December 2011

Transitioning from Microarrays to mRNA-Seq
By providing HiSeq® 2000 mRNA-Seq data in two formats, Expression Analysis enables its
customers to compare sequencing results with older array data, while gaining insight into theentire transcriptome.
Introduction
Gene expression microarrays have been commercially available
for more than 10 years, providing researchers with biologicallymeaningful results that have enhanced our understanding of diseaseprogression and supported advances in therapeutic development.
These microarrays use probes to simultaneously analyze theexpression of thousands of genes, with each probe targeting
a unique sequence within an mRNA transcript of interest. The
result is a snapshot of actively expressed genes and transcripts(transcriptome) at a given point in time. While microarrays are
effective for identifying the expression of known genes andtranscripts, the snapshot of the transcriptome they provide isincomplete. They cannot detect previously unidentified genes ortranscripts, and will thus miss changes in their expression that may
be associated with a phenotype of interest.
As the cost of sequencing has gone down, researchers have begun toturn to mRNA sequencing (mRNA-Seq) for gene expression analysis.
Offering more comprehensive coverage through single-read or pairedend sequencing, this approach enables rapid profiling and deep investigation of the transcriptome. Yet, with more than a decade of experience
using microarrays and often little experience with sequencing, it’s hardfor some scientists to make the switch. While they need the more informative, higher quality data that mRNA-Seq provides, they also want theability to compare the sequencing data with earlier microarray results.
Expression Analysis, an Illumina Certified Service Provider (CSPro®)
based in Durham, North Carolina, developed a service solution thataddresses these concerns. By developing tools that increase theaccessibility of the mRNA-Seq data for sequencing neophytes andmake it easier to compare sequencing data with microarray resultsfrom previous studies, Expression Analysis has uniquely positioned
itself to better serve the mRNA-Seq market. iCommunity spokewith Steve McPhail, President and Chief Executive Officer, Wendell
Jones, Ph.D. Vice President, Statistics and Bioinformatics, and JoelParker, Ph.D., Principal Scientist, Statistics and Bioinformatics at(Pictured left to right) Steve McPhail, President and Chief Executive Officer,Wendell Jones, Ph.D., Vice President, Statistics and Bioinformatics, andJoel Parker, Ph.D., Principal Scientist, Statistics and Bioinformatics atExpression Analysis.

Expression Analysis to learn how Illumina sequencing systems andreagents are supporting their service offering.
Q: What types of services does Expression Analysis offer?
Steve McPhail (SM): We started out offering genotyping and gene
expression testing using microarrays and brought sequencing on board
in 2009 with an Illumina Genome Analyzer™ sequencer. We’ve sinceadded another Genome Analyzer and three HiSeq 2000 systems, andnow offer a variety of sequencing services, including small RNA sequencing, exome sequencing, candidate gene resequencing, and mostrecently, mRNA sequencing. The HiSeq 2000 system provides us with away to generate more data cost-effectively.
Wendell Jones (WJ): In partnering with clients to complete a project,
we advise them about different approaches to solve the research challenge. From a client’s perspective, there’s always the question of whatplatform they should use. We provide the benefits, tradeoffs, and costsof each approach and they decide the direction they’d like to take. It
can be a fairly complex equation with a lot of intangibles, such as will
they be able to analyze the data in the same way they have in the pastor can they compare these results with data that they’ve generated
previously. This has certainly been the case with mRNA-Seq.
Q: Why did you decide to add mRNA-Seq services?
SM: We’d been thinking about adding mRNA-Seq for just over ayear because we felt that it offered very compelling benefits overmicroarray-based technologies in conducting transcriptome studies. mRNA-Seq offers improved specificity, so it’s better at detecting
transcripts, and specifically isoforms, than microarrays. It’s also moresensitive in detecting differential expression and offers increased
dynamic range. One of the things that all clients are concerned about
with microarrays is batch processing bias, something we don’t see
to the same degree with sequencing. Also, it’s well known that when
we see expression on a microarray platform, the expression signal
tends to be very compressed compared to qPCR. We see much lesscompression taking place in mRNA-Seq. We also see significantly
improved reproducibility of data sets in mRNA-Seq based on a varietyof sequencing parameters.
WJ: The fact that mRNA-Seq can detect single nucleotide variants
and small insertions and deletions is huge. It’s impossible to see indels
with a microarray. mRNA-Seq is also unbiased with respect to potential content, so no predefined transcriptome information is required.
That is, there is not a requirement to design and manufacture probes
to create a result. mRNA-Seq enables us to capture a very broad
range of what is going on in the sample, rather than only look into
areas we already know are important.

December 2011
Q: How have your customers responded to moving tomRNA-Seq for transcriptome studies?

from sequencing, we were amazed when the higher resolution ofmRNA-Seq showed events occurring that we had not anticipated.

