Detection Of Differential Gene Expression Research Articles

Comparison of high-throughput single-cell RNA-seq methods for ex vivo drug screening.

Functional precision medicine (FPM) aims to optimize patient-specific drug selection based on the unique characteristics of their cancer cells. Recent advancements in high throughput ex vivo drug profiling have accelerated interest in FPM. Here, we present a proof-of-concept study for an integrated experimental system that incorporates ex vivo treatment response with a single-cell gene expression output enabling barcoding of several drug conditions in one single-cell sequencing experiment. We demonstrate this through a proof-of-concept investigation focusing on the glucocorticoid-resistant acute lymphoblastic leukemia (ALL) E/R+Reh cell line. Three different single-cell transcriptome sequencing (scRNA-seq) approaches were evaluated, each exhibiting high cell recovery and accurate tagging of distinct drug conditions. Notably, our comprehensive analysis revealed variations in library complexity, sensitivity (gene detection), and differential gene expression detection across the methods. Despite these differences, we identified a substantial transcriptional response to fludarabine, a highly relevant drug for treating high-risk ALL, which was consistently recapitulated by all three methods. These findings highlight the potential of our integrated approach for studying drug responses at the single-cell level and emphasize the importance of method selection in scRNA-seq studies. Finally, our data encompassing 27327 cells are freely available to extend to future scRNA-seq methodological comparisons.

SlERF.J2 reduces chlorophyll accumulation and inhibits chloroplast biogenesis and development in tomato leaves

Chlorophyll metabolism and chloroplast biogenesis in tomato (Solanum lycopersicum) leaves contribute to photosynthesis; however, their molecular mechanisms are poorly understood. In this study, we found that overexpression of SlERF.J2 (ethylene transcription factor) resulted in a decrease in leaf chlorophyll content and reduced accumulation of starch and soluble sugar. The slerf.j2 knockout mutant showed no apparent change. Further observation of tissue sections and transmission electron microscopy (TEM) showed that SlERF.J2 was involved in chlorophyll accumulation and chloroplast formation. RNA-seq of mature SlERF.J2-OE leaves showed that many genes involved in chlorophyll biosynthesis and chloroplast formation were significantly downregulated compared with those in WT leaves. Genome global scanning of the ERF TF binding site combined with RNA-seq differential gene expression and qRT-PCR detection analysis showed that COP1 was a potential target gene of SlERF.J2. Tobacco transient expression technology, a dual-luciferase reporter system and Y1H technology were employed to verify that SlERF.J2 could bind to the COP1 promoter. Notably, overexpression of SlERF.J2 in Nr mutants resulted in impaired chloroplast biogenesis and development. Taken together, our findings demonstrated that SlERF.J2 plays an essential role in chlorophyll accumulation and chloroplast formation, laying a foundation for enhancing plant photosynthesis.

Role of extracellular LncRNA-SNHG14/miRNA-3940-5p/NAP12 mRNA in colorectal cancer

Background We aim to identify and analyze the expression of dyregulated RNAs in colorectal cancer (CRC). Methods We selected a panel of RNAs specific to CRC composed of Nucleosome Assembly Protein 1 Like 2 (NAP1L2) mRNA, LNCRNA SNHG14 small nucleolar RNA host gene 14 (LNCRNA SNHG14) and homo sapiens microRNA-3940-5p(hsa-miRNA-3940-5p) from genetic and epigenetic databases. Validation of the chosen RNAs was achieved by real time quantitative PCR in sera of patients with CRC, versus controls groups (benign lesions and healthy individual). Results We found that LLNCRNA SNHG14, hsa-miRNA-3940-5p and NAP1L2 mRNA had an excellent performance characteristics and more superior than CEA, and CA19.9 for differentiating CRC from controls. Combined expression of lncRNA SNHG14- hsa-miR-3940-5p and NAP1L2 mRNA had reached 100% sensitivity with accuracy 93%. Interestingly, serum hsa-miRNA-3940-5p could be an independent prognostic factor in CRC. Conclusion The extracellular lncRNA SNHG14- hsa-miR-3940-5p - NAP1L2 mRNA may aid in CRC management. KEY MESSAGES The extracellular RNAs provide a potential class of noninvasive biomarkers with high specificity, accuracy and stability for detection of CRC. We used insilico data analysis followed by qPCR for detection of differential NAP1L2 gene expression with the selected epigenetic regulators. Our data presented interesting biomarker panel (NAP1L2 gene, lncRNA-SNHG14 and hsa-miR-3940-5p) that may be potential for CRC diagnosis and prognosis.

Trips-Viz: a transcriptome browser for exploring Ribo-Seq data.

Ribosome profiling (Ribo-Seq) is a technique that allows for the isolation and sequencing of mRNA fragments protected from nuclease digestion by actively translating ribosomes. Mapping these ribosome footprints to a genome or transcriptome generates quantitative information on translated regions. To provide access to publicly available ribosome profiling data in the context of transcriptomes we developed Trips-Viz (transcriptome-wide information on protein synthesis-visualized). Trips-Viz provides a large range of graphical tools for exploring global properties of translatomes and of individual transcripts. It enables analysis of aligned footprints to evaluate datasets quality, differential gene expression detection, visual identification of upstream ORFs and alternative proteoforms. Trips-Viz is available at https://trips.ucc.ie.

Impact of Variable RNA-Sequencing Depth on Gene Expression Signatures and Target Compound Robustness: Case Study Examining Brain Tumor (Glioma) Disease Progression.

PurposeGene expression profiling can uncover biologic mechanisms underlying disease and is important in drug development. RNA sequencing (RNA-seq) is routinely used to assess gene expression, but costs remain high. Sample multiplexing reduces RNA-seq costs; however, multiplexed samples have lower cDNA sequencing depth, which can hinder accurate differential gene expression detection. The impact of sequencing depth alteration on RNA-seq–based downstream analyses such as gene expression connectivity mapping is not known, where this method is used to identify potential therapeutic compounds for repurposing.MethodsIn this study, published RNA-seq profiles from patients with brain tumor (glioma) were assembled into two disease progression gene signature contrasts for astrocytoma. Available treatments for glioma have limited effectiveness, rendering this a disease of poor clinical outcome. Gene signatures were subsampled to simulate sequencing alterations and analyzed in connectivity mapping to investigate target compound robustness.ResultsData loss to gene signatures led to the loss, gain, and consistent identification of significant connections. The most accurate gene signature contrast with consistent patient gene expression profiles was more resilient to data loss and identified robust target compounds. Target compounds lost included candidate compounds of potential clinical utility in glioma (eg, suramin, dasatinib). Lost connections may have been linked to low-abundance genes in the gene signature that closely characterized the disease phenotype. Consistently identified connections may have been related to highly expressed abundant genes that were ever-present in gene signatures, despite data reductions. Potential noise surrounding findings included false-positive connections that were gained as a result of gene signature modification with data loss.ConclusionFindings highlight the necessity for gene signature accuracy for connectivity mapping, which should improve the clinical utility of future target compound discoveries.

DFseq: Distribution-Free Method to Detect Differential Gene Expression for RNA-Sequencing Data.

Many current RNA-sequencing data analysis methods compare expressions one gene at a time, taking little consideration of the correlations among genes. In this study, we propose a method to convert such an one-dimensional comparison approach into a two-dimensional evaluation of the ratio of standard deviations (SD) of two constructed random variables. This method allows the identification of differentially expressed genes while controlling a preset significance level conditional on the read count mean-variance relationship. Meanwhile, correlations among genes are naturally accommodated due to the clustering of genes with similar distribution in the proposed σ-σ plot. The proposed distribution-free method is designated as DFseq, because it does not depend on a parametric distribution to fit read count. As a result, compared with parametric methods, DFseq can effectively handle genes with a bimodal-like distribution and/or genes with excessive 0 read counts, as well as genes with outlying observations. Besides, DFseq is an ideal platform for comparing performance of different differential gene expression detection methods.

A Unified Model for Joint Normalization and Differential Gene Expression Detection in RNA-Seq Data.

The RNA-sequencing (RNA-seq) is becoming increasingly popular for quantifying gene expression levels. Since the RNA-seq measurements are relative in nature, between-sample normalization is an essential step in differential expression (DE) analysis. The normalization step of existing DE detection algorithms is usually ad hoc and performed only once prior to DE detection, which may be suboptimal since ideally normalization should be based on non-DE genes only and thus coupled with DE detection. We propose a unified statistical model for joint normalization and DE detection of RNA-seq data. Sample-specific normalization factors are modeled as unknown parameters in the gene-wise linear models and jointly estimated with the regression coefficients. By imposing sparsity-inducing L1 penalty (or mixed L1/L2 penalty for multiple treatment conditions) on the regression coefficients, we formulate the problem as a penalized least-squares regression problem and apply the augmented Lagrangian method to solve it. Simulation and real data studies show that the proposed model and algorithms perform better than or comparably to existing methods in terms of detection power and false-positive rate. The performance gain increases with increasingly larger sample size or higher signal to noise ratio, and is more significant when a large proportion of genes are differentially expressed in an asymmetric manner.

