Transcriptional profiling
The transcriptome, the entire repertoire of transcripts in
a species, represents a key link between information
encoded in DNA and phenotype. A fully quantitatively
described transcriptome is dauntingly large. For example,
there are more than 3 billion bases in the human genome,
about 1014 cells in the body, each cell has about 300,000
molecules of RNA [1], and the average gene size is about
28 kilobase pairs [2]. Thus, for a full representation of a
human, there are about 8.423 (280000 × 300000 × 1014)
RNA bases in the full transcriptome. The tools for
profiling RNA have been available for years, as Northern
blots, reverse-transcription PCR (RT-PCR), expressed
sequence tags (ESTs), and serial analysis of gene
expression (SAGE). But the rapid and high-throughput
quantification of the transcriptome became a possibility
only with the development of gene expression
microarrays [3]. With the more recent advent of
techniques for direct sequencing of the transcriptional
output of the genome, we can now at least begin to think
about a complete transcriptional characterization of all
the cells of an organism.
Microarrays
Gene expression microarray results have produced much
important information about how the transcriptome is
deployed in different cell types [4] and tissues [5], how
gene expression changes across development states [6,7]
and disease phenotypes [8,9], and how it varies within
[10] and between species [11]. They have also led to
surprising and contentious conclusions on how much of
the genome is transcribed into non-coding RNAs.
The starting point for a microarray is a set of short
oligonucleotide probes representing genomic DNA. A
typical modern microarray consists of patches of such
probes complementary to the transcripts whose presence
is to be investigated, and immobilized on a solid
substrate. In modern arrays, probe design is usually
based on genome sequence or on known or predicted
open reading frames and usually multiple probes are
designed per gene model. Transcripts are extracted from
samples of the cell or tissues to be investigated, labeled
with fluorescent dyes (either one color or two),
hybridized to the arrays, washed, and scanned with a
laser. Probes that correspond to transcribed RNA
hybridize to their complementary target. Because
transcripts are labeled with fluorescent dyes, light
intensity can be used as a measure of gene expression.
Expression profiling by microarrays has been very
successful. Searching the term ‘microarray’ in PubMed
produces more than 40,000 citations. The Gene Expression
Omnibus (GEO), the repository of transcript ome datasets
managed by the National Center for Biotechnology
Information, has more than 520,000 individual experi-
ments archived and around 21,000 project submissions,
most produced from microarrays. This impressive body
of work has produced a range of mature strategies for
data analysis and experimental design [12].
Abstract
Microarrays first made the analysis of the
transcriptome possible, and have produced much
important information. Today, however, researchers
are increasingly turning to direct high-throughput
sequencing – RNA-Seq – which has considerable
advantages for examining transcriptome fine structure
– for example in the detection of allele-specific
expression and splice junctions. In this article, we
discuss the relative merits of the two techniques, the
inherent biases in each, and whether all of the vast
body of array work needs to be revisited using the
newer technology. We conclude that microarrays
remain useful and accurate tools for measuring
expression levels, and RNA-Seq complements and
extends microarray measurements.
Microarrays, deep sequencing and the true
measure of the transcriptome
John H Malone* and Brian Oliver
REVIEW Open Access
*Correspondence: malonej@niddk.nih.gov
Laboratory of Cellular and Developmental Biology, National Institute of Digestive,
Diabetes, and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892,
USA
© 2011 Malone and Oliver; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.
Malone and Oliver BMC Biology 2011, 9:34
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As we have learned more about the design, chemistry
and kinetics of array assays, the quality of microarray
data has improved dramatically. In the early days,
microarrays designed by different companies appeared to
produce different results with the same samples [13]. The
fluorescent readout of hybridization intensities varied
between different laser scanners and there was variation
in reproducibility between different labs [14]. Ozone
differentially degraded the fluorescent dyes [15].
Recognition of biases and other artifacts by individual
labs and organizations, such as the MicroArray Quality
Control (MAQC) consortium, has led to the development
of quality control standards that operate to ensure the
utility of a well performed microarray experiment [16].
