Dataset Information

Deciphering the RNA landscape by RNAome sequencing.

ABSTRACT: 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.

SUBMITTER: Derks KW

PROVIDER: S-EPMC4615683 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Deciphering the RNA landscape by RNAome sequencing.

Derks Kasper W J KW Misovic Branislav B van den Hout Mirjam C G N MC Kockx Christel E M CE Gomez Cesar Payan CP Brouwer Rutger W W RW Vrieling Harry H Hoeijmakers Jan H J JH van IJcken Wilfred F J WF Pothof Joris J

RNA biology 20150101 1

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 exis ...[more]

PMID: 25826412

Similar Datasets

Project description:Simple Summary Survival rates for children with acute myeloid leukemia have significantly improved in recent decades. Still, 20–30% will succumb to therapy-related toxicity and relapse. Research has shown that leukemic stem cells (LSCs) in particular are responsible for relapse. Therefore, the characterization of the LSC is needed to improve the current treatment options for and survival of these pediatric patients. Previously, research has focused mainly on the protein-coding part of the human genome, but recently the focus has also shifted to non-coding genes, including long non-coding RNA (lncRNA) and microRNA (miRNA). The aim of this study was to investigate the expression of lncRNAs and miRNAs in pediatric AML subpopulations (LSCs and leukemic blasts) and their normal counterparts (hematopoietic stem cells and control myeloblasts). We provide here a unique set of non-coding RNAs with a potential role in the pathogenesis of pediatric AML, paving the way for further translational research studies. Abstract Pediatric acute myeloid leukemia (pedAML) is a heterogeneous blood cancer that affects children. Although survival rates have significantly improved over the past few decades, 20–30% of children will succumb due to treatment-related toxicity or relapse. The molecular characterization of the leukemic stem cell, shown to be responsible for relapse, is needed to improve treatment options and survival. Recently, it has become clear that non-coding RNAs, including long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), play a role in the development of human diseases, including pediatric cancer. Nevertheless, non-coding RNA expression data in pedAML are scarce. Here, we explored lncRNA (n = 30,168) and miRNA (n = 627) expression in pedAML subpopulations (leukemic stem cells (LSCs) and leukemic blasts (L-blasts)) and their normal counterparts (hematopoietic stem cells and control myeloblasts). The potential regulatory activity of differentially expressed lncRNAs in LSCs (unique or shared with the L-blast comparison) on miRNAs was assessed. Moreover, pre-ranked gene set enrichment analyses of (anti-) correlated protein-coding genes were performed to predict the functional relevance of the differentially upregulated lncRNAs in LSCs (unique or shared with the L-blast comparison). In conclusion, this study provides a catalog of non-coding RNAs with a potential role in the pathogenesis of pedAML, paving the way for further translational research studies.

Dataset Information

Deciphering the RNA landscape by RNAome sequencing.

Publications

Deciphering the RNA landscape by RNAome sequencing.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets