Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

RNA Capture-Seq resolves the deep complexity of the human transcriptome (454)

ABSTRACT: Transcriptomic analyses have revealed an unexpected complexity of the human transcriptome, whose breadth and depth exceeds current RNA sequencing capacity 1-3. Here we combine tiling arrays and RNA sequencing technologies, in an approach we term RNA Capture-Seq, to enable the assembly and characterisation of transcripts whose expression level is below the detection limits of conventional sequencing approaches. By this technique we identify novel exons and splicing patterns to even well-studied genes, such as the p53 and Hox loci, and expose widespread, regulated and remarkably complex noncoding transcription in intergenic regions. We show that unassigned tags observed in conventional RNA sequencing datasets are not noise but can derive from rare transcripts fully assembled by RNA Capture-Seq. We expect RNA Capture-Seq to prove an invaluable technique for future research into gene expression and its relationship to phenotypic variation. Application of RNA Capture sequencing from human fibroblasts for identifying rare novel transcripts. Hybridization enhancing oligos are designed to cover the full sequencing adaptor and MID sequences during hybridisation. Enhancing Oligo A 5 CCATCTCATCCCTGCGTGTCTCCGACTCAG/3ddc/ Enhancing Oligo B 5' CCTATCCCCTGTGTGCCTTGGCAGTCTCAG/3ddc/

ORGANISM(S): Homo sapiens

SUBMITTER: Tim Mercer

PROVIDER: E-GEOD-29040 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Targeted RNA sequencing reveals the deep complexity of the human transcriptome.

Mercer Tim R TR Gerhardt Daniel J DJ Dinger Marcel E ME Crawford Joanna J Trapnell Cole C Jeddeloh Jeffrey A JA Mattick John S JS Rinn John L JL

Nature biotechnology 20111113 1

Transcriptomic analyses have revealed an unexpected complexity to the human transcriptome, whose breadth and depth exceeds current RNA sequencing capability. Using tiling arrays to target and sequence select portions of the transcriptome, we identify and characterize unannotated transcripts whose rare or transient expression is below the detection limits of conventional sequencing approaches. We use the unprecedented depth of coverage afforded by this technique to reach the deepest limits of the ...[more]

PMID: 22081020

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Project description:Rationale: Asthma and atopy shares common features including Th2-inflammation. However, impairment of airway function seems to be absent in atopy. Increased understanding of the complex cellular and molecular pathways defining the similarities and differences between asthma and atopy may be achieved by transcriptomic analysis (RNA-Seq). Hypothesis and Aims: As the airway smooth muscle (ASM) layer plays an important role in airway function, we hypothesized that the transcriptomic profile of the ASM layer in endobronchial biopsies is different between atopic asthma patients and atopic healthy controls. First, we examined the differences in transcriptomic profiles of the ASM layer in endobronchial biopsies between atopic mild, steroid-free asthma patients, and atopic and non-atopic healthy controls. Second, we investigated the association between the transcriptomic profiles of the ASM layer and airway function. Methods: This cross-sectional study included 12 steroid-free atopic asthma patients, 6 atopic, and 6 non-atopic healthy controls. RNA of ASM from 4 endobronchial biopsies per subject was isolated and sequenced (GS FLX+, 454/Roche). Ingenuity Pathway Analysis was used to identify gene networks. Comparison of the numbers of reads per gene in asthma and controls was based on the negative binomial distribution. At the current sample size the estimated false discovery rate was approximately 1%. Results: Yield of isolated RNA was 30-821ng. We identified 174 differentially expressed genes between asthma and atopic controls, 108 between asthma and non-atopic controls, and 135 between atopic and non-atopic controls. A set of 8 genes was identified, which seems to define asthma patients from non-asthmatic controls regardless of atopy. Four of these genes were significantly associated with airway hyperresponsiveness. Conclusion: A difference in transcriptomic profile of the airway smooth muscle layer in asthma patients compared to atopic and non-atopic healthy controls may lead to a different regulation of inflammatory pathways and of airway smooth muscle function and development resulting in impaired airway function. This cross-sectional transcriptomics study consisted of 2 visits. At visit 1, asthma patients (n=12), and healthy atopic (n=6) and non-atopic (n=6) controls were screened for eligibility to participate according to the in- and exclusion criteria. Spirometry and a methacholine bronchoprovocation test were performed. At visit 2, FEV1 reversibility was measured and 4 endobronchial biopsies per subject were collected during a bronchoscopy. Airway smooth muscle was collected from the biopsies by laser capture microdissection and total RNA isolated. cDNA was prepared using the Ovation RNA-Seq System (NuGEN). RNA-Seq was performed using the GS FLX+ instrument (454/Roche). Sequence reads were mapped against the human genome (hg19; UCSC). Comparison of the numbers of reads per gene between asthma and healthy controls was based on the negative binomial distribution and carried out with the R package DESeq including correction for multiple testing.

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Dataset Information

RNA Capture-Seq resolves the deep complexity of the human transcriptome (454)

Publications

Targeted RNA sequencing reveals the deep complexity of the human transcriptome.

Similar Datasets

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