For example, experimental and computational methods
have been developed for dealing with systematic variation
between laboratories [12,17-19]. As with any measure-
ment tool, it is important to know the biases inherent in
the technique. For microarrays, it has taken a decade to
understand these biases but for microarrays this has now
been achieved and stable analytical solutions have been
developed.
Deep sequencing
Meanwhile, a revolution in the analysis of RNA has come
about through the development of tools for massively
parallel sequencing of DNA molecules. Not very many
years ago a graduate student using a slab gel
electrophoresis instrument with fluorescent terminator
chemistry would be excited to get 500-800 base pairs of
high quality sequence data from a single gene after about
a week’s worth of work. For perspective, Drosophila
melanogaster has 120 million bases in its small and
compact genome and so a hard-working graduate student
would need more than 400 years to complete one
genome. In early genome projects, even with an entire
team of people spread across both academic and
commercial sectors of science it took several years of
work to complete the D. melanogaster genome [20].
Today, roughly 10 years later, we have instruments that
can sequence multiple fly genomes in a few days to a
week [21]. This technology allows a DNA fragment to be
repeatedly sequenced in a very short time – a procedure
that is known as deep sequencing and delivers greatly
increased sensitivity and accuracy. These techniques have
most recently been extended to the analysis of the
transcriptome by what is known as RNA-Seq [22-28].
Deep sequencing of RNA on Illumina’s Genome Analyzer
and HiSeq instruments as well as Applied Biosystems’
SOLiD instrument are now fast-developing alternatives
for profiling the transcriptome.
Instead of using molecular hybridization to ‘capture’
transcript molecules of interest, RNA-Seq samples
transcripts present in the starting material by direct
sequencing. Transcript sequences are then mapped back
to a reference genome. Reads that map back to the
reference are then counted to assess the level of gene
expression, the number of mapped reads being the
measure of expression level for that gene or genomic
region.
There are several things that sequencing RNA can do
that microarrays cannot. Because RNA-Seq provides
direct access to the sequence, junctions between exons
can be assayed without prior knowledge of the gene
structure, RNA editing events can be detected, and
knowledge of polymorphisms can provide direct
measurements of allele-specific expression. Because
microarray probes are designed on the basis of inferences
from prior genomic sequence data, and light intensity is
used as surrogate of gene expression, microarrays will
miss exon junctions for novel expressed regions and RNA
editing events, and cannot easily detect allele-specific
differences in gene expression. Finally, because RNA-Seq
provides direct access to the sequence this technique can
be used on species for which a full genome sequence is
not available, whereas the only option in this case for
microarrays is to hybridize RNA to a microarray designed
for another species, which has limitations because of
sequence divergence.
There are also several general problems with measuring
gene expression levels genome-wide that sequencing
RNA might make easier. Expressed regions of the genome
that correspond to genes not currently identified might
be easier to detect with sequencing than with
microarrays, because detection depends only on where
reads map in the genome and not on whether that region
is annotated. That limitation of microarrays can, however,
be overcome by what are known as tiling arrays, in which
overlapping probes are designed to assay sequences over
the entire genome [29-32]. Tiling arrays were the basis
for the discovery of genomic ‘dark matter’ – extensive
transcription from non-coding regions of the genome.
However it is difficult using tiling arrays to balance the
design of probes to achieve full genome coverage while
avoiding as far as possible cross-hybridization potential,
and this has led to controversy about the extent of the
non-coding transcriptome. RNA-Seq does not depend
upon hybridization and thus does not suffer from this
potential artifact.
Another strength of RNA-Seq is in the quantification of
individual transcript isoforms [33,34]. Alternative
splicing, the mechanism whereby different isoforms of
proteins are generated, is acknowledged to be an
important source of functional diversity in eukaryotes,
but it has been relatively little studied at the level of the
transcriptome, principally because of the difficulty of
measuring expression for each isoform. Splicing arrays
exist but they require probes designed to be
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complementary to junctions, and these can therefore be
generated only if the genes and the distinct isoforms
produced from them are already known [35]. Sequencing
by contrast provides direct access to reads that span
exon/exon boundaries and in theory makes it possible to
study the expression of different isoforms for a gene and
to make comparisons of isoform diversity and abundance.