A multiple near isogenic line (multi-NIL) RNA-seq approach to identify candidate genes underpinning QTL.

This study demonstrates how identification of genes underpinning disease-resistance QTL based on differential expression and SNPs can be improved by performing transcriptomic analysis on multiple near isogenic lines. Transcriptomic analysis has been widely used to understand the genetic basis of a trait of interest by comparing genotypes with contrasting phenotypes. However, these approaches identify such large sets of differentially expressed genes that it proves difficult to isolate which genes underpin the phenotype of interest. This study tests whether using multiple near isogenic lines (NILs) can improve the resolution of RNA-seq-based approaches to identify genes underpinning disease-resistance QTL. A set of NILs for a major effect Fusarium crown rot-resistance QTL in barley on the 4HL chromosome arm were analysed under Fusarium crown rot using RNA-seq. Differential gene expression and single nucleotide polymorphism detection analyses reduced the number of putative candidates from thousands within individual NIL pairs to only one hundred and two genes, which were differentially expressed or contained SNPs in common across NIL pairs and occurred on 4HL. Our findings support the value of performing RNA-seq analysis using multiple NILs to remove genetic background effects. The enrichment analyses indicated conserved differences in the response to infection between resistant and sensitive isolines suggesting that sensitive isolines are impaired in systemic defence response to Fusarium pseudograminearum.

A deep transcriptomic resource for the copepod crustacean Labidocera madurae: A potential indicator species for assessing near shore ecosystem health.

Coral reef ecosystems of many sub-tropical and tropical marine coastal environments have suffered significant degradation from anthropogenic sources. Research to inform management strategies that mitigate stressors and promote a healthy ecosystem has focused on the ecology and physiology of coral reefs and associated organisms. Few studies focus on the surrounding pelagic communities, which are equally important to ecosystem function. Zooplankton, often dominated by small crustaceans such as copepods, is an important food source for invertebrates and fishes, especially larval fishes. The reef-associated zooplankton includes a sub-neustonic copepod family that could serve as an indicator species for the community. Here, we describe the generation of a de novo transcriptome for one such copepod, Labidocera madurae, a pontellid from an intensively-studied coral reef ecosystem, Kāne‘ohe Bay, Oahu, Hawai‘i. The transcriptome was assembled using high-throughput sequence data obtained from whole organisms. It comprised 211,002 unique transcripts, including 72,391 with coding regions. It was assessed for quality and completeness using multiple workflows. Bench-marking-universal-single-copy-orthologs (BUSCO) analysis identified transcripts for 88% of expected eukaryotic core proteins. Targeted gene-discovery analyses included searches for transcripts coding full-length “giant” proteins (>4,000 amino acids), proteins and splice variants of voltage-gated sodium channels, and proteins involved in the circadian signaling pathway. Four different reference transcriptomes were generated and compared for the detection of differential gene expression between copepodites and adult females; 6,229 genes were consistently identified as differentially expressed between the two regardless of reference. Automated bioinformatics analyses and targeted manual gene curation suggest that the de novo assembled L. madurae transcriptome is of high quality and completeness. This transcriptome provides a new resource for assessing the global physiological status of a planktonic species inhabiting a coral reef ecosystem that is subjected to multiple anthropogenic stressors. The workflows provide a template for generating and assessing transcriptomes in other non-model species.

Unit-Free and Robust Detection of Differential Expression from RNA-Seq Data

Ultra high-throughput sequencing of transcriptomes (RNA-Seq) is a widely used method for quantifying gene expression levels due to its low cost, high accuracy, and wide dynamic range for detection. However, the nature of RNA-Seq makes it nearly impossible to provide absolute measurements of transcript abundances. Several units or data summarization methods for transcript quantification have been proposed in the past to account for differences in transcript lengths and sequencing depths across different genes and different samples. Nevertheless, further between-sample normalization is still needed for reliable detection of differentially expressed genes. In this paper, we propose a unified statistical model for joint detection of differential gene expression and between-sample normalization. Our method is independent of the unit in which gene expression levels are summarized. We also introduce an efficient algorithm for model fitting. Due to the L0-penalized likelihood used in our model, it is able to reliably normalize the data and detect differential gene expression in some cases when more than 50% of the genes are differentially expressed in an asymmetric manner. We compare our method with existing methods using simulated and real data sets.

Network module-based model in the differential expression analysis for RNA-seq.

RNA-seq has emerged as a powerful technology for the detection of differential gene expression in the transcriptome. The commonly used statistical methods for RNA-seq differential expression analysis were designed for individual genes, which may detect too many irrelevant significantly genes or too few genes to interpret the phenotypic changes. Recently network module-based methods have been proposed as a powerful approach to analyze and interpret expression data in microarray and shotgun proteomics. But the module-based statistical model has not been adequately addressed for RNA-seq data. we proposed a network module-based generalized linear model for differential expression analysis of the count-based sequencing data from RNA-seq. The simulation studies demonstrated the effectiveness of the proposed model and the improvement of the statistical power for identifying the differentially expressed modules in comparison to the existing methods. We also applied our method to tissue datasets and identified 207 significantly differentially expressed kidney-active or liver-active modules. For liver cancer datasets, significantly differentially expressed modules, including Wnt signaling pathway and VEGF pathway, were found to be tightly associated with liver cancer. Besides, in comparison with the single gene-level analysis, our method could identify more significantly biological modules, which related to the liver cancer. The R package SeqMADE is available at https://cran.r-project.org/web/packages/SeqMADE/ . jing.li@sjtu.edu.cn. Supplementary data are available at Bioinformatics online.

RNA-seq based transcriptomic map reveals new insights into mouse salivary gland development and maturation.

BackgroundMouse models have served a valuable role in deciphering various facets of Salivary Gland (SG) biology, from normal developmental programs to diseased states. To facilitate such studies, gene expression profiling maps have been generated for various stages of SG organogenesis. However these prior studies fall short of capturing the transcriptional complexity due to the limited scope of gene-centric microarray-based technology. Compared to microarray, RNA-sequencing (RNA-seq) offers unbiased detection of novel transcripts, broader dynamic range and high specificity and sensitivity for detection of genes, transcripts, and differential gene expression. Although RNA-seq data, particularly under the auspices of the ENCODE project, have covered a large number of biological specimens, studies on the SG have been lacking.ResultsTo better appreciate the wide spectrum of gene expression profiles, we isolated RNA from mouse submandibular salivary glands at different embryonic and adult stages. In parallel, we processed RNA-seq data for 24 organs and tissues obtained from the mouse ENCODE consortium and calculated the average gene expression values. To identify molecular players and pathways likely to be relevant for SG biology, we performed functional gene enrichment analysis, network construction and hierarchal clustering of the RNA-seq datasets obtained from different stages of SG development and maturation, and other mouse organs and tissues. Our bioinformatics-based data analysis not only reaffirmed known modulators of SG morphogenesis but revealed novel transcription factors and signaling pathways unique to mouse SG biology and function. Finally we demonstrated that the unique SG gene signature obtained from our mouse studies is also well conserved and can demarcate features of the human SG transcriptome that is different from other tissues.ConclusionsOur RNA-seq based Atlas has revealed a high-resolution cartographic view of the dynamic transcriptomic landscape of the mouse SG at various stages. These RNA-seq datasets will complement pre-existing microarray based datasets, including the Salivary Gland Molecular Anatomy Project by offering a broader systems-biology based perspective rather than the classical gene-centric view. Ultimately such resources will be valuable in providing a useful toolkit to better understand how the diverse cell population of the SG are organized and controlled during development and differentiation.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3228-7) contains supplementary material, which is available to authorized users.

Removal of unwanted variation reveals novel patterns of gene expression linked to sleep homeostasis in murine cortex.

BackgroundWhy we sleep is still one of the most perplexing mysteries in biology. Strong evidence indicates that sleep is necessary for normal brain function and that sleep need is a tightly regulated process. Surprisingly, molecular mechanisms that determine sleep need are incompletely described. Moreover, very little is known about transcriptional changes that specifically accompany the accumulation and discharge of sleep need. Several studies have characterized differential gene expression changes following sleep deprivation. Much less is known, however, about changes in gene expression during the compensatory response to sleep deprivation (i.e. recovery sleep).ResultsIn this study we present a comprehensive analysis of the effects of sleep deprivation and subsequent recovery sleep on gene expression in the mouse cortex. We used a non-traditional analytical method for normalization of genome-wide gene expression data, Removal of Unwanted Variation (RUV). RUV improves detection of differential gene expression following sleep deprivation. We also show that RUV normalization is crucial to the discovery of differentially expressed genes associated with recovery sleep. Our analysis indicates that the majority of transcripts upregulated by sleep deprivation require 6 h of recovery sleep to return to baseline levels, while the majority of downregulated transcripts return to baseline levels within 1–3 h. We also find that transcripts that change rapidly during recovery (i.e. within 3 h) do so on average with a time constant that is similar to the time constant for the discharge of sleep need.ConclusionsWe demonstrate that proper data normalization is essential to identify changes in gene expression that are specifically linked to sleep deprivation and recovery sleep. Our results provide the first evidence that recovery sleep is comprised of two waves of transcriptional regulation that occur at different times and affect functionally distinct classes of genes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3065-8) contains supplementary material, which is available to authorized users.

Comparison of normalization and differential expression analyses using RNA-Seq data from 726 individual Drosophila melanogaster.

BackgroundA generally accepted approach to the analysis of RNA-Seq read count data does not yet exist. We sequenced the mRNA of 726 individuals from the Drosophila Genetic Reference Panel in order to quantify differences in gene expression among single flies. One of our experimental goals was to identify the optimal analysis approach for the detection of differential gene expression among the factors we varied in the experiment: genotype, environment, sex, and their interactions. Here we evaluate three different filtering strategies, eight normalization methods, and two statistical approaches using our data set. We assessed differential gene expression among factors and performed a statistical power analysis using the eight biological replicates per genotype, environment, and sex in our data set.ResultsWe found that the most critical considerations for the analysis of RNA-Seq read count data were the normalization method, underlying data distribution assumption, and numbers of biological replicates, an observation consistent with previous RNA-Seq and microarray analysis comparisons. Some common normalization methods, such as Total Count, Quantile, and RPKM normalization, did not align the data across samples. Furthermore, analyses using the Median, Quantile, and Trimmed Mean of M-values normalization methods were sensitive to the removal of low-expressed genes from the data set. Although it is robust in many types of analysis, the normal data distribution assumption produced results vastly different than the negative binomial distribution. In addition, at least three biological replicates per condition were required in order to have sufficient statistical power to detect expression differences among the three-way interaction of genotype, environment, and sex.ConclusionsThe best analysis approach to our data was to normalize the read counts using the DESeq method and apply a generalized linear model assuming a negative binomial distribution using either edgeR or DESeq software. Genes having very low read counts were removed after normalizing the data and fitting it to the negative binomial distribution. We describe the results of this evaluation and include recommended analysis strategies for RNA-Seq read count data.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2353-z) contains supplementary material, which is available to authorized users.

Expressed Genes in the Lymphoid Organ of Broodstock Banana Prawn (Penaeus merguiensis) Using Suppression Subtractive Hybridization

Most systemic viral infections in penaeid prawns are associated with the formation of lymphoid organ spheroid (LOS) cells in the lymphoid organ (LO). The development of spheroids is suggested to be a major defence mechanism against viral infection in penaeids. Spheroids were observed in a broodstock population of banana prawns (Penaeus merguiensis), while these changes were not identified in the wild population. This opportunity was taken to see how well suppression subtractive hybridization (SSH) would resolve a histological change with no known aetiology. A total of 316 sequenced clones were clustered into 141 contigs and 51.6% of the clones shared significant similarities to known peptide or nucleotide sequences in the NCBI GenBank database. These transcripts were assigned into 8 categories, including immune related genes (2.5%), synthesis, processing and regulation-related proteins (4.4%), ribosomal proteins (6.3%), proteases and inhibitors (7.0%), energy and metabolism factors (7.3%), structural and cytoskeletal related proteins (10.1%) and other sequences (13.9%). Many sequenced clones (48.4%) from the libraries had no significant similarity to amino acids/nucleotides in the public database revealing the ability of this method in disclosing new differentially expressed genes in the lymphoid organ of prawns. Despite the detection of differential gene expression in the SHH libraries, unfortunately there were no viral genomes identified which were associated with spheroid formation within the LO of penaeids. However, polymerase chain reaction (PCR) amplification with HPV140F and HPV140R primers suggested that the development of spheroid cells in the lymphoid organ of the broodstock population most likely due to Penaeus merguiensis hepandensovirus (PmeDV) which maybe the senior synonym of lymphoidal parvovirus (LPV).

Corset: enabling differential gene expression analysis for de novo assembled transcriptomes.

