromosome 5

- Chromosome 6

2 4 6 8 10 12 14 1 18 20 22

A 7 7

Pseudomolecule position (MB)

Figure 5
Significance of SFP fold change measurements between pools of DNA from blast resistant and susceptible
RILs. SFP markers showing association of the resistant parent (SHZ) allele with the resistant pool are assigned positive values
and association with the susceptible parent allele (LTH) with the resistant pool are assigned negative values. The line is drawn
using loess smoothing.

a percent identity greater than 70%. The remaining probes
were sorted by Tm, and the probe closest to the optimum
was selected for each indel. The SFP discovery scripts can
be found in Additional File 5, and the output of those
scripts in Additional File 6. A total of 880 putative SFPs
were identified from indels with sequences meeting the
probe design criteria (Additional File 1). Additionally, six
probes were designed from sequences not present in rice
to provide negative controls, and six probes were designed
from sequences present in both Nipponbare and 93-11
for use as positive controls. Six probes were designed from
repetitive sequences for optimization and troubleshoot-
ing of the hybridization protocol. Details of the various
control elements are provided in Additional File 3.

Microarray printing
The oligonucleotides were commercially synthesized
(Operon Biotechnologies, Huntsville, AL) with a 5'-amine
modification. The synthesized oligonucleotides were
arranged in 384-well plates, and dissolved at 20 pmol/iL
in 3 x SSC buffer. The oligonucleotide probes were printed
on Superamine substrate slides (SMM, Telechem, Sunny-
vale, CA) using an Omnigrid 100 printer (Genomic Solu-
tions,) Ann Arbor, MI equipped with Telechem SMP3
spotting pins. Each probe, including 880 putative markers
and 18 controls, was printed with three replicates per slide
in separate subarrays. After printing, the slides were baked
at 80C for two hours.

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24 26 28 30

Plant Methods 2008, 4:13

Chromosome 7

Chromosome 8

Chromosome 9
-| ---- -. "

Chromosome 10

Chromosome 11

SChromosome 12

0 2 4 6 8 10 12 14 16 18 20


'4 26 28 30 /1/13


0 0


o o


I I2
24 26

2 4 6 8 10 12 14 16 18 20 22
Chr. 12 pseudomolecule position (MB)

Figure 6
Location of the five most significant SFP markers
(ordered A-E) associated with blast resistance in the
bulked segregant experiment. The SFP markers are co-
located with the previously mapped microsatellite marker
(RM 179), known to be linked to blast resistance.

Target preparation
DNA samples were extracted from leaf tissues using a
modified chloroform-SDS protocol [54]. The genomic
DNA at a concentration of 100 ng/ul in a volume of 100
ul was sheared using a High Intensity Ultrasonic Processor
device (Sonics & Materials Inc., 250-Watt Model, New-
town, CT). For each sample, 1 |ig of sheared DNA was
labeled with Cy3 or Cy5 dUTP/dCTP (Amersham Bio-
sciences, Piscataway, NJ) using the BioPrime Array CGH
Genomic Labeling System (cat# 18095-012, Invitrogen,
Carlsbad, CA) in a 50 tiL volume with a 12-16 hour reac-
tion time. The Cy3 and Cy5 labeled products were puri-
fled simultaneously in a single spin-column (Qiagen PCR
purification kit, cat# 28104, Valencia, CA), and eluted in
25 tiL of water.

Prior to hybridization, the microarray slides were rehy-
drated over a 50C water bath for ten seconds and then
snap-dried on a 65 C heating block, repeating both steps
four times. Next, the slides were UV cross-linked using a
Stratalinker [180 ml] (Stratagene, La Jolla, CA). The slides
were then incubated in a 1% BSA solution in 6.6x SSC at
37C for 40 minutes, placed in 1% SDS for five minutes,
dipped ten times in water, and spun to dryness in a bench-
top centrifuge at 1,000 rpm for 2 minutes. The hybridiza-
tion buffer was prepared using 24 iL of labeled DNA
(including both the Cy3 and Cy5 samples), 1.2 [iL of 2%
SDS, 3 tiL of 20x SSC, and 1.8 [iL of Liquid Block (Amer-
sham Life Science, cat# 1059304). To denature the labeled
samples, the hybridization buffer was heated in a thermo-
cycler for five minutes at 100 C and placed immediately

0 0 0

on ice. The hybridization buffer (60 ul) was loaded onto
each slide under a cover slip (Lifter slip, Erie Scientific,
241x301-2-511) and incubated for 12-16 hours at 65C
in a hybridization chamber (Telechem/ArrayIt Hybridiza-
tion Cassette, AHC). After hybridization, the slides were
washed successively in three solutions for five minutes
each, with 2x SSC and 0.5 % SDS at 650C, 0.5x SSC at
room temperature, and 0.2x SSC at room temperature.
The slides were centrifuged to dryness at 1,000 rpm for 2

Data acquisition and analysis
The microarray slides were scanned with a Gene Pix
Autoloader (Axon/Molecular Devices, 4200A01, Sunny-
vale, CA) at a resolution of 10 |im per pixel with laser illu-
mination (100% power) at 532 and 635 nm, and PMT
gain settings between 700 and 800 (adjusted for balance
between colors). The images were saved in 16-bit gray-
scale multi-image TIFF format. Spot finding and data
extraction was done using GenePix Pro 6 software (Axon/
Molecular Devices, Sunnyvale, CA) and a GAL format file
describing the position and content of each spot created
using the Gridder software connected to the Omnigrid
100 printer.

The extracted data were analyzed in the R statistical lan-
guage htt: // using the Limma package
[55] of the BioConductor project [56]. Normalization for
dye balance within arrays was done based on the color
ratios of the non-polymorphic control spots and global
loess normalization [57]. Replicate spots within arrays
were handled using a correlation method [58]. A linear
model was fitted to the log transformed color ratios of
each probe, and an empirical Bayes approach was used to
shrink the estimated sample variances towards a pooled
estimate [59]. The R scripts used for the analysis are in
Additional File 7.

Experimental verification of SFPs
The SFPs were experimentally verified by hybridization
with DNA from each of the sequenced cultivars on four
slides. DNA samples from cv. Nipponbare and cv. 93-11
were each labeled with Cy5 and Cy3. A dye swap design
was used with Nipponbare labeled with Cy3 and 93-11
labeled with Cy5 on two of the slides and 93-11 labeled
with Cy5 and Nipponbare labeled with Cy5 on the other
two slides. The slides were normalized for color balance
using median centering.

Polymorphism survey
A diverse panel of rice cultivars was genotyped using the
microarrays. The panel included 21 accessions. There were
four accessions from each of the five sub-populations of
rice as previously established with microsatellite markers
[27]. An accession of the Australian wild relative Oryza

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Plant Methods 2008, 4:13 /1/13

meridionalis was included as an out-group. Nipponbare
DNA was used as a common reference, with one hybridi-
zation per genotype. The slides were normalized for color
balance using median centering. SFP markers were scored
as the 93-11 allele (different from the reference) for a log-
fold change greater than 1, and scored as the Nipponbare
allele for a log-fold change less than 0.5 (same as the ref-
erence). SFPs with intermediate log-fold changes were
treated as missing data. Neighbor-joining trees were con-
structed using the neighbor-joining algorithm in Power-
marker [60] based on a shared allele distance matrix, and
visualized using TreeView [61]. Population structure was
inferred and site-by-site probabilities for the population
of origin of alleles were calculated with the model-based
clustering method STRUCTURE [26], using the linkage
ancestry model with a bum-in of 100,000 and 100,000
MCMC replications. Site-by-site probabilities of alleles
were plotted using GGT [62].

Bulked segregant analysis
A RIL population of 215 individuals derived from the
blast resistant indica variety Sanhuangzhan 2 (SHZ) and
the blast susceptible japonica variety Lijiangxin-tuan-heigu
(LTH) was previously phenotyped for blast resistance
[33]. The SHZ and LTH parents were genotyped by hybrid-
ization to the microarrays with a dye-swap on two slides
to determine which SFP markers are polymorphic in the
RIL population. DNA samples from individual RILs were
divided into two pools with 73 individuals each according
to their levels of blast resistance. A dye swap design was
used with six slides. The data were lowess normalized
using all features. The chromosomal positions of the
markers were assigned according to their locations on the
rice pseudomolecules.

Indel: insertion/deletion

Competing interests
The authors declare that they have no competing interests.

Authors' contributions
JDE carried out the bioinformatics for indel discovery,
design of the oligonucleotide probes, and analysis of the
microarray data, and drafted the manuscript, JJ developed
the genomic labeling procedure, carried out the hybridiza-
tion experiments, and drafted the materials and methods
portion of the manuscript, MS designed primers sur-
rounding the SFPs, and optimized a low-cost method for
labeling of genomic DNA, ABK helped develop the proce-
dure for labeling of genomic DNA and helped draft the
manuscript, BL and HW produced the mapping popula-
tions, carried out the phenotyping experiment, and
helped draft the manuscript, DWG conceived of the study,

and participated in its design and coordination and
helped to draft the manuscript.

Additional material

Additional File 1
Genomic locations of the 880 SFP oligonucleotide probes.
Click here for file

Additional File 2
SFP genotype scores for rice accessions. A score of "1" indicates presence
of the allele that is complementary to the probe sequence and a score of
"0" indicates absence. Missing data are indicated by a "?" character.
Click here for file

Additional File 3
Low Cost Labeling Method.
Click here for file

Additional File 4
Sources of the positive, negative, and multi-copy control oligonucleotide
Click here for file

Additional File 5
SFP discovery scripts.
Click here for file

Additional File 6
Output of SFP discovery scripts.
Click here for file

Additional File 7
R-scripts for data analysis.
Click here for file

We thank Dr. Susan McCouch for supplying rice DNA samples, and Shao-
hong Zhang and Xiaoyuan Zhu for assistance in phenotyping. This project
was funded by a grant to DWG from the competitive grants program of the
USDA (grant number USDA-CSREES-NRI 2005-35604-15327).

Page 10 of 12
(page number not for citation purposes)

Plant Methods 2008, 4:13 /1/13

I. Chan EY: Advances in sequencing technology. Mutat Res 2005,
2. Kumar LS: DNA markers in plant improvement: an overview.
Biotechnol Adv 1999, 17:143-182.
3. Southern EM, Maskos U, Elder JK: Analyzing and comparing
nucleic acid sequences by hybridization to arrays of oligonu-
cleotides: evaluation using experimental models. Genomics
1992, 13:1008-1017.
4. Winzeler EA, Richards DR, Conway AR, Goldstein AL, Kalman S,
McCullough MJ, McCuskerJH, Stevens DA, Wodicka L, Lockhart DJ,
Davis RW: Direct Allelic Variation Scanning of the Yeast
Genome. Science 1998, 281:1 194-1 197.
5. Wodicka L, Dong H, Mittmann M, Ho MH, Lockhart DJ: Genome-
wide expression monitoring in Saccharomyces cerevisiae.
Nat Biotechnol 1997, 15:1359-1367.
6. Borevitz JO, Liang D, Plouffe D, Chang HS, Zhu T, Weigel D, Berry
CC, Winzeler E, ChoryJ: Large-scale identification of single-fea-
ture polymorphisms in complex genomes. Genome Res 2003,
7. Hazen SP, Borevitz JO, Harmon FG, Pruneda-Paz JL, Schultz TF,
Yanovsky MJ, Liljegren SJ, Ecker JR, Kay SA: Rapid array mapping
of circadian clock and developmental mutations in Arabi-
dopsis. Plant Physiol 2005, 138:990-997.
8. The International HapMap Consortium: A haplotype map of the
human genome. Nature 2005, 437:1299-1320.
9. Kruglyak L: Prospects for whole-genome linkage disequilib-
rium mapping of common disease genes. Nat Genet 1999,
10. Reich DE, Schaffner SF, Daly MJ, McVean G, Mullikin JC, Higgins JM,
Richter DJ, Lander ES, Altshuler D: Human genome sequence
variation and the influence of gene history, mutation and
recombination. Nat Genet 2002, 32:135-142.
I I. Syvanen AC: Toward genome-wide SNP genotyping. Nat Genet
2005, 37:5-10.
12. Stickney HL, Schmutz J, Woods IG, Holtzer CC, Dickson MC, Kelly
PD, Myers RM, Talbot WS: Rapid mapping of zebrafish muta-
tions with SNPs and oligonucleotide microarrays. Genome Res
2002, 12:1929-1934.
13. Barczak A, Rodriguez MW, Hanspers K, Koth LL, Tai YC, Bolstad BM,
Speed TP, Erie DJ: Spotted long oligonucleotide arrays for
human gene expression analysis. Genome Res 2003,
14. Schena M, Shalon D, Davis RW, Brown PO: Quantitative monitor-
ing of gene expression patterns with a complementary DNA
microarray. Science 1995, 270:467-470.
15. Kane MD, Jatkoe TA, Stumpf CR, Lu J, Thomas JD, Madore SJ:
Assessment of the sensitivity and specificity of oligonucle-
otide (50 mer) microarrays. Nucleic Acids Res 2000,
16. Jaccoud D, Peng K, Feinstein D, Kilian A: Diversity arrays: a solid
state technology for sequence information independent gen-
otyping. Nucleic Acids Res 2001, 29:E25.
17. Wenzl P, CarlingJ, Kudrna D, Jaccoud D, Huttner E, Kleinhofs A, Kil-
ian A: Diversity Arrays Technology (DArT) for whole-
genome profiling of barley. Proc Natl Acad Sci USA 2004,
18. Xie Y, McNally K, Li CY, Leung H, Zhu YY: A high-throughput
genomic tool: Diversity array technology complementary
for rice genotyping. j Integr Plant Biol 2006, 48:1069-1076.
19. Yu J, Hu S, Wang J, Wong GK, Li S, Liu B, Deng Y, Dai L, Zhou Y,
Zhang X, Cao M, Liu J, Sun J, Tang J, Chen Y, Huang X, Lin W, Ye C,
Tong W, Cong L, Geng J, Han Y, Li L, Li W, Hu G, Huang X, Li W, Li
J, Liu Z, Li L, Liu J, Qi Q, Liu J, Li L, Li T, Wang X, Lu H, Wu T, Zhu
M, Ni P, Han H, Dong W, Ren X, Feng X, Cui P, Li X, Wang H, Xu X,
Zhai W, Xu Z, Zhang J, He S, Zhang J, Xu J, Zhang K, Zheng X, Dong
J, Zeng W, Tao L, Ye J, Tan J, Ren X, Chen X, He J, Liu D, Tian W,
Tian C, Xia H, Bao Q, Li G, Gao H, Cao T, Wang J, Zhao W, Li P,
Chen W, Wang X, Zhang Y, Hu J, Wang J, Liu S, Yang J, Zhang G,
Xiong Y, Li Z, Mao L, Zhou C, Zhu Z, Chen R, Hao B, Zheng W, Chen
S, Guo W, Li G, Liu S, Tao M, WangJ, Zhu L, Yuan L, Yang H: A Draft
Sequence of the Rice Genome (Oryza sativa L. ssp. indica).
Science 2002, 296:79-92.
20. Khush G: Productivity improvements in rice. Nutr Rev 2003,
61:S 114-116.

