Dataset Information

Gene expression variability and the analysis of large-scale RNA-seq studies with the MDSeq.

ABSTRACT: Rapidly decreasing cost of next-generation sequencing has led to the recent availability of large-scale RNA-seq data, that empowers the analysis of gene expression variability, in addition to gene expression means. In this paper, we present the MDSeq, based on the coefficient of dispersion, to provide robust and computationally efficient analysis of both gene expression means and variability on RNA-seq counts. The MDSeq utilizes a novel reparametrization of the negative binomial to provide flexible generalized linear models (GLMs) on both the mean and dispersion. We address challenges of analyzing large-scale RNA-seq data via several new developments to provide a comprehensive toolset that models technical excess zeros, identifies outliers efficiently, and evaluates differential expressions at biologically interesting levels. We evaluated performances of the MDSeq using simulated data when the ground truths are known. Results suggest that the MDSeq often outperforms current methods for the analysis of gene expression mean and variability. Moreover, the MDSeq is applied in two real RNA-seq studies, in which we identified functionally relevant genes and gene pathways. Specifically, the analysis of gene expression variability with the MDSeq on the GTEx human brain tissue data has identified pathways associated with common neurodegenerative disorders when gene expression means were conserved.

SUBMITTER: Ran D

PROVIDER: S-EPMC5737414 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Gene expression variability and the analysis of large-scale RNA-seq studies with the MDSeq.

Ran Di D Daye Z John ZJ

Nucleic acids research 20170701 13

Rapidly decreasing cost of next-generation sequencing has led to the recent availability of large-scale RNA-seq data, that empowers the analysis of gene expression variability, in addition to gene expression means. In this paper, we present the MDSeq, based on the coefficient of dispersion, to provide robust and computationally efficient analysis of both gene expression means and variability on RNA-seq counts. The MDSeq utilizes a novel reparametrization of the negative binomial to provide flexi ...[more]

PMID: 28535263

Similar Datasets

Project description:Aflatoxin B1 (AFB1), which is mainly produced by Aspergillus flavus and Aspergillus parasiticus, is the most toxic and hepatocarcinogenic polyketide known. Chemical fungicides are currently utilized to reduce this fungal contaminant, but they are potentially harmful to human health and the environment. Therefore, natural anti-aflatoxigenic products are used as sustainable alternatives to control food and feed contamination. For example, eugenol, presents in many essential oils, has been identified as an aflatoxin inhibitor. However, its exact mechanism of inhibition is yet to be clarified. In this study, the anti-aflatoxigenic mechanism of eugenol in A. flavus was determined using a comparative transcriptomic approach. Twenty of twenty-nine genes in the aflatoxin biosynthetic pathway were down-regulated by eugenol. The most strongly down-regulated gene was aflMa, followed by aflI, aflJ, aflCa, aflH, aflNa, aflE, aflG, aflM, aflD, and aflP. However, the expression of the regulator gene aflR did not change significantly and the expression of aflS was slightly up-regulated. The down-regulation of the global regulator gene veA resulted in the up-regulation of srrA, and the down-regulation of ap-1 and mtfA. The early developmental regulator brlA was profoundly up-regulated in A. flavus after eugenol treatment. These results suggested a model in which eugenol improves fungal development by up-regulating the expression of brlA by the suppression of veA expression and inhibits aflatoxin production through the suppression of veA expression. Exposure to eugenol also caused dysregulated transcript levels of the G protein-coupled receptors (GPCRs) and oxylipins genes. A Gene Ontology analysis indicated that the genes that were highly responsive to eugenol were mainly enriched in RNA-binding functions, suggesting that post-transcriptional modification plays a pivotal role in aflatoxin biosynthesis. KEGG analysis showed that ribosome biogenesis was the most dysregulated pathway, suggesting that eugenol dysregulates ribosome biogenesis, which then interrupts the biosynthesis of Nor-1, Ver-1, and OmtA, and prevents aflatoxisomes performing their normal function in aflatoxin production. In conclusion, our results indicated that eugenol inhibited AFB1 production by modulating the expression of structural genes in aflatoxin pathway, fungal antioxidant status, post-transcriptional modifications and biosynthesis of backbone enzymes in A. flavus.

Project description:BackgroundEarly application of second-generation sequencing technologies to transcript quantitation (RNA-seq) has hinted at a vast mammalian transcriptome, including transcripts from nearly all known genes, which might be fully measured only by ultradeep sequencing. Subsequent studies suggested that low-abundance transcripts might be the result of technical or biological noise rather than active transcripts; moreover, most RNA-seq experiments did not provide enough read depth to generate high-confidence estimates of gene expression for low-abundance transcripts. As a result, the community adopted several heuristics for RNA-seq analysis, most notably an arbitrary expression threshold of 0.3 - 1 FPKM for downstream analysis. However, advances in RNA-seq library preparation, sequencing technology, and informatic analysis have addressed many of the systemic sources of uncertainty and undermined the assumptions that drove the adoption of these heuristics. We provide an updated view of the accuracy and efficiency of RNA-seq experiments, using genomic data from large-scale studies like the ENCODE project to provide orthogonal information against which to validate our conclusions.ResultsWe show that a human cell's transcriptome can be divided into active genes carrying out the work of the cell and other genes that are likely the by-products of biological or experimental noise. We use ENCODE data on chromatin state to show that ultralow-expression genes are predominantly associated with repressed chromatin; we provide a novel normalization metric, zFPKM, that identifies the threshold between active and background gene expression; and we show that this threshold is robust to experimental and analytical variations.ConclusionsThe zFPKM normalization method accurately separates the biologically relevant genes in a cell, which are associated with active promoters, from the ultralow-expression noisy genes that have repressed promoters. A read depth of twenty to thirty million mapped reads allows high-confidence quantitation of genes expressed at this threshold, providing important guidance for the design of RNA-seq studies of gene expression. Moreover, we offer an example for using extensive ENCODE chromatin state information to validate RNA-seq analysis pipelines.

Dataset Information

Gene expression variability and the analysis of large-scale RNA-seq studies with the MDSeq.

Publications

Gene expression variability and the analysis of large-scale RNA-seq studies with the MDSeq.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets