Limitations and possibilities of small RNA digital gene expression profiling

Linsen, Sam E.V.; Wit, Elzo de; Janssens, Georges E.; Heater, Sheila; Chapman, Laura; Parkin, Rachael K.; Fritz, Brian R.; Wyman, Stacia K.; Bruijn, Ewart de; Voest, Emile E.; Kuersten, Scott; Tewari, Muneesh; Cuppen, Edwin

doi:10.1038/nmeth0709-474

Cited by 264 publications

(273 citation statements)

References 8 publications

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“…At present, however, standard normalization methods for non-direct RNA-seq are not sufficient to remove RNA composition bias because relying on library size (Robinson and Oshlack 2010). Systematic technical bias such as RNA ligase preferences, reverse transcription reaction, and PCR based amplification during library preparation are likely to contribute to bias in RNA-seq experiments (Linsen et al 2009). …”

Section: Normalization Techniques For Small Rna-seq Datamentioning

confidence: 99%

“…Small RNA-seq approaches relying on cDNA library-construction are strongly biased towards certain small RNAs (Linsen et al 2009) largely independent of the sequencing platform but strongly determined by the method used for small RNA library preparation. However, a satisfactory correction model has not yet been identified (Linsen et al 2009).…”

Section: Normalization Techniques For Small Rna-seq Datamentioning

confidence: 99%

“…However, a satisfactory correction model has not yet been identified (Linsen et al 2009). As these biases are systematic and highly reproducible (Marioni et al 2008), RNA-seq may be suited for determining relative expression differences between samples (Linsen et al 2009). …”

Section: Normalization Techniques For Small Rna-seq Datamentioning

confidence: 99%

“…Using this method reads are divided by the total number of small RNA-seq reads aligning to the genome (Marques et al 2010). Alternatively, the relative frequency of miRNAs is determined by normalizing miRNA reads against the total count of 18 to 22 nucleotide reads or total number of reads that map to known miRNAs, respectively (Zhang et al 2009;Linsen et al 2009;Git et al 2010). Normalized data is then reported as reads (or transcripts) per million for each respective library.…”

Section: Normalization Techniques For Small Rna-seq Datamentioning

confidence: 99%

“…a valuable tool for biomarker and therapeutic target identification and functional prediction of miRNAs by correlating miRNA expression patterns to corresponding mRNA (Jayaswal et al 2009;Manakov et al 2009) and protein profiles (Iliopoulos et al 2008). MiRNA profiling strategies based on deep sequencing (reviewed in Linsen et al 2009) allow the de novo identification as well as the relative quantification of miRNAs and are essential for getting a holistic picture of small RNA signatures. Each miRNA profiling strategy faces unique challenges.…”

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confidence: 99%

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Normalization strategies for microRNA profiling experiments: a ‘normal’ way to a hidden layer of complexity?

2010

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To cite this version:Swanhild U. Meyer, Michael W. Pfaffl, Susanne E. Ulbrich. Normalization strategies for microRNA profiling experiments: a 'normal' way to a hidden layer of complexity?. Biotechnology Letters, Springer Verlag, 2010, 32 (12) review provides a critical overview of commonly used and newly developed normalization methods for miRNA RT-qPCR, miRNA hybridization microarray, and small RNA-seq datasets. Emphasis is laid on the complexity, the importance and the potential for further optimization of normalization techniques for miRNA profiling datasets.

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Section: Normalization Techniques For Small Rna-seq Datamentioning

confidence: 99%

Section: Normalization Techniques For Small Rna-seq Datamentioning

confidence: 99%

Section: Normalization Techniques For Small Rna-seq Datamentioning

confidence: 99%

Section: Normalization Techniques For Small Rna-seq Datamentioning

confidence: 99%

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confidence: 99%

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Normalization strategies for microRNA profiling experiments: a ‘normal’ way to a hidden layer of complexity?

2010

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Genome‐Wide Annotation and Quantitation of Translation by Ribosome Profiling

Ingolia

Brar

Rouskin

et al. 2013

CP Molecular Biology

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Recent studies highlight the importance of translational control in determining protein abundance, underscoring the value of measuring gene expression at the level of translation. We present a protocol for genome-wide, quantitative analysis of in vivo translation by deep sequencing. This ribosome profiling approach maps the exact positions of ribosomes on transcripts by nuclease footprinting. The nuclease-protected mRNA fragments are converted into a DNA library suitable for deep sequencing using a strategy that minimizes bias. The abundance of different footprint fragments in deep sequencing data reports on the amount of translation of a gene. Additionally, footprints reveal the exact regions of the transcriptome that are translated. To better define translated reading frames, we describe an adaptation that reveals the sites of translation initiation by pre-treating cells with harringtonine to immobilize initiating ribosomes. The protocol we describe requires 5 – 7 days to generate a completed ribosome profiling sequencing library. Sequencing and data analysis requires a further 4 – 5 days.

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Next‐Generation Sequencing RNA‐Seq Library Construction

Podnar

Deiderick

Huerta

et al. 2014

CP Molecular Biology

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This unit presents protocols for construction of next-generation sequencing (NGS) directional RNA sequencing libraries for the Illumina HiSeq and MiSeq from a wide variety of input RNA sources. The protocols are based on the New England Biolabs (NEB) small RNA library preparation set for Illumina, although similar kits exist from different vendors. The protocol preserves the orientation of the original RNA in the final sequencing library, enabling strand-specific analysis of the resulting data. These libraries have been used for differential gene expression analysis and small RNA discovery and are currently being tested for de novo transcriptome assembly. The protocol is robust and applicable to a broad range of RNA input types and RNA quality, making it ideal for high-throughput laboratories.

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Limitations and possibilities of small RNA digital gene expression profiling

Cited by 264 publications

References 8 publications

Normalization strategies for microRNA profiling experiments: a ‘normal’ way to a hidden layer of complexity?

Normalization strategies for microRNA profiling experiments: a ‘normal’ way to a hidden layer of complexity?

Genome‐Wide Annotation and Quantitation of Translation by Ribosome Profiling

Next‐Generation Sequencing RNA‐Seq Library Construction

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