Additionally, sequencing appears to be better at detecting
exon/exon junctions than arrays [29].
Practical advantages and drawbacks of microarrays
and RNA sequencing
So far, we have discussed the advantages and
disadvantages of sequencing and arrays that are inherent
in the two techniques. But there are also major practical
considerations. The greatest current advantage of arrays
is their relatively low cost compared with sequencing (in
our lab about 10X). Presently, using a 12-plex array from
Nimblegen our array costs are less than $100 per sample
whereas sequencing is around $1,000 per sample. These
costs will decrease as sequencing output increases.
Another advantage is knowledge of biases in array data
and mature analysis strategies and experimental designs
for dealing with them. By comparison, sources of bias in
sequence data are still being actively researched, and
optimum analytical strategies developed [36]. Meanwhile
RNA-Seq continues to evolve, so it will take some time to
develop appropriate standards for this tool.
One of the most important concerns about sequencing
RNA is the depth of sequencing required to effectively
sample the transcriptome. This equates to how many
times to sequence a sample. For highly expressed genes,
small amounts of sequencing are sufficient, but for the
middle and low end of expression levels, it is clear that
many reads are needed. In the fly modENCODE samples
for example, even after 50 million mapped reads new
transcript discovery did not saturate [37]. In our hands,
we estimate that 6-8 million mapped reads provide
adequate coverage to accurately estimate roughly 80-90%
of the head transcriptome in flies. Other tissues are
different and this is particularly the case for genes with
low levels of expression. The gene doublesex, a
transcription factor involved in sexual dimorphism in
flies, is not detected by RNA-Seq in the deeply sequenced
modENCODE embryo samples [37] where it is known to
be expressed in a few cells. This gene and others at similar
expression levels missed by sequencing highlight the
problem of detecting genes with low expression no
matter what the technique, be it arrays or sequencing.
This example aside, failure to obtain sufficient coverage
and check the representation of this coverage (that is,
library complexity) will provide erroneous metrics of
gene expression and lead to false inferences even for
genes that are detected. Given the current expense of
RNA-Seq, and the excitement about the prospects of
deep sequencing, this may cause some groups to avoid
determining the coverage (that is, the number of reads)
necessary to accurately sample the transcriptome of
interest. High costs may also tempt some to avoid using
biological replicates. These choices can lead to inaccurate
estimates of gene expression level and thus false
inferences [36]. Another source of bias in sequencing is
the heterogeneity of reads across an expressed region –
that is, uneven sequencing depth along the length of a
transcript. This heterogeneity in coverage will influence
expression estimates for transcripts and needs to be
corrected [38,39,40]. Coverage and heterogeneity are not
an issue in microarrays because of the fixed nature of
probes that capture the transcripts by hybridization.
A final consideration about arrays and sequencing is
the quantity and size of the data. In expression
microarrays the raw data are composed of image files,
typically TIFF files that may be around 30 MB per array.
These TIFF files are transformed into text files that
contain fluorescence intensities for each gene. The
Illumina instrument generates upwards of 600 GB of data
files but the sequence files (around 20-30 GB) are
typically used as a starting point for analysis. These
sequence files are an order of magnitude larger than
those from arrays and because of these large file sizes,
Python, Perl, Unix command line, and other scripting are
necessary to sort and experiment with these files. Using
spreadsheet software will not be an option and therefore
bioinformatics support is necessary. For biologists
unfamiliar with computer languages, there are growing
alternatives for working with sequencing data. For
example, many of the tools for sequencing data analysis
are now available in Galaxy software, a web interface that
provides a user friendly graphical interface [41,42].
An example from the fruit fly
As a way to introduce and discuss microarrays and deep
sequencing for measuring the transcriptome we will use
a fly example from our own laboratory: specifically,
experiments designed to profile gene expression in
female and male heads of Drosophila pseudoobscura. This
is one of several species of fly that we are profiling to
validate evolutionarily novel D. melanogaster transcripts
in the model organism Encyclopedia of DNA Elements
(modENCODE) project [43]. We performed microarray
and RNA-Seq experiments on the same samples and then
compared expression measurements between micro-
arrays and RNA-Seq.