Next generation sequencing has made it possible to perform differential gene expression studies in non-model organisms. For these studies, the need for a reference genome is circumvented by performing de novo assembly on the RNA-seq data. However, transcriptome assembly produces a multitude of contigs, which must be clustered into genes prior to differential gene expression detection. Here we present Corset, a method that hierarchically clusters contigs using shared reads and expression, then summarizes read counts to clusters, ready for statistical testing. Using a range of metrics, we demonstrate that Corset out-performs alternative methods. Corset is available from https://code.google.com/p/corset-project/.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-014-0410-6) contains supplementary material, which is available to authorized users.

Co-regulated protein functional modules with varying activities in dynamic PPI networks

Current methods for the detection of differential gene expression focus on finding individual genes that may be responsible for certain diseases or external irritants. However, for common genetic diseases, multiple genes and their interactions should be understood and treated together during the exploration of disease causes and possible drug design. The present study focuses on analyzing the dynamic patterns of co-regulated modules during biological progression and determining those having remarkably varying activities, using the yeast cell cycle as a case study. We first constructed dynamic active protein-protein interaction networks by modeling the activity of proteins and assembling the dynamic co-regulation protein network at each time point. The dynamic active modules were detected using a method based on the Bayesian graphical model and then the modules with the most varied dispersion of clustering coefficients, which could be responsible for the dynamic mechanism of the cell cycle, were identified. Comparison of results from our functional module detection with the state-of-art functional module detection methods and validation of the ranking of activities of functional modules using GO annotations demonstrate the efficacy of our method for narrowing the scope of possible essential responding modules that could provide multiple targets for biologists to further experimentally validate.

Contaminated Chi-Square Modeling and Large-Scale ANOVA Testing

We propose a convenient moment-based procedure for testing the omnibus null hypothesis of no contamination of a central chi-square distribution by a non-central chi-square distribution. In sharp contrast with likelihood ratio tests for mixture models, there is no need for re-sampling or random field theory to obtain critical values. Rather, critical values are available from an asymptotic normal distribution, and there is excellent agreement between nominal and actual significance levels. This procedure may be used to model numerous chi-square statistics, obtained via monotonic transformations of F statistics, from large-scale ANOVA testing, such as that encountered in microarray data analysis. In that context, modeling chi-square statistics instead of p-values may improve detection of differential gene expression, as we demonstrate through simulation studies, while also reducing false declarations of the same, as we illustrate in a case study on aging and cognition. Our procedure may also be incorporated into a gene filtration process, which may reduce type II errors on genewise null hypotheses by justifying lighter controls for Type I errors.

Empirical null distribution-based modeling of multi-class differential gene expression detection

In this paper, we study the multi-class differential gene expression detection for microarray data. We propose a likelihood-based approach to estimating an empirical null distribution to incorporate gene interactions and provide a more accurate false-positive control than the commonly used permutation or theoretical null distribution-based approach. We propose to rank important genes by p-values or local false discovery rate based on the estimated empirical null distribution. Through simulations and application to lung transplant microarray data, we illustrate the competitive performance of the proposed method.

Efficient experimental design and analysis strategies for the detection of differential expression using RNA-Sequencing

BackgroundRNA sequencing (RNA-Seq) has emerged as a powerful approach for the detection of differential gene expression with both high-throughput and high resolution capabilities possible depending upon the experimental design chosen. Multiplex experimental designs are now readily available, these can be utilised to increase the numbers of samples or replicates profiled at the cost of decreased sequencing depth generated per sample. These strategies impact on the power of the approach to accurately identify differential expression. This study presents a detailed analysis of the power to detect differential expression in a range of scenarios including simulated null and differential expression distributions with varying numbers of biological or technical replicates, sequencing depths and analysis methods.ResultsDifferential and non-differential expression datasets were simulated using a combination of negative binomial and exponential distributions derived from real RNA-Seq data. These datasets were used to evaluate the performance of three commonly used differential expression analysis algorithms and to quantify the changes in power with respect to true and false positive rates when simulating variations in sequencing depth, biological replication and multiplex experimental design choices.ConclusionsThis work quantitatively explores comparisons between contemporary analysis tools and experimental design choices for the detection of differential expression using RNA-Seq. We found that the DESeq algorithm performs more conservatively than edgeR and NBPSeq. With regard to testing of various experimental designs, this work strongly suggests that greater power is gained through the use of biological replicates relative to library (technical) replicates and sequencing depth. Strikingly, sequencing depth could be reduced as low as 15% without substantial impacts on false positive or true positive rates.