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microRNA quality and effects on quantification ... NEW ! microRNA normalisation in real-time qRT-PCR Data normalisation in microRNA experiments using qRT-PCR is a new challenge in gene quantification analysis. The reliability of any relative RT-PCR experiment can be improved by including an invariant endogenous control (reference gene) in the assay to correct for sample to sample variations in the qRT-PCR efficiency and errors in sample quantification. A biologically meaningful reporting of target mRNA copy numbers requires accurate and relevant normalisation to some standard and is strongly recommended in microRNA qRT-PCR. => But the
quality of normalized quantitative expression data cannot be better
than the quality of the normalizer itself. Any variation in the normalizer will obscure real changes and produce artifactual changes. Real-time RT-PCR-specific errors in the quantification of microRNA transcripts are easily compounded with any variation in the amount of starting material between the samples, e.g. caused by sample-to-sample variation and cDNA sample loading variation. This is especially relevant when the samples have been obtained from different individuals, different tissues and different time courses, and will result in the misinterpretation of the derived expression profile of the target genes. => Therefore, normalisation of target gene expression levels must be performed to compensate intra- and inter-kinetic RT-PCR variations (sample-to-sample & run-to-run variations). Data normalisation can be carried out against one or more endogenous unregulated reference gene transcript or against total cellular DNA or RNA content (molecules/g total DNA/RNA and concentrations/g total DNA/RNA). Normalisation according the total cellular RNA content is increasingly used, but little is known about the total RNA content of cells or even about the microRNA or mRNA concentrations. The content per cell or per gram tissue may vary in different tissues in vivo, in cell culture (in vitro), between individuals and under different experimental conditions. Nevertheless, it has been shown that normalisation to total cellular RNA is the least unreliable method. It requires an accurate quantification of the isolated total RNA or mRNA or microRNA fraction by optical density at 260 nm, Lab-on-Chip capillary electrophoresis instruments, or Ribogreen RNA Quantification Kit. To normalize the absolute amount according to a single reference gene (or better a set of multiple stable reference genes), further sets of kinetic PCR reactions has to be performed for the invariant endogenous control(s) on all experimental samples and the relative abundance values are calculated for internal control as well as for the target gene. For each target gene sample, the relative abundance value obtained is divided by the value derived from the control sequence in the corresponding target gene. The normalized values for different biological samples can then directly be compared. The workflow:
TALK - Better appreciation of true biological miRNA expression differences using an improved version of the global mean normalization strategy by Jo Vandesompele, RNAi and miRNA world congress, Boston 2011 Molecular Cancer 2006, 5:29 E Bandrés*1, E Cubedo1, X Agirre2, R Malumbres1, R Zárate1, N Ramirez1, A Abajo1, A Navarro3, I Moreno4, M Monzó3 and J García-Foncillas1 MicroRNAs (miRNAs)
are short non-coding RNA molecules playing regulatory roles by
repressing translation or
cleaving RNA transcripts. Although the number of verified human miRNA
is still expanding, only few
have been functionally described. However, emerging evidences suggest
the potential
involvement of altered regulation of miRNA in pathogenesis of cancers
and these genes are thought to
function as both tumours suppressor and oncogenes. In our study, we
examined by Real-Time PCR the expression of 156 mature miRNA in
colorectal cancer. The analysis
by several bioinformatics algorithms of colorectal tumours and adjacent
nonneoplastic tissues from patients
and colorectal cancer cell lines allowed identifying a group of 13 miRNA whose
expression is significantly altered in this tumor. The most
significantly deregulated miRNA being miR-31,
miR-96, miR-133b, miR-135b, miR-145, and miR-183. In addition, the expression level of
miR-31 was correlated with the stage of CRC tumor. Our results suggest
that miRNA expression profile could have relevance to the biological
and clinical
behavior of colorectal neoplasia.
Expression
profiling of microRNA using real-time quantitative PCR, how to use it
and what is available.
We review different methodologies to estimate the
expression levels of microRNAs (miRNAs) using real-time quantitative
PCR (qPCR). As miRNA analysis is a fast changing research field, we
have introduced novel technological approaches and compared them to
existing qPCR profiling methodologies. qPCR also remains the method of
choice for validating results obtained from whole-genome screening
(e.g. with microarray). In contrast to presenting a stepwise
description of different platforms, we discuss expression profiling of
mature miRNAs by qPCR in four sequential sections: (1) cDNA synthesis;
(2) primer design; (3) detection of amplified products; and (4) data
normalization. We address technical challenges associated with each of
these and outline possible solutions.Benes V, Castoldi M. European Molecular Biology Laboratory, Heidelberg D 69117, Germany. Methods. 2010 Apr;50(4): 244-249 A novel and universal method for microRNA
RT-qPCR data normalization.