Figure 1 describes an expression experiment designed
to identify genes that are differentially expressed in
D.  pseudoobscura female and male heads, which were
manually dissected from flies over dry ice, after which
total RNA was extracted followed by a poly A+ selection.
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Figure 1. Data production workflow for microarrays. Microarrays require labeling of target material, hybridization to arrays, washing, and scanning
to obtain measures of gene expression. RNA converted to cDNA from the sample will hybridize to the corresponding oligonucleotide targets, so
that more highly expressed genes will be reflected in more abundant material hybridized and thus greater fluorescence intensity. In modern arrays,
multiple probes are designed for a single gene in order to obtain fluorescence intensities that can be used as an index of gene expression.
Convert to cDNA by
RT and random
priming and Label
Microarrays
Sa
m
pl
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Pr
ep
PolyA+ mRNA Extraction
Hybridization
Pr
oc
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Ac
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by laser
Females Males
PolyA+ mRNA Extraction
D
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a Raw: Image files (a few megabytes in size) with fluorescent intensities
Processed: Text files with intensities of gene expression
Female-Biased
Ex
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si
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L
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Male-Biased
No-Sex-Bias
Ex
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L
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Ex
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L
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poly A+ selected mRNA was converted to cDNA using
end-labeled random nonamers and reverse transcription.
During this reaction, a fluorophore is added to the 5′ end
of each short cDNA. In this case, the cDNA of one sex
was labeled with one type of fluorescent dye (cyanine 3 or
Cy3) and the cDNA of the other sex was labeled with a
different fluorescent dye (cyanine 5 or Cy5) with
fluorescence at a different wavelength. We generated
replicate samples (N = 4) and samples with dyes swapped
between females and males in order to control for
technical artifacts due to labeling and dye biases and to
measure the inherent variability in gene expression
irrespective of the sex of the sample.
As with any assay, replicate samples are critical for
statistical analysis. The female and male labeled cDNA
samples were mixed and applied to the microarray for
hybridization. cDNAs that are complementary to probes
on the microarray hybridize on the basis of simple first
principles: more highly expressed genes will have more
transcripts converted to labeled cDNA, and these more
abundant cDNAs will bind more to their target probes
than those of less expressed genes. Because we co-
hybridized samples labeled with different fluorescent
dyes we can take a ratiometric expression score between
female and male heads: that is, genes that are more highly
expressed in one sex than in the other will hybridize
more to the target probe and generate a stronger signal.
Genes that are expressed at the same level in both sexes
will have equivalent amounts of transcript bound to
probes and so the signal will be a combination of both
Cy3 and Cy5 signal thereby generating a signal
intermediate between the two (yellow fluorescence). The
analysis and normalization methods for microarrays are
highly developed [12] and thus this experiment should
allow the differences in steady-state mRNA levels
between female and male head tissue to be reliably
measured.
Figure 2 shows the same analysis performed by RNA-
Seq, using an Illumina Genome Analyzer and a
commonly deployed protocol for preparing libraries [44].
First, the transcriptomes for females and males are
fragmented by alkaline hydrolysis, then reverse-
transcribed to make double-stranded cDNAs using
random hexamer primers. Next, the ends of transcript
fragments are prepared to enable oligonucleotide
adaptors to be ligated onto the ends. Fragments are then
size-selected, amplified by PCR and injected into a flow
cell. The flow cell is a glass slide that contains a lawn of
oligonucleotides complementary to the adaptors ligated
to transcripts and with a series of separate lanes in which
sequencing reactions take place.
Once the adaptors on the DNA fragments have
hybridized to the complementary oligonucleotides in the
flow cell, the fragments are amplified by isothermal
bridge amplification to generate clusters of DNA clones.
