Quantitative miRNA expression analysis: Comparing microarrays with next-generation sequencing

Willenbrock, Hanni; Salomon, Jesper; Søkilde, Rolf; Barken, Kim Bundvig; Hansen, Thomas N.; Nielsen, Finn Cilius; Möller, Sören; Litman, Thomas

doi:10.1261/rna.1699809

Cited by 145 publications

(130 citation statements)

References 12 publications

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“…Omission of ''obsolete'' or ''not_designed_for_hsa'' Exiqon probes resulted in minimal changes to these numbers (60.2 in optimal fold-change cutoff and 60.04 in TP/TN rates; data not shown). The low sensitivity (TP) of GAseq contradicts the commonly expressed expectation of digital miRNA profiling and was also recently reported in a comparative study using a pool of synthetic RNAs (Willenbrock et al 2009). …”

Section: Correlation With Qpcr Resultsmentioning

confidence: 91%

Systematic comparison of microarray profiling, real-time PCR, and next-generation sequencing technologies for measuring differential microRNA expression

Git

Dvinge²,

Salmon‐Divon³

et al. 2010

RNA

607

453

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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.

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Section: Correlation With Qpcr Resultsmentioning

confidence: 91%

Systematic comparison of microarray profiling, real-time PCR, and next-generation sequencing technologies for measuring differential microRNA expression

Git

Dvinge²,

Salmon‐Divon³

et al. 2010

RNA

607

453

View full text Add to dashboard Cite

show abstract

“…Previously, it has been reported that absolute microarray expression measures correlate better than deep-sequencing data with RNA samplecontent when synthetic samples mimicking the human miRNA pool are used (Willenbrock et al, 2009). However, the correlation between absolute miRNA expression values determined by microarrays and by next-generation sequencing may differ strongly depending on the platform used for microarray profiling (Git et al, 2010), or on the algorithm used for processing of sequencing data.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic and prognostic signatures from the small non-coding RNA transcriptome in prostate cancer

et al. 2011

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Prostate cancer (PCa) is the most frequent male malignancy and the second most common cause of cancer-related death in Western countries. Current clinical and pathological methods are limited in the prediction of postoperative outcome. It is becoming increasingly evident that small non-coding RNA (ncRNA) species are associated with the development and progression of this malignancy. To assess the diversity and abundance of small ncRNAs in PCa, we analyzed the composition of the entire small transcriptome by Illumina/ Solexa deep sequencing. We further analyzed the micro-RNA (miRNA) expression signatures of 102 fresh-frozen patient samples during PCa progression by miRNA microarrays. Both platforms were cross-validated by quantitative reverse transcriptase-PCR. Besides the altered expression of several miRNAs, our deep sequencing analyses revealed strong differential expression of small nucleolar RNAs (snoRNAs) and transfer RNAs (tRNAs). From microarray analysis, we derived a miRNA diagnostic classifier that accurately distinguishes normal from cancer samples. Furthermore, we were able to construct a PCa prognostic predictor that independently forecasts postoperative outcome. Importantly, the majority of miRNAs included in the predictor also exhibit high sequence counts and concordant differential expression in Illumina PCa samples, supported by quantitative reverse transcriptase-PCR. Our findings provide miRNA expression signatures that may serve as an accurate tool for the diagnosis and prognosis of PCa.

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“…Recently, there have been several investigations [13][14][15] into the biases that affect the accuracy with which RNA-Seq represents the absolute abundance of a given transcript as measured by high precision approaches such as Taqman RT-PCR [16]. It has been shown that these abundance measures are prone to biases correlated with the nucleotide composition [14,17] and length of the transcript [1,18].…”

mentioning

confidence: 99%

Development in Rice Genome Research Based on Accurate Genome Sequence

2014

The Role of Bioinformatics in Agriculture

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Background: RNA sequencing (RNA-Seq) has emerged as a powerful approach for the detection of differential gene expression with both high-throughput and high resolution capabilities possible depending upon the experimental design chosen. Multiplex experimental designs are now readily available, these can be utilised to increase the numbers of samples or replicates profiled at the cost of decreased sequencing depth generated per sample. These strategies impact on the power of the approach to accurately identify differential expression. This study presents a detailed analysis of the power to detect differential expression in a range of scenarios including simulated null and differential expression distributions with varying numbers of biological or technical replicates, sequencing depths and analysis methods. Results: Differential and non-differential expression datasets were simulated using a combination of negative binomial and exponential distributions derived from real RNA-Seq data. These datasets were used to evaluate the performance of three commonly used differential expression analysis algorithms and to quantify the changes in power with respect to true and false positive rates when simulating variations in sequencing depth, biological replication and multiplex experimental design choices. Conclusions: This work quantitatively explores comparisons between contemporary analysis tools and experimental design choices for the detection of differential expression using RNA-Seq. We found that the DESeq algorithm performs more conservatively than edgeR and NBPSeq. With regard to testing of various experimental designs, this work strongly suggests that greater power is gained through the use of biological replicates relative to library (technical) replicates and sequencing depth. Strikingly, sequencing depth could be reduced as low as 15% without substantial impacts on false positive or true positive rates.

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Quantitative miRNA expression analysis: Comparing microarrays with next-generation sequencing

Cited by 145 publications

References 12 publications

Systematic comparison of microarray profiling, real-time PCR, and next-generation sequencing technologies for measuring differential microRNA expression

Systematic comparison of microarray profiling, real-time PCR, and next-generation sequencing technologies for measuring differential microRNA expression

Diagnostic and prognostic signatures from the small non-coding RNA transcriptome in prostate cancer

Development in Rice Genome Research Based on Accurate Genome Sequence

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