Gene expression analysis of microRNA molecules is becoming
increasingly important. In this study we assess the use of the mean
expression value of all expressed microRNAs in a given sample as a
normalization factor for microRNA real-time quantitative PCR data and
compare its performance to the currently adopted approach. We
demonstrate that the mean expression value outperforms the current
normalization strategy in terms of better reduction of technical
variation and more accurate appreciation of biological changes.Mestdagh P, Van Vlierberghe P, De Weer A, Muth D, Westermann F, Speleman F, Vandesompele J. Center for Medical Genetics, Ghent University Hospital, De Pintelaan 185, Ghent, Belgium. pieter.mestdagh@ugent.be Genome Biol. 2009;10(6): R64 Systematic
comparison of microarray profiling, real-time PCR, and next-generation
sequencing technologies
RNA abundance and DNA copy number are routinely measured
in high-throughput using microarray and next-generation sequencing
(NGS) technologies, and the attributes of different platforms have been
extensively analyzed. Recently, the application of both microarrays and
NGS has expanded to include microRNAs (miRNAs), but the relative
performance of these methods has not been rigorously characterized. We
analyzed three biological samples across six miRNA microarray platforms
and compared their hybridization performance. We examined the utility
of these platforms, as well as NGS, for the detection of differentially
expressed miRNAs. We then validated the results for 89 miRNAs by
real-time RT-PCR and challenged the use of this assay as a "gold
standard." Finally, we implemented a novel method to evaluate
false-positive and false-negative rates for all methods in the absence
of a reference method.for measuring differential microRNA expression. Git A, Dvinge H, Salmon-Divon M, Osborne M, Kutter C, Hadfield J, Bertone P, Caldas C. Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre, Cambridge CB2 0RE, United Kingdom RNA. 2010 May;16(5): 991-1006 A
modified LOESS normalization applied to microRNA arrays: a comparative
evaluation.
MOTIVATION: Microarray normalization is a fundamental step
in removing systematic bias and noise variability caused by technical
and experimental artefacts. Several approaches, suitable for
large-scale genome arrays, have been proposed and shown to be effective
in the reduction of systematic errors. Most of these methodologies are
based on specific assumptions that are reasonable for whole-genome
arrays, but possibly unsuitable for small microRNA (miRNA) platforms.
In this work, we propose a novel normalization (loessM), and we
investigate, through simulated and real datasets, the influence that
normalizations for two-colour miRNA arrays have on the identification
of differentially expressed genes. RESULTS: We show that normalizations
usually applied to large-scale arrays, in several cases, modify the
actual structure of miRNA data, leading to large portions of false
positives and false negatives. Nevertheless, loessM is able to
outperform other techniques in most experimental scenarios. Moreover,
when usual assumptions on differential expression distribution are
missed, channel effect has a strikingly negative influence on small
arrays, bias that cannot be removed by normalizations but rather by an
appropriate experimental design. We find that the combination of loessM
with eCADS, an experimental design based on biological replicates
dye-swap recently proposed for channel-effect reduction, gives better
results in most of the experimental conditions in terms of
specificity/sensitivity both on simulated and real data.Risso D, Massa MS, Chiogna M, Romualdi C. Department of Statistical Sciences, University of Padova, via C. Battisti 241 and Department of Biology, University of Padova, via U. Bassi 58/B, 35121 Padova, Italy. Bioinformatics. 2009 25(20): 2685-2691 AVAILABILITY: LoessM R function is freely available at http://gefu.cribi.unipd.it/papers/miRNA-simulation/ Improved
microRNA quantification in total RNA from clinical samples.