(In isothermal bridge amplification, the templates arch
over and bind to adjacent oligonucleotides and then DNA
polymerase copies the templates.) Double-stranded
DNAs are denatured and the process is repeated to
generate clusters of DNA clones. Next, the free 3′ OH
ends of the linearized clusters are blocked to prevent
nonspecific sequencing reactions. Finally, the clusters are
denatured and a sequencing primer is hybridized to the
linearized and blocked clusters.
Sequencing reactions consist of a series of reactions to
image individual bases within each cluster. Bases are
imaged by using reversible fluorophore terminator
nucleotides. The first base in the cluster is identified by
adding four labeled reversible terminators, primers, and
polymerase. A laser is used to excite the fluorophores and
this allows identification of the first base. The next cycle
repeats the incorporation of four reversible terminator
nucleotides, primers, and polymerase. A laser again
excites the terminators and bases are identified. These
cycles of adding reagents, followed by laser excitation,
and data capture are repeated to produce a read and
typical reads range from 25 to over 75 base pairs in size.
At the end of a run (3-7 days or more depending on read
length) there are 30-40 million (possibly more) high
quality sequences.
The RNA-Seq measure of gene expression is density of
reads mapping to a particular transcript. For species with
sequenced genomes, a common method is to map reads
to a reference genome. Illumina provides a mapper called
ELAND but many free open source tools are available.
The tools that we have used most extensively for RNA-
Seq are the Tuxedo Suite Tools (Bowtie [45], a short read
mapper; Tophat [46], a splice junction identifier, and
Cufflinks [33], a transcript assembler). Two expression
metrics are commonly used which provide a value
normalized by overall sequencing depth, FPKM
(expected fragments per kilobase of transcript per million
fragments mapped) and RPKM (reads per kilobase per
million mapped reads) [23,33,40], which are conceptually
similar. In the example given in Figure 2, we estimate
expression in units of RPKM by quantifying reads that
map with genes predicted from genomic sequence.
Therefore, higher RPKM in females would be examples of
genes with female-biased expression, higher RPKM for
males would be genes with male-biased expression, and
equivalent RPKM in both sexes would be examples of
non-sex-biased genes.
Do arrays and RNA-Seq tell a consistent story?
A key first question is whether, when used to ask exactly
the same question, both techniques give the same answer.
Comparing expression metrics from array intensities to
RNA-Seq density shows a strong congruence (Figure 3).
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Figure 2. Data production workflow for RNA-Seq. RNA-Seq requires building libraries of fragmented RNA that are then converted to cDNA by
reverse transcription, followed by adaptor ligation and size selection. Sequencing libraries are prepared for clustering on an 8 lane flow cell and
sequencing-by-synthesis is used to generate tens of millions of sequences per sample that can be mapped to a reference genome. The number of
reads that map to a scaled region of genome space are the index of the expression level of the gene.
mRNA-Seq
Fragment RNA
Ligate Adapters
AAAAAA
AAAAAA
AAAAAA
Cluster
(solid phase
clonal amplification)
Sequence
by
Synthesis
PolyA+ mRNA Extraction
Females Males
PolyA+ mRNA Extraction
Sa
m
pl
e
Pr
ep
AAAAAA
AAAAAA
AAAAAA
AAAAAA
AAAAAA
Pr
oc
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ur
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Flow cell
Ac
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iti
on
A
T
C
G
C
T
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A
D
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a Raw: FASTQ files of millions of sequences (1-10s of Gigabytes / lane)
Processed: Mapping, Junction Detection, Quantification
Convert to cDNA
by RT and
random priming
5’
Pr
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A
C
T
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A
5’
Pr
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Cycle 1:
Add sequencing reagents
First base incorporated
Remove unincorporated bases
Detect signal with laser
Cycle 2-n:
Add sequencing reagents
and repeat
A
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A
Gene Models
Read Coverage
> <=
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The relationship is not quite linear, as there appears to be
a slight compression in the array data at the high end, but
the vast majority of the expression values are similar
between the methods. Scatter increases at low expression,
which is not surprising, as background correction
methods for arrays are complicated when signal levels
approach noise levels. Similarly, RNA-Seq is a sampling
method and stochastic events become a source of error
in the quantification of rare transcripts [47]. There is,
however, one consistent difference in our comparisons in
Drosophila. There is a large range of expression values at
the low end on arrays that that are undetectable by RNA-
Seq. We cannot explain this difference, but whatever the
cause, it does not affect the measurement of differential
expression at expression levels that are detectable by
RNA-Seq (Figure 3). In our experiment, we used
biological replicate samples for the arrays and applied
moderated t-tests to detect those genes that were
differentially expressed between females and males. In
the analysis in Figure 3, our goal was to compare
expression measurements between the platforms. The
genes showing sex-biased expression (red and blue dots
in Figure 3) are in outstanding agreement between
microarrays and RNA-Seq. We have observed similar
congruence in the extremely deep RNA-Seq data in
modENCODE D. melanogaster female and male samples
[37]. Annotated sex-biased genes based on the extensive
array-based literature [48] and the deeply sequenced
modENCODE samples report the same biology.