21. Delcher AL, Kasif S, Fleischmann RD, Peterson J, White O, Salzberg
SL: Alignment of whole genomes. Nucleic Acids Res
22. Delcher AL, Phillippy A, Carlton J, Salzberg SL: Fast algorithms for
large-scale genome alignment and comparison. Nucleic Acids
Res 2002, 30:2478-2483.
23. Kurtz S, Phillippy A, Delcher AL, Smoot M, Shumway M, Antonescu
C, Salzberg SL: Versatile and open software for comparing
large genomes. Genome Biol 2004, 5:RI2.
24. Ouyang S, Buell CR: The TIGR Plant Repeat Databases: a col-
lective resource for the identification of repetitive sequences
in plants. Nucleic Acids Res 2004, 32:360-363.
25. Cheng Z, Dong F, Langdon T, Ouyang S, Buell CR, Gu M, Blattner FR,
Jiang J: Functional rice centromeres are marked by a satellite
repeat and a centromere-specific retrotransposon. Plant Cell
2002, 14:1691-1704.
26. Falush D, Stephens M, PritchardJK: Inference of population struc-
ture using multilocus genotype data: linked loci and corre-
lated allele frequencies. Genetics 2003, 164:1567-1587.
27. Garris AJ, Tai TH, Coburn J, Kresovich S, McCouch S: Genetic
structure and diversity in Oryza sativa L. Genetics 2005,
28. Second G: Origin of the Genic Diversity of Cultivated Rice
(Oryza-Spp) Study of the Polymorphism Scored at 40
Isoenzyme Loci. JpnJ Genet 1982, 57:25-57.
29. Glaszmann JC: Isozymes and Classification of Asian Rice Vari-
eties. Theor Appl Genet 1987, 74:21-30.
30. Jain S, Jain RK, McCouch SR: Genetic analysis of Indian aromatic
and quality rice (Oryza sativa L.) germplasm using panels of
fluorescently-labeled microsatellite markers. Theor AppI Genet
2004, 109:965-977.
31. Michelmore RW, Paran I, Kesseli RV: Identification of markers
linked to disease-resistance genes by bulked segregant anal-
ysis: a rapid method to detect markers in specific genomic
regions by using segregating populations. Proc NatlAcad Sci USA
1991, 88:9828-9832.
32. Benjamini Y, Hochberg Y: Controlling the false discovery rate: a
practical and powerful approach to multiple testing. journal of
the Royal Statistical Society Series B 1995, 57:289-300.
33. Liu B, Zhang S, Zhu X, Yang Q, Wu S, Mei M, Mauleon R, Leach J, Mew
T, Leung H: Candidate defense genes as predictors of quanti-
tative blast resistance in rice. Mol Plant Microbe Interact 2004,
34. McNally KL, Bruskiewich R, Mackill D, Buell CR, Leach JE, Leung H:
Sequencing multiple and diverse rice varieties. Connecting
whole-genome variation with phenotypes. Plant Physiol 2006,
35. Clark RM, Schweikert G, Toomajian C, Ossowski S, Zeller G, Shinn
P, Warthmann N, Hu TT, Fu G, Hinds DA, Chen H, Frazer KA, Huson
DH, Scholkopf B, Nordborg M, Ratsch G, EckerJR, Weigel D: Com-
mon sequence polymorphisms shaping genetic diversity in
Arabidopsis thaliana. Science 2007, 3 17:338-342.
36. Hospital F: Size of donor chromosome segments around
introgressed loci and reduction of linkage drag in marker-
assisted backcross programs. Genetics 2001, 158:1363-1379.
37. Langridge P: Lessons from applying genomics to wheat and
barley improvement. In 5th International Rice Genetics Symposium:
19-23 November 2005; Manila, Philippines Edited by: Brar DS, Mackill
DJ, Hardy B. Singapore: International Rice Research Institute;
38. Tanksley SD, Nelson JC: Advanced backcross QTL analysis: A
method for the simultaneous discovery and transfer of valu-
able QTLs from unadapted germplasm into elite breeding
lines. Theor Appl Genet 1996, 92:191-203.
39. Eshed Y, Zamir D: An introgression line population of Lycoper-
sicon pennellii in the cultivated tomato enables the identifi-
cation and fine mapping of yield-associated QTL. Genetics
1995, 141:1147-1162.
40. Tanksley SD, McCouch SR: Seed banks and molecular maps:
unlocking genetic potential from the wild. Science 1997,
41. ThornsberryJM, Goodman MM, DoebleyJ, Kresovich S, Nielsen D,
Buckler ES: Dwarf8 polymorphisms associate with variation in
flowering time. Nat Genet 2001, 28:286-289.
42. Tenaillon MI, Sawkins MC, Long AD, Gaut RL, DoebleyJF, Gaut BS:
Patterns of DNA sequence polymorphism along chromo-

Page 11 of 12
(page number not for citation purposes)