microRNAs are small regulatory RNAs that are currently
emerging as new biomarkers for cancer and other diseases. In order for
biomarkers to be useful in clinical settings, they should be accurately
and reliably detected in clinical samples such as formalin fixed
paraffin embedded (FFPE) sections and blood serum or plasma. These
types of samples represent a challenge in terms of microRNA
quantification. A newly developed method for microRNA qPCR using Locked
Nucleic Acid (LNA)-enhanced primers enables accurate and reproducible
quantification of microRNAs in scarce clinical samples. Here we show
that LNA-based microRNA qPCR enables biomarker screening using very low
amounts of total RNA from FFPE samples and the results are compared to
microarray analysis data. We also present evidence that the addition of
a small carrier RNA prior to total RNA extraction, improves microRNA
quantification in blood plasma and laser capture microdissected (LCM)
sections of FFPE samples.Andreasen D, Fog JU, Biggs W, Salomon J, Dahslveen IK, Baker A, Mouritzen P. Exiqon A/S, Skelstedet 16, DK-2950 Vedbaek, Denmark. ina@exiqon.com Methods. 2010 Apr;50(4): S6-9. Measuring
microRNAs: comparisons of microarray and quantitative PCR measurements,
BACKGROUND: Determining the expression levels of microRNAs
(miRNAs) is of great interest to researchers in many areas of biology,
given the significant roles these molecules play in cellular
regulation. Two common methods for measuring miRNAs in a total RNA
sample are microarrays and quantitative RT-PCR (qPCR). To understand
the results of studies that use these two different techniques to
measure miRNAs, it is important to understand how well the results of
these two analysis methods correlate. Since both methods use total RNA
as a starting material, it is also critical to understand how
measurement of miRNAs might be affected by the particular method of
total RNA preparation used. RESULTS: We measured the expression of 470
human miRNAs in nine human tissues using Agilent microarrays, and
compared these results to qPCR profiles of 61 miRNAs in the same
tissues. Most expressed miRNAs (53/60) correlated well (R > 0.9)
between the two methods. Using spiked-in synthetic miRNAs, we further
examined the two miRNAs with the lowest correlations, and found the
differences cannot be attributed to differential sensitivity of the two
methods. We also tested three widely-used total RNA sample prep methods
using miRNA microarrays. We found that while almost all miRNA levels
correspond between the three methods, there were a few miRNAs whose
levels consistently differed between the different prep techniques when
measured by microarray analysis. These differences were corroborated by
qPCR measurements. CONCLUSION: The correlations between Agilent miRNA
microarray results and qPCR results are generally excellent, as are the
correlations between different total RNA prep methods. However, there
are a few miRNAs whose levels do not correlate between the microarray
and qPCR measurements, or between different sample prep methods.
Researchers should therefore take care when comparing results obtained
using different analysis or sample preparation methods.and of different total RNA prep methods. Ach RA, Wang H, Curry B. Agilent Laboratories, Agilent Technologies, Santa Clara, CA 95051, USA. robert_ach@agilent.com BMC Biotechnol. 2008 8: 69.
Proper normalization is a critical but often an
underappreciated aspect of quantitative gene expression
analysis. This study describes the identification and
characterization of appropriate reference RNA targets for the
normalization of microRNA (miRNA) quantitative RT-PCR
data. miRNA microarray data from dozens of normal and
disease human tissues revealed ubiquitous and stably expressed normalization
candidates for evaluation by qRT-PCR. miR-191 and miR-103, among others,
were found to be highly consistent in their expression across 13 normal
tissues and five pair of distinct tumor/normal
adjacent tissues. These miRNAs were statistically
superior to the most commonly used reference RNAs used in miRNA
qRT-PCR experiments, such as 5S rRNA, U6 snRNA, or total RNA. The most stable
normalizers were also highly conserved across flash-frozen and formalin-fixed
paraffin-embedded lung cancer tumor/NAT sample sets, resulting in the
confirmation of one well-documented oncomir (let-7a), as well as the identification
of novel oncomirs. These findings constitute the first report describing
the rigorous normalization of miRNA qRT-PCR data and have important implications
for proper experimental design and accurate data interpretation.
Identification of suitable endogenous control genes for microRNA gene expression analysis in human breast cancer. Davoren PA, McNeill RE, Lowery AJ, Kerin MJ, Miller N. Department of Surgery, National University of Ireland, Galway, Ireland. BMC Mol Biol. 2008 9: 76. The discovery of
microRNAs (miRNAs) added an extra level of intricacy to the already
complex system regulating gene expression. These single-stranded RNA molecules,
18-25 nucleotides in length, negatively regulate gene expression through
translational inhibition or mRNA cleavage. The discovery that aberrant expression
of specific miRNAs contributes to human disease has fueled much interest
in profiling the expression of these molecules. Real-time quantitative PCR
(RQ-PCR) is a sensitive and reproducible gene expression quantitation technique
which is now being used to profile miRNA expression in cells and tissues.