The answer is yes
Both sequencing and hybridizing mRNA to arrays are
high-throughput ways to profile the transcriptome and
for problems that can be addressed by both, they show
similar performance and complement each other
[29,47,49]. Detecting genes with low expression will
Figure 3. Comparison of array and RNA-Seq data for measuring differential gene expression in the heads of male and female
D. pseudoobscura. (a) Results for female heads; (b) results for male heads. We used custom designed Nimblegen arrays to an early release of the
D. pseudoobscura annotation. This array consists of 50-mer probes selected without bias to gene position, and with an average of 10 probes per
gene model. A full description of this array platform can be found in the GEO under platform number GPL4631. Robust Multi-array Averaging (RMA)
[50] was used to normalize array experiments and normalization improves the correlation between arrays and sequencing results. A full description
of the analysis and all sequencing data can be found in [51]. Colored circles are genes identified as differentially expressed between females
and males by microarray analysis with four biological replicates. In this case, one of the four biological replicates was prepared for sequencing
by fragmenting RNA using alkaline hydrolysis and constructing a cDNA library for sequencing. For these analyses, we generated about 6 million
36 base pair reads from the Illumina GA I platform and the number of reads per kilobase per million mapped reads (RPKM) was calculated by
counting the number of unique mapping reads from the default Illumina mapper (ELAND but the same pattern holds for Bowtie) to the same
coding sequence models that were used for constructing probes for the microarray. The correlation between fluorescence intensity as a surrogate
for gene expression and the RPKM metric as obtained by mRNA-Seq is high (Pearson’s r = 0.90-0.91; Spearman’s rho = 0.90-0.91) and slightly higher
for just the genes identified as differentially expressed by microarrays (Pearson’s r = 0.89-0.92; Spearman’s rho = 0.90-0.94). In the case of fold change
(c) measurements (female/male), the congruence is reasonable for the entire data set (Pearson’s r = 0.62; Spearman’s rho = 0.54) but high in the case
of the fold change measurements for the genes with sex-biased expression (Pearson’s r = 0.92; Spearman’s rho = 0.89).
M
ic
ro
ar
ra
y
In
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(l
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2)
mRNA-Seq Read Density (log2)
M
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Females
B C
Males F - M Array vs. F-M Seq
A
Female-biased expression
Male-biased expression
No-sex-bias expression
0
4
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6
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0 42 6-2-4 8
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remain a problem for both techniques, but there are
some applications, such as transcript discovery and
isoform identification, where RNA-Seq will be the better
choice. Given the substantial agreement between the two
methods, the array data in the literature should be
durable.
Acknowledgements
We thank members of the Oliver lab for useful comments and discussion.
This research was supported by the Intramural Research Program of the NIH,
NIDDK.
Published: 31 May 2011
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Malone and Oliver BMC Biology 2011, 9:34
http://www.biomedcentral.com/1741-7007/9/34
doi:10.1186/1741-7007-9-34
Cite this article as: Malone JH, Oliver B: Microarrays, deep sequencing and
the true measure of the transcriptome. BMC Biology 2011, 9:34.
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