Barcoded cDNA Libraries for miRNA Profiling by Next‐Generation Sequencing

Hafner, Markus; Renwick, Neil; Pena, John; Mihailović, Aleksandra; Tuschl, Thomas

doi:10.1002/9783527647064.ch38

Cited by 2 publications

(2 citation statements)

References 51 publications

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“…Total RNA was extracted from frozen tumor tissue using Trizol reagent according to the manufacturer's instructions (Invitrogen). Small RNA cDNA libraries were prepared from 22 mesenchymal tumors, including 3 MIR143‐NOTCH fusion positive GT1, GT2, and GT19, one myopericytoma (M1), one infantile myofibromatosis, 3 leiomyomas, 12 leiomyosarcomas, and 2 GISTs, as previously described (Hafner et al, ; Italiano et al, ). In 20 μl reactions, 2 μg total RNA was ligated to 100 pmol adenylated 3′ adapter containing a unique pentamer barcode at the 5′ end using 1 μg Rnl2(1–249)K227Q (purified from E. coli containing pET16b‐Rnl2(1–249)K227Q [Addgene, Cambridge, MA]), in 50mM Tris‐HCl, pH 7.6; 10mM MgCl2; 10mM 2‐mercaptoethanol; 0.1 mg/ml acetylated bovine serum albumin (BSA; Sigma‐Aldrich, St. Louis, MO) and 15% DMSO for 16 hr on ice.…”

Section: Methodsmentioning

confidence: 99%

Novel MIR143‐NOTCH fusions in benign and malignant glomus tumors

Mosquera

Sboner

Zhang

et al. 2013

Genes Chromosomes & Cancer

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Glomus tumors (GT) have been classified among tumors of perivascular smooth muscle differentiation, together with myopericytoma, myofibroma/tosis, and angioleiomyoma, based on their morphologic overlap. However, no molecular studies have been carried out to date to investigate their genetic phenotype and to confirm their shared pathogenesis. RNA sequencing was performed in three index cases (GT1, malignant GT; GT2, benign GT and M1, multifocal myopericytoma), followed by FusionSeq data analysis, a modular computational tool developed to discover gene fusions from paired-end RNA-seq data. A gene fusion involving MIR143 in band 5q32 was identified in both GTs with either NOTCH2 in 1p13 in GT1 or NOTCH1 in 9q34 in GT2, but none in M1. After being validated by FISH and RT-PCR, these abnormalities were screened on 33 GTs, 6 myopericytomas, 9 myofibroma/toses, 18 angioleiomyomas and in a control group of 5 sino-nasal hemangiopericytomas. Overall NOTCH2 gene rearrangements were identified in 52% of GT, including all malignant cases and one NF1-related GT. No additional cases showed NOTCH1 rearrangement. As NOTCH3 shares similar functions with NOTCH2 in regulating vascular smooth muscle development, the study group was also investigated for abnormalities in this gene by FISH. Indeed, NOTCH3 rearrangements were identified in 9% of GTs, all present in benign soft tissue GT, one case being fused to MIR143. Only 1/18 angioleiomyomas showed NOTCH2 gene rearrangement, while all the myopericytomas and myofibroma/toses were negative. In summary we describe novel NOTCH1-3 rearrangements in benign and malignant, visceral and soft tissue GTs.

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Section: Methodsmentioning

confidence: 99%

Novel MIR143‐NOTCH fusions in benign and malignant glomus tumors

Mosquera

Sboner

Zhang

et al. 2013

Genes Chromosomes & Cancer

Self Cite

151

118

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“…The small RNA libraries were prepared using a customized protocol that has been carefully developed and extensively calibrated at the Tuschl lab. [37][38][39][40] We used the sequence data on the same set of samples as the benchmark to empirically evaluate the effect of normalization on the Agilent array data and compare the performance of normalization methods.…”

Section: Introductionmentioning

confidence: 99%

An Empirical Evaluation of normalization Methods for MicroRNA Arrays in a Liposarcoma Study

2013

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BackgroundMethods for array normalization, such as median and quantile normalization, were developed for mRNA expression arrays. These methods assume few or symmetric differential expression of genes on the array. However, these assumptions are not necessarily appropriate for microRNA expression arrays because they consist of only a few hundred genes and a reasonable fraction of them are anticipated to have disease relevance.MethodsWe collected microRNA expression profiles for human tissue samples from a liposarcoma study using the Agilent microRNA arrays. For a subset of the samples, we also profiled their microRNA expression using deep sequencing. We empirically evaluated methods for normalization of microRNA arrays using deep sequencing data derived from the same tissue samples as the benchmark.ResultsIn this study, we demonstrated array effects in microRNA arrays using data from a liposarcoma study. We found moderately high correlation between Agilent data and sequence data on the same tumors, with the Pearson correlation coefficients ranging from 0.6 to 0.9. Array normalization resulted in some improvement in the accuracy of the differential expression analysis. However, even with normalization, there is still a significant number of false positive and false negative microRNAs, many of which are expressed at moderate to high levels.ConclusionsOur study demonstrated the need to develop more efficient normalization methods for microRNA arrays to further improve the detection of genes with disease relevance. Until better methods are developed, an existing normalization method such as quantile normalization should be applied when analyzing microRNA array data.

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Barcoded cDNA Libraries for miRNA Profiling by Next‐Generation Sequencing

Cited by 2 publications

References 51 publications

Novel MIR143‐NOTCH fusions in benign and malignant glomus tumors

Novel MIR143‐NOTCH fusions in benign and malignant glomus tumors

An Empirical Evaluation of normalization Methods for MicroRNA Arrays in a Liposarcoma Study

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