To correct for systematic variables such as amount of starting
template, RNA quality and enzymatic efficiencies,
RQ-PCR data is commonly normalised to an endogenous
control (EC) gene, which ideally, is stably-expressed across the test sample
set. A universal endogenous control suitable for every tissue type, treatment
and disease stage has not been identified and is unlikely to exist, so,
to avoid introducing further error in the
quantification of expression data it is necessary that
candidate ECs be validated in the samples of interest. While ECs have
been validated for quantification of mRNA expression in various
experimental settings, to date there is no report of
the validation of miRNA ECs for expression profiling in
breast tissue. In this study, the expression of five miRNA
genes (let-7a, miR-10b, miR-16, miR-21 and miR-26b) and three small nucleolar
RNA genes (RNU19, RNU48 and Z30) was examined across malignant, benign and
normal breast tissues to determine the most appropriate normalisation strategy.
This is the first study to identify reliable ECs for analysis of miRNA by
RQ-PCR in human breast tissue.
High-throughput stem-loop RT-qPCR miRNA expression profiling using minute amounts of input RNA. Mestdagh P, Feys T, Bernard N, Guenther S, Chen C, Speleman F, Vandesompele J. Center for Medical Genetics, Ghent University Hospital, Ghent, Belgium. Nucleic Acids Res. 2008 36(21): e143 MicroRNAs (miRNAs) are
an emerging class of small non-coding RNAs implicated in a wide
variety of cellular processes. Research in this field is accelerating,
and the growing number of miRNAs emphasizes the need
for high-throughput and sensitive detection methods.
Here we present the successful evaluation of the Megaplex
reverse transcription format of the stem-loop primer-based real-time quantitative
polymerase chain reaction (RT-qPCR) approach to quantify miRNA expression.
The Megaplex reaction provides simultaneous reverse transcription of 450
mature miRNAs, ensuring high-throughput detection. Further, the
introduction of a complementary DNA pre-amplification
step significantly reduces the amount of input RNA
needed, even down to single-cell level. To evaluate possible pre-amplification
bias, we compared the expression of 384 miRNAs in three different
cancer cell lines with Megaplex RT, with or without an additional pre-amplification
step. The normalized Cq values of all three sample pairs showed a
good correlation with maintenance of differential miRNA expression
between the cell lines. Moreover, pre-amplification
using 10 ng of input RNA enabled the detection of
miRNAs that were undetectable when using Megaplex alone with 400 ng of
input RNA. The high specificity of RT-qPCR together with a superior sensitivity
makes this approach the method of choice for high-throughput miRNA expression
profiling.
Facile
means for quantifying microRNA expression by real-time PCR.
Shi R, Chiang VL. North Carolina State University, Raleigh, NC 27695-7247, USA Biotechniques. 2005 39(4): 519-525  MicroRNAs (miRNAs) are 20-24 nucleotide RNAs that are predicted to play regulatory roles in animals and plants. Here we report a simple and sensitive real-time PCR method for quantifying the expression of plant miRNAs. Total RNA, including miRNAs, was polyadenylated and reverse-transcribed with a poly(T) adapter into cDNAs for real-time PCR using the miRNA-specific forward primer and the sequence complementary to the poly(T) adapter as the reverse primer. Several Arabidopsis miRNA sequences were tested using SYBR Green reagent, demonstrating that this method, using as little as 100 pg total RNA, could readily discriminate the expression of miRNAs having asfew as one nucleotide sequence difference. This method also revealed miRNA tissue-specific expression patterns that cannot be resolved by Northern blot analysis and may therefore be widely useful for characterizing miRNA expression in plants as well as in animals. A
single-molecule method for the quantitation of microRNA gene expression.