SM: I think there were several reasons why our clients were initiallyslow to evaluate mRNA-Seq. Number one was cost. Until very
recently, the cost of mRNA-Seq was still significantly higher than thecost of running expression profiling on microarrays. Illumina solvedthat with the introduction of HiSeq 2000 and its TruSeq™ v3 reagents.
Since most of our customers were more familiar with microarrays, wealso felt that many of them were waiting to see more gene expression
studies performed with mRNA-Seq.

JP: mRNA-Seq identified variations that we couldn’t see in the arraydata. In one cell line, we were able to confirm a known heterozygous
mutation in GATA3, a transcription factor involved in growth control
and the maintenance of differentiated epithelial cells. Research has
shown that GATA3 variants may contribute to tumorigenesis in certain
breast tumors2.

Q: Is that why you conducted an mRNA-Seq vs. arraycomparison study?
SM: We wanted to help our clients understand the benefits ofmRNA-Seq over microarrays for transcriptome analysis, so wedecided to conduct real-world experiments and give our clients
access to these data sets so they could begin to integrate them into
their research. We felt the data would be persuasive in showing how
mRNA-Seq could provide additional information about the biology ofwhat they were studying.
WJ: Instead of reference samples, we used samples with biological diversity. We simulated a real biological experiment that people
send us every week, performed the analysis on microarrays and theHiSeq 2000 system, and compared the results.
Joel Parker (JM): The design of the study involved 15 breast cancer
cell lines, with five unique lines representing each of three cancer subtypes. We created three independent library preparations of each line
using the Illumina TruSeq protocol. Forty-five samples were multiplexed
into seven pools and run on two HiSeq flow cells. The 15 samples werealso assayed on Affymetrix and Illumina microarrays and we compared
the performance based on repeatability, sensitivity, specificity of detection, abundance estimation, fold-change, and differential expression.
The results demonstrated the uniformity and quality or the data produced with mRNA-Seq. Replicates run at different times demonstrated
a negligible decrease in reproducibility as opposed to microarrays,
where batch effects due to technical variation, sample handling, andprocess variances often negatively impact reproducibility.
mRNA-Seq detected more of the content specific to Affymetrix andIllumina microarrays than either of the microarray platforms on thesame samples. It also detected genes, isoforms, and differential
expression not detected by either one, with approximately onethird of the SNVs detected by mRNA-Seq not known to dbSNP, thepublic domain archive of all established genetic variation. We foundthat 25M reads are necessary for repeatability equivalent to or better than the microarray platforms, while 10M may be sufficient forequivalent or improved detection and differential expression.
SM: We generated a pretty compelling data set, initially presenting
it in a September webinar1. It’s been a popular presentation, with over
200 downloads and more than 125 people viewing the webinar. We’re
now sharing this data with customers.
Q: Were you surprised by these results?
WJ: We expected the sequencer to perform better than arrayson several different fronts and were happy to see that confirmed.
While we weren’t surprised that we would obtain more information

Unlike microarrays, mRNA-Seq enabled us to see novel expression,
specifically isoform switching between two breast cancer stages.
There are instances where a gene’s isoform expression increases in
one cell subtype and decreases in another, while another isoform of
the same gene behaves oppositely. For example, CD44 is a cell surface protein that modulates cellular signaling in differentiation, growth,and apoptosis and has been linked with metastasis. The mRNA-Seq
data clearly showed increased luminal expression of one variant ofCD44 and decreased claudin expression, while another variant of thesame gene was just the opposite. When we first saw the switch, wethought it was interesting. Later, we found that another researcher had
recently independently published documentation of the same event,and found that it was essential for epithelial-mesenchymal transition
and breast cancer progression3.

“While we weren’t surprised that wewould obtain more information fromsequencing, we were amazed when
the higher resolution of mRNA-Seq
showed events occurring that we
had not anticipated.”
We also identified EGFR isoform switching in our samples. While
this growth factor has been found to undergo isoform switching incolorectal cancer, this event had not been seen in breast cancer.
Further research is needed to understand its potential role in theprogression of the disease.
Q: What other hurdles are there for clients wanting to switchfrom gene expression microarrays to mRNA-Seq?
SM: Mainly the fact that computational and analytical tools have notbeen readily available to assist researchers in analyzing the sequencing
data. Most of the gene expression informatics infrastructure has been
built around handling array-based data sets, so customer pipelines are
not yet in shape to handle RNA-Seq data sets. Up until now, they’ve
also had no way to compare their new mRNA-Seq data with their oldmicroarray data.
Q: Part of your mRNA-Seq offering is a bioinformatics output
that mimics array reports. Can you talk more about yourdeliverables and the software you developed to do that?
WJ: We recognized that we’d need to help some of our clients
overcome the inertia of switching platforms, especially their concerns about wanting to compare their new data with their old data.
So we offer the typical sequencing output you’d expect, with reads

December 2011
and alignments, as well as quantitation of the transcriptome—in aneasy-to-read table output—to a much finer degree than you could
have with microarrays.

set that mimics their microarray data set formats and lets them view
their output data in viewers on their desktop, and store, process, andanalyze the intermediate data in the cloud.

To enable customers to compare their data, we also provide sequencing
data in a format that mimics the CEL files many Affymetrix clients use fordownstream analysis of microarrays. CEL files are the standard format
for basic Affymetrix array data and reflect hybridization data for all theprobes on an array. Numerous pipelines and statistical packages suchas R/Bioconductor have adopted this format as a standard input tofacilitate downstream analysis. We created a ‘translated’ CEL file that’s
in the same CEL format they’re used to, but reflects sequencing datarather than hybridization data. Even though the richness of the sequencing data set is not in the CEL file, it will be easy to see that a portionof the mRNA-Seq results are better. For example, you’ll see greater
detection and dynamic range. These files allow our clients to compare
data that they’ve generated previously with mRNA-Seq data, creating abridge into the richer data set that they obtain via sequencing.

I see the transition from microarrays to mRNA-Seq as paralleling theshift in data demands that we initially saw with microarrays. For along time, people wanted the microarray data and the images. As
they became more comfortable with the image processing quality,
they realized all they needed were the final data. The same thing may
be true for mRNA-Seq, especially those who are looking at it as a
microarray replacement. They won’t necessarily need to have all thesequencing data; they could look at the quantification output, identify
the novel information, and use that to move their research forward. We
also archive the data for a period of time, so if they need to look at itlater on they can.

Q: Since they aren’t used to working with sequencing data, how
do you help customers decipher mRNA-Seq results?

SM: We chose Illumina sequencers because your company is themarket leader. We spoke with a number of your customers who werevery happy with the performance of their systems.

WJ: We provide summarized quantitation of transcripts that appear ina spreadsheet-like format, just like they’re used to seeing, with transcripts in each row, samples in each column, and quantitative values
for the transcripts in each sample. To help dig deeper into the data set,
we provide alignment files that can be used in genome or transcriptome viewers to examine alignments, point mutations, or small indels
that could be relevant to the biology of what is being studied. Ouralignment tools were designed to provide better unambiguous alignments, and deliver better detection and repeatability than when using
standard tools like Bowtie or TopHat individually.

“The HiSeq 2000 system generates thenumber of reads to make mRNA-Seq
cost-competitive with microarrays.”
SM: We have an agreement with Golden Helix to offer a cloud-based
service for the computational heavy lifting and processing of themRNA-Seq intermediate data. The Golden Helix application manages
the data on an as-needed basis, allowing researchers to see information
at any place in the genome without downloading all the data at once.
WJ: In this way, researchers are getting the full richness of thesequencing data set, with the ability to see what’s occurring at a baselevel, and if they want to, to view their entire sequencing data set. They
can also zoom out and easily look at the overall experiment, just likethey do with microarrays.

Q: Why did you choose Illumina sequencers, and specifically theHiSeq 2000 system, for your mRNA-Seq services?

WJ: Especially for mRNA-Seq, just about every measure regarding
performance (including many quality metrics) is going to be driven by
the number of reads that you can generate on the sequencer. So if
you have a technology that can generate a very large number of reads,
that gives you a competitive advantage performing mRNA-Seq. TheHiSeq 2000 system generates the number of reads to make mRNASeq cost-competitive with microarrays.
Q: Do you see the use of microarrays becoming limited toniche applications?
SM: I see the use of arrays for discovery-based applications falling off
pretty substantially. I think arrays will still have a place in the market fordevelopment-based applications and potentially for clinical applications.
WJ: For clinical applications, it depends on how quickly things become CLIA-approved. Microarrays have a head start because they’vebeen around longer, but turnaround time is also important in a clinicalsetting. Systems like the Illumina MiSeq® have a fast turnaround, sofor a small number of samples in the clinic, sequencers may offer anadvantage even over microarrays.

References:1.

http://www.expressionanalysis.com/webinars/

2.

Usary J, Llaca V, Karaca G, Presswala S, Karaca M, et al. (2004) Mutation ofGATA3 in human breast tumors. Oncogene 23:7669–7678.

3.

Brown RL, Reinke LM, Damerow MS, Perez D, Chodosh LA, et al. (2011)
CD44 splice isoform switching in human and mouse epithelium is essential
for epithelial-mesenchymal transition and breast cancer progression.
J Clin Invest 121: 1064–1074.

Q: Do you anticipate increasing demand for your mRNA-Seq services?
WJ: I think the data are very compelling and we’re doing a good jobof eliminating perceived barriers by providing researchers with a data
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Pub. No. 070-2011-034 Current as of 12 December 2011