Deciphering the RNA landscape by RNAome sequencing

Current RNA expression profiling methods rely on enrichment steps for specific RNA classes, thereby not detecting all RNA species in an unperturbed manner. We report strand-specific RNAome sequencing that determines expression of small and large RNAs from rRNA-depleted total RNA in a single sequence...

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Autores:
Tipo de recurso:
Fecha de publicación:
2015
Institución:
Universidad del Rosario
Repositorio:
Repositorio EdocUR - U. Rosario
Idioma:
eng
OAI Identifier:
oai:repository.urosario.edu.co:10336/23257
Acceso en línea:
https://doi.org/10.1080/15476286.2015.1017202
https://repository.urosario.edu.co/handle/10336/23257
Palabra clave:
Cisplatin
Long untranslated rna
Messenger rna
Microrna
Polyadenylated rna
Ribosome rna
Rna
Small nucleolar rna
Rna
Transcriptome
Algorithm
Animal cell
Apoptosis
Article
Controlled study
Dna library
Embryo
Embryonic stem cell
Enhancer region
Gene expression
Gene repression
Genetic code
Genetic transcription
Microarray analysis
Mouse
Nonhuman
Quantitative analysis
Rna analysis
Rna isolation
Rna sequence
Rna synthesis
Animal
High throughput sequencing
Human
Metabolism
Procedures
Sequence analysis
Animals
High-throughput nucleotide sequencing
Humans
Mice
Rna
Transcriptome
Non-coding rna
Rna abundance
Rna expression
Rnaome
Strand-specific rna-sequencing
Whole transcriptome
rna
Sequence analysis
Rights
License
Abierto (Texto Completo)
Description
Summary:Current RNA expression profiling methods rely on enrichment steps for specific RNA classes, thereby not detecting all RNA species in an unperturbed manner. We report strand-specific RNAome sequencing that determines expression of small and large RNAs from rRNA-depleted total RNA in a single sequence run. Since current analysis pipelines cannot reliably analyze small and large RNAs simultaneously, we developed TRAP, Total Rna Analysis Pipeline, a robust interface that is also compatible with existing RNA sequencing protocols. RNAome sequencing quantitatively preserved all RNA classes, allowing cross-class comparisons that facilitates the identification of relationships between different RNA classes. We demonstrate the strength of RNAome sequencing in mouse embryonic stem cells treated with cisplatin. MicroRNA and mRNA expression in RNAome sequencing significantly correlated between replicates and was in concordance with both existing RNA sequencing methods and gene expression arrays generated from the same samples. Moreover, RNAome sequencing also detected additional RNA classes such as enhancer RNAs, anti-sense RNAs, novel RNA species and numerous differentially expressed RNAs undetectable by other methods. At the level of complete RNA classes, RNAome sequencing also identified a specific global repression of the microRNA and microRNA isoform classes after cisplatin treatment whereas all other classes such as mRNAs were unchanged. These characteristics of RNAome sequencing will significantly improve expression analysis as well as studies on RNA biology not covered by existing methods. © Kasper WJ Derks, Branislav Misovic, Mirjam CGN van den Hout, Christel EM Kockx, Cesar Payan Gomez, Rutger WW Brouwer, Harry Vrieling, Jan HJ Hoeijmakers, Wilfred FJ van IJcken, and Joris Pothof.