Neely LA, Patel S, Garver J, Gallo M, Hackett M, McLaughlin S, Nadel M, Harris J, Gullans S, Rooke J. US Genomics, 12 Gill Street, Suite 4700, Woburn, Massachusetts 01801, USA Nat Methods. 2006 (1): 41-46 MicroRNAs (miRNA) are short endogenous noncoding RNA molecules that regulate fundamental cellular processes such as cell differentiation, cell proliferation and apoptosis through modulation of gene expression. Critical to understanding the role of miRNAs in this regulation is a method to rapidly and accurately quantitate miRNA gene expression. Existing methods lack sensitivity, specificity and typically require upfront enrichment, ligation and/or amplification steps. The Direct miRNA assay hybridizes two spectrally distinguishable fluorescent locked nucleic acid (LNA)-DNA oligonucleotide probes to the miRNA of interest, and then tagged molecules are directly counted on a single-molecule detection instrument. In this study, we show the assay is sensitive to femtomolar concentrations of miRNA (500 fM), has a three-log linear dynamic range and is capable of distinguishing among miRNA family members. Using this technology, we quantified expression of 45 human miRNAs within 16 different tissues, yielding a quantitative differential expression profile that correlates and expands upon published results. Endogenous Controls for Real-Time Quantitation of miRNA Using TaqMan® MicroRNA Assays. Applied Biosystems - Application Note MicroRNAs
(miRNAs) are small noncoding RNAs whose function has been implicated in
a wide range of fundamental cellular processes including cell
proliferation, cell differentiation, and cell death. Quantitation of
miRNA gene expression levels has become an essential step in
understanding these mechanisms, and has shown great promise in
identifying effective biomarkers correlative with human disease1,2.
Applied Biosystems has developed an extensive set of TaqMan®
MicroRNA Assays, novel stem-loop RT and real-time PCR assays, for the
quantitation of mature miRNA expression3. TaqMan® Assays are the
ideal choice for these applications because of their unsurpassed
sensitivity, specificity, and wide dynamic range. Additionally, far
less input material is required compared to microarrays and other
alternative technologies. When performing these experiments,variation
in the amount of starting material, sample collection, RNA preparation
and quality, and reverse transcription (RT) efficiency can contribute
to quantification errors. Normalization to endogenous control genes is
currently the most accurate method to correct for potential RNA input
or RT efficiency biases. Careful selection of an appropriate control or
set of controls is extremely important as significant variation has
been observed between samples, even for the most commonly used
housekeeping genes, including ACTB (ß-Actin) and GAPDH4. An ideal
endogenous control generally demonstrates gene expression that is
relatively constant and highly abundant across tissues and cell types.
However, one must still validate the chosen endogenous control or set
of controls for the target cell, tissue, or treatment5, as no single
control can serveas a universal endogenous control for
all experimental conditions. When considering endogenous controls
suitable for use with TaqMan MicroRNA Assays, it is important that they
share similar properties, such as RNA stability and size, and are
amenable to the miRNA assay design. A number of reports indicate that
other classes of small non-coding RNAs (ncRNAs) are expressed both
abundantly and stably, making them good endogenous control candidates.
We have performed a systematic study of a set of human ncRNA species
ranging in size from 45 to 200 nucleotides, including transfer RNA
(tRNA), small nuclear RNA (snRNA) and small nucleolar RNA (snoRNA) 6
across a relatively wide variety of tissues and cell lines to determine
their suitability as endogenous controls when quantitating miRNA
expression levels using real-time PCR methods.
microRNA normalisation of microRNA arrays Quality
assessment and data analysis for microRNA expression arrays.
MicroRNAs are small (approximately 22 nt) RNAs that
regulate gene expression and play important roles in both normal and
disease physiology. The use of microarrays for global characterization
of microRNA expression is becoming increasingly popular and has the
potential to be a widely used and valuable research tool. However,
microarray profiling of microRNA expression raises a number of data
analytic challenges that must be addressed in order to obtain reliable
results. We introduce here a universal reference microRNA reagent set
as well as a series of nonhuman spiked-in synthetic microRNA controls,
and demonstrate their use for quality control and between-array
normalization of microRNA expression data. We also introduce diagnostic
plots designed to assess and compare various normalization methods. We
anticipate that the reagents and analytic approach presented here will
be useful for improving the reliability of microRNA microarray
experiments.Sarkar D, Parkin R, Wyman S, Bendoraite A, Sather C, Delrow J, Godwin AK, Drescher C, Huber W, Gentleman R, Tewari M. Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA 98109, USA. Nucleic Acids Res. 2009 Feb;37(2):e17 A
comparison of normalization techniques for microRNA microarray data.
Rao Y, Lee Y, Jarjoura D, Ruppert AS, Liu CG, Hsu JC, Hagan JP. The Ohio State University, USA. Stat Appl Genet Mol Biol. 2008;7(1): Article 22 Normalization of
expression levels applied to microarray data can help in reducing
measurement error. Different methods, including cyclic loess, quantile
normalization and median or mean normalization, have been utilized to
normalize microarray data. Although there is considerable literature
regarding normalization techniques for mRNA microarray data, there are
no publications comparing normalization techniques for microRNA (miRNA)
microarray data, which are subject to similar sources of measurement
error. In this paper, we compare the performance of cyclic loess,
quantile normalization, median normalization and no
normalization for a single-color microRNA microarray dataset. We show
that the quantile normalization method works best in reducing
differences in miRNA expression values for replicate tissue samples. By
showing that the total mean squared error are lowest across almost all
36 investigated tissue samples, we are assured that the bias correction
provided by quantile normalization is not outweighed by additional
error variance that can arise from a more complex normalization method.
Furthermore, we show that quantile normalization does not achieve these
results by compression of scale.
A
sensitive array for microRNA expression profiling (miChip) based on
locked nucleic acids (LNA).
MicroRNAs represent a class of short (approximately 22
nt), noncoding regulatory RNAs involved in development,
differentiation, and metabolism. We describe a novel
microarray platform for genome-wide profiling of mature miRNAs (miChip)
using locked nucleic acid (LNA)-modified capture
probes. The biophysical properties of LNA were
exploited to design probe sets for uniform, high-affinity hybridizations
yielding highly accurate signals able to discriminate between single
nucleotide differences and, hence, between closely related miRNA family
members. The superior detection sensitivity
eliminates the need for RNA size selection and/or
amplification. MiChip will greatly simplify miRNA expression profiling
of biological and clinical samples.
Castoldi M, Schmidt S, Benes V, Noerholm M, Kulozik AE, Hentze MW, Muckenthaler MU. Department of Pediatric Oncology, Hematology and Immunology, University of Heidelberg, Germany. RNA. 2006 12(5): 913-920 miChip:
an array-based method for microRNA expression profiling using locked
nucleic acid capture probes.
Mirco Castoldi, Sabine Schmidt, Vladimir Benes, Matthias W Hentze & Martina U Muckenthaler Nature Protocols 3, - 321 - 329 (2008) MicroRNAs (miRNAs)
represent a class of short (22 nt) noncoding RNAs that control gene
expression post-transcriptionally. Microarray technology is frequently
applied to monitor miRNA expression levels but is challenged by (i) the
short length of miRNAs that offers little sequence for appending
detection molecules; (ii) low copy number of some miRNA; and (iii) a
wide range of predicted melting temperatures (Tm) versus their DNA
complementary sequences. We recently developed a microarray platform
for genome-wide profiling of miRNAs (miChip) by applying locked nucleic
acid (LNA)-modified capture probes. Here, we provide detailed protocols
for the generation of the miChip microarray platform, the preparation
and fluorescent labeling of small RNA containing total RNA, its
hybridization to the immobilized LNA-modified capture probes and the
post-hybridization handling of the microarray. Starting from the intact
tissue sample, the entire protocol takes approx3 d to yield highly
accurate and sensitive data about miRNA expression levels.
A
personalized microRNA microarray normalization method using a logistic
regression model.
Wang B, Wang XF, Howell P, Qian X, Huang K, Riker AI, Ju J, Xi Y. Department of Mathematics and Statistics, University of South Alabama, Mobile, AL 36688, USA. Bioinformatics. 2010 Jan 15;26(2):228-34 AVAILABILITY: Datasets and R package are available at http://gauss.usouthal.edu/publ/logit/ Intra-platform
repeatability and inter-platform comparability of microRNA microarray
technology.
Sato F, Tsuchiya S, Terasawa K, Tsujimoto G. Department of Nanobio Drug Discovery, Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Kyoto, Japan. PLoS One. 2009;4(5):e5540. Epub 2009 May 14. Impact
of normalization on miRNA microarray expression profiling.
Pradervand S, Weber J, Thomas J, Bueno M, Wirapati P, Lefort K, Dotto GP, Harshman K. Lausanne DNA Array Facility, Center for Integrative Genomics, University of Lausanne, CH-1015 Lausanne, Switzerland. RNA. 2009 Mar;15(3):493-501 Comparison
of normalization methods with microRNA microarray.
Hua YJ, Tu K, Tang ZY, Li YX, Xiao HS. Bioinformatics Center, The Center of Functional Genomics, Key Lab of System Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, People's Republic of China. Genomics. 2008 Aug;92(2):122-8. Epub 2008 Jun 2. |