Dataset Information

How Many Genes Are Expressed in a Transcriptome? Estimation and Results for RNA-Seq.

ABSTRACT: RNA-seq experiments estimate the number of genes expressed in a transcriptome as well as their relative frequencies. However, an undetermined number of genes can remain undetected due to their low expression relative to the sample size (sequence depth). Estimation of the true number of genes expressed in a transcriptome is essential in order to determine which genes are exclusively expressed in specific tissues or under particular conditions. A reliable estimate of the true number of expressed genes is also required to accurately measure transcriptome changes and to predict the sequencing depth needed to increase the proportion of detected genes. This problem is analogous to ecological sampling problems such as estimating the number of species at a given site. Here we present a non-parametric estimator for the number of undetected genes as well as for the extra sample size needed to detect a given proportion of the undetected genes. Our estimators are superior to ones already published by having smaller standard errors and biases. We applied our method to a set of 32 publicly available RNA-seq experiments, including the evaluation of 311 individually sequenced libraries. We found that in the majority of the cases more than one thousand genes are undetected, and that on average approximately 6% of the expressed genes per accession remain undetected. This figure increases to approximately 10% if individual sequencing libraries are analyzed. Our method is also applicable to metagenomic experiments. Using our method, the number of undetected genes as well as the sample size needed to detect them can be calculated, leading to more accurate and complete gene expression studies.

SUBMITTER: Garcia-Ortega LF

PROVIDER: S-EPMC4479379 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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How Many Genes Are Expressed in a Transcriptome? Estimation and Results for RNA-Seq.

García-Ortega Luis Fernando LF Martínez Octavio O

PloS one 20150624 6

RNA-seq experiments estimate the number of genes expressed in a transcriptome as well as their relative frequencies. However, an undetermined number of genes can remain undetected due to their low expression relative to the sample size (sequence depth). Estimation of the true number of genes expressed in a transcriptome is essential in order to determine which genes are exclusively expressed in specific tissues or under particular conditions. A reliable estimate of the true number of expressed g ...[more]

PMID: 26107654

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Project description:BackgroundRNA sequencing (RNA-Seq) measures genome-wide gene expression. RNA-Seq data is count-based rendering normal distribution models for analysis inappropriate. Normalization of RNA-Seq data to transform the data has limitations which can adversely impact the analysis. Furthermore, there are a few count-based methods for analysis of RNA-Seq data but they are essentially for pairwise analysis of treatment groups or multiclasses but not pattern-based to identify co-expressed genes.ResultsWe adapted our extracting patterns and identifying genes methodology for RNA-Seq (EPIG-Seq) count data. The software uses count-based correlation to measure similarity between genes, quasi-Poisson modelling to estimate dispersion in the data and a location parameter to indicate magnitude of differential expression. EPIG-Seq is different than any other software currently available for pattern analysis of RNA-Seq data in that EPIG-Seq 1) uses count level data and supports cases of inflated zeros, 2) identifies statistically significant clusters of genes that are co-expressed across experimental conditions, 3) takes into account dispersion in the replicate data and 4) provides reliable results even with small sample sizes. EPIG-Seq operates in two steps: 1) extract the pattern profiles from data as seeds for clustering co-expressed genes and 2) cluster the genes to the pattern seeds and compute statistical significance of the pattern of co-expressed genes. EPIG-Seq provides a table of the genes with bootstrapped p-values and profile plots of the patterns of co-expressed genes. In addition, EPIG-Seq provides a heat map and principal component dimension reduction plot of the clustered genes as visual aids. We demonstrate the utility of EPIG-Seq through the analysis of toxicogenomics and cancer data sets to identify biologically relevant co-expressed genes. EPIG-Seq is available at: sourceforge.net/projects/epig-seq.ConclusionsEPIG-Seq is unlike any other software currently available for pattern analysis of RNA-Seq count level data across experimental groups. Using the EPIG-Seq software to analyze RNA-Seq count data across biological conditions permits the ability to extract biologically meaningful co-expressed genes associated with coordinated regulation.

Project description:BACKGROUND:The transient middle cerebral artery occlusion (tMCAO) model is used for studying the molecular mechanisms of ischemic damage and neuroprotection. Numerous studies have demonstrated the role of individual genes and associated signaling pathways in the pathogenesis of ischemic stroke. Here, the tMCAO model was used to investigate the genome-wide response of the transcriptome of rat brain tissues to the damaging effect of ischemia and subsequent reperfusion. RESULTS:Magnetic resonance imaging and histological examination showed that the model of focal ischemia based on endovascular occlusion of the right middle cerebral artery for 90 min using a monofilament, followed by restoration of the blood flow, led to reproducible localization of ischemic damage in the subcortical structures of the brain. High-throughput RNA sequencing (RNA-Seq) revealed the presence of differentially expressed genes (DEGs) in subcortical structures of rat brains resulting from hemisphere damage by ischemia after tMCAO, as well as in the corresponding parts of the brains of sham-operated animals. Real-time reverse transcription polymerase chain reaction expression analysis of 20 genes confirmed the RNA-Seq results. We identified 469 and 1939 genes that exhibited changes in expression of >?1.5-fold at 4.5 and 24 h after tMCAO, respectively. Interestingly, we found 2741 and 752 DEGs under ischemia-reperfusion and sham-operation conditions at 24 h vs. 4.5 h after tMCAO, respectively. The activation of a large number of genes involved in inflammatory, immune and stress responses, apoptosis, ribosome function, DNA replication and other processes was observed in ischemia-reperfusion conditions. Simultaneously, massive down-regulation of the mRNA levels of genes involved in the functioning of neurotransmitter systems was recorded. A Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis showed that dozens of signaling pathways were associated with DEGs in ischemia-reperfusion conditions. CONCLUSIONS:The data obtained revealed a global profile of gene expression in the rat brain sub-cortex under tMCAO conditions that can be used to identify potential therapeutic targets in the development of new strategies for the prevention and treatment of ischemic stroke.

Dataset Information

How Many Genes Are Expressed in a Transcriptome? Estimation and Results for RNA-Seq.

Publications

How Many Genes Are Expressed in a Transcriptome? Estimation and Results for RNA-Seq.

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