Mapping and quantifying mammalian transcriptomes by RNA-Seq

Mortazavi, A; Williams, Brian A.; McCue, Kenneth; Schaeffer, Lorian; Wold, B

doi:10.1038/nmeth.1226

Cited by 11,749 publications

(10,142 citation statements)

References 22 publications

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“…Because we had a clinically homogeneous cohort of hemi‐paretic patients, we further focused our analysis on stroke patients with a confirmed MCA territory infarct. Analysis of gene expression was performed on read data (normalized to RPKM values18, 20). We define a gene as consisting of multiple exons that define differential splicing and isoforms of the gene.…”

Section: Resultsmentioning

confidence: 99%

“…Gene expression and exon expression data were indicated as reads per kilobase per million aligned reads (RPKM) 20. Expression values were filtered to remove low expression genes (<10 reads/gene) and low occurrence (present in <50% of samples) quartile normalized, Log2 (+1 offset) transformed and subjected to analysis of variance (ANOVA) with diagnosis (control or MCA as the discriminating factor).…”

Section: Subjects/materials and Methodsmentioning

confidence: 99%

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Blood transcriptome changes after stroke in an African American population

Meller

Pearson

Hardy

et al. 2016

Ann Clin Transl Neurol

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ObjectiveMolecular diagnostic medicine holds much promise to change point of care treatment. An area where additional diagnostic tools are needed is in acute stroke care, to assist in diagnosis and prognosis. Previous studies using microarray‐based gene expression analysis of peripheral blood following stroke suggests this approach may be effective. Next‐generation sequencing (NGS) approaches have expanded genomic analysis and are not limited to previously identified genes on a microarray chip. Here, we report on a pilot NGS study to identify gene expression and exon expression patterns for the prediction of stroke diagnosis and prognosis.MethodsWe recruited 28 stroke patients and 28 age‐ and sex‐matched hypertensive controls. RNA was extracted from 3 mL blood samples, and RNA‐Seq libraries were assembled and sequenced.ResultsBioinformatical analysis of the aligned RNA data reveal exonic (30%), intronic (36%), and novel RNA components (not currently annotated: 33%). We focused our study on patients with confirmed middle cerebral artery occlusion ischemic stroke (n = 17). On the basis of our observation of differential splicing of gene transcripts, we used all exonic RNA expression rather than gene expression (combined exons) to build prediction models using support vector machine algorithms. Based on model building, these models have a high predicted accuracy rate >90% (spec. 88% sen. 92%). We further stratified outcome based on the improvement in NIHss scores at discharge; based on model building we observe a predicted 100% accuracy rate.Interpretation NGS‐based exon expression analysis approaches have a high potential for patient diagnosis and outcome prediction, with clear utility to aid in clinical patient care.

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Section: Resultsmentioning

confidence: 99%

Section: Subjects/materials and Methodsmentioning

confidence: 99%

Blood transcriptome changes after stroke in an African American population

Meller

Pearson

Hardy

et al. 2016

Ann Clin Transl Neurol

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show abstract

“…The traditional R/FPKM 8, 9 (reads/fragments per kilobase per million reads) has been largely superseded by the TPM 10 (transcripts per million), since the latter is more consistent across libraries. Regardless, both of these units attempt to “correct for” sequencing depth and feature length and thus do not reflect the influence of these on quantification uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences

2015

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High-throughput sequencing of cDNA (RNA-seq) is used extensively to characterize the transcriptome of cells. Many transcriptomic studies aim at comparing either abundance levels or the transcriptome composition between given conditions, and as a first step, the sequencing reads must be used as the basis for abundance quantification of transcriptomic features of interest, such as genes or transcripts. Several different quantification approaches have been proposed, ranging from simple counting of reads that overlap given genomic regions to more complex estimation of underlying transcript abundances. In this paper, we show that gene-level abundance estimates and statistical inference offer advantages over transcript-level analyses, in terms of performance and interpretability. We also illustrate that while the presence of differential isoform usage can lead to inflated false discovery rates in differential expression analyses on simple count matrices and transcript-level abundance estimates improve the performance in simulated data, the difference is relatively minor in several real data sets. Finally, we provide an R package ( tximport) to help users integrate transcript-level abundance estimates from common quantification pipelines into count-based statistical inference engines.

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“…Immediately following the assembly, the genome will need to be annotated to catalog genes and other features of interest [64], or aligned to other genomes to enable comparative genomics studies [65]. Several sequencing-based assays, such as RNA-seq [66] and Methyl-seq [67], can be used with the assembly to study transcriptionally or epigenetically active regions of the genome, and population studies will often attempt to build higher-order relationships, such as gene networks, or relate genotype to phenotype.…”

Section: Analyticsmentioning

confidence: 99%