Plant Methods 2008, 4:13 /1/13

some I of maize (Zea mays ssp. mays L.). Proc Nat Acad Sci USA
2001, 98:9161-9166.
43. Remington DL, Thornsberry JM, Matsuoka Y, Wilson LM, Whitt SR,
Doebley J, Kresovich S, Goodman MM, Buckler ES: Structure of
linkage disequilibrium and phenotypic associations in the
maize genome. Proc NatlAcad Sci USA 2001, 98:11479-11484.
44. Malosetti M, Linden CG van der, Vosman B, van Eeuwijk FA: A
mixed-model approach to association mapping using pedi-
gree information with an illustration of resistance to Phy-
tophthora infestans in potato. Genetics 2007, 175:879-889.
45. Jiang N, Bao Z, Zhang X, Eddy SR, Wessler SR: Pack-MULE trans-
posable elements mediate gene evolution in plants. Nature
2004, 43 1:569-573.
46. Lai J, Li Y, Messing J, Dooner HK: Gene movement by Helitron
transposons contributes to the haplotype variability of
maize. Proc NatlAcad Sci USA 2005, 102:9068-9073.
47. Bruggmann R, Bharti AK, Gundlach H, Lai J, Young S, Pontaroli AC,
Wei F, Haberer G, Fuks G, Du C: Uneven chromosome contrac-
tion and expansion in the maize genome. Genome Res 2006,
48. Fu H, Dooner HK: Intraspecific violation of genetic colinearity
and its implications in maize. Proc Natl Acad Sci USA 2002,
49. Yuan Q, Ouyang S, Wang A, Zhu W, Maiti R, Lin H, Hamilton J, Haas
B, Sultana R, Cheung F: The Institute for Genomic Research
Osal Rice Genome Annotation Database. Plant Physiol 2005,
50. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local
alignment search tool. J Mol Biol 1990, 215:403-410.
51. Chou CC, Chen CH, Lee TT, Peck K: Optimization of probe
length and the number of probes per gene for optimal
microarray analysis of gene expression. Nucleic Acids Res 2004,
52. Wallace RB, Shaffer J, Murphy RF, Bonner J, Hirose T, Itakura K:
Hybridization of synthetic oligodeoxyribonucleotides to phi
chi 174 DNA: the effect of single base pair mismatch. Nucleic
Acids Res 1979, 60:6353-6357.
53. Xu W, Bak S, Decker A, Paquette SM, Feyereisen R, Galbraith DW:
Microarray-based analysis of gene expression in very large
gene families: the cytochrome P450 gene superfamily of Ara-
bidopsis thaliana. Gene 2001, 272:61-74.
54. Dellaporta SL, Wood J, Hicks JB: A plant DNA minipreparation:
Version II. Plant Mol Biol Rep 1983, 1:19-21.
55. Smyth GK: Limma: Linear Models for Microarray Data. In Bio-
informatics and Computational Biology Solutions using R and Bioconductor
Edited by: Gentleman RC, Dudoit S, Irizarry R, Huber W. New York:
Springer; 2005:397-420.
56. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S,
Ellis B, Gautier L, Ge Y, Gentry J: Bioconductor: open software
development for computational biology and bioinformatics.
Genome Biol 2004, 5:R80.
57. Smyth GK, Speed T: Normalization of cDNA microarray data.
Methods 2003, 3 1:265-273.
58. Smyth GK, Michaud J, Scott HS: Use of within-array replicate
spots for assessing differential expression in microarray
experiments. Bioinformatics 2005, 21(9):2067-2075.
59. Smyth GK: Linear models and empirical Bayes methods for
assessing differential expression in microarray experiments.
Statistical Applications in Genetics and Molecular Biology 2004, 3:3.
60. Liu K, Muse SV: PowerMarker: an integrated analysis environ-
ment for genetic marker analysis. Bioinformatics 2005, Publish with BioMed Central and every
61. Page RD: TreeView: an application to display phylogenetic sentist can read your work free of charge
trees on personal computers. Comput Appl Biosci 1996, "BioMed Central will be the most significant development for
12:357-358o. disseminating the results of biomedical research in our lifetime."
62. van Berloo R: Computer note. GGT: software for the display
of graphical genotypes. j Hered 1999, 90:328-329. Sir Paul Nurse, Cancer Research UK
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