Design Of RNA-seq Experiments Research Articles

Adjusted Sample Size Calculation for RNA-seq Data in the Presence of Confounding Covariates

Sample size calculation for adequate power analysis is critical in optimizing RNA-seq experimental design. However, the complexity increases for directly estimating sample size when taking into consideration confounding covariates. Although a number of approaches for sample size calculation have been proposed for RNA-seq data, most ignore any potential heterogeneity. In this study, we implemented a simulation-based and confounder-adjusted method to provide sample size recommendations for RNA-seq differential expression analysis. The data was generated using Monte Carlo simulation, given an underlined distribution of confounding covariates and parameters for a negative binomial distribution. The relationship between the sample size with the power and parameters, such as dispersion, fold change and mean read counts, can be visualized. We demonstrate that the adjusted sample size for a desired power and type one error rate of α is usually larger when taking confounding covariates into account. More importantly, our simulation study reveals that sample size may be underestimated by existing methods if a confounding covariate exists in RNA-seq data. Consequently, this underestimate could affect the detection power for the differential expression analysis. Therefore, we introduce confounding covariates for sample size estimation for heterogeneous RNA-seq data.

Translating RNA Splicing Analysis into Diagnosis and Therapy

A large proportion of rare disease patients remain undiagnosed and the vast majority of such conditions remain untreatable whether diagnosed or not. RNA splicing analysis is able to increase the diagnostic rate in rare disease by identifying cryptic splicing mutations and can help in interpreting the pathogenicity of genomic variants. Whilst targeted RT-PCR analysis remains a highly sensitive tool for assessing the splicing effects of known variants, RNA-seq can provide a more comprehensive transcriptome-wide analysis of splicing. Appropriate care should be taken in RNA-seq experimental design since sample quality, processing, choice of library preparation and sequencing parameters all introduce variability. Many bioinformatic tools exist to aid both in the prediction of splicing effects from DNA sequence and in the handling of RNA-seq data for splicing analysis. Once identified, splicing abnormalities may be amenable to correction using antisense oligonucleotide compounds by masking cryptic splice sites or blocking key splice regulatory elements, or by use of alternative corrective technologies such as trans-splicing. A growing number of such drugs have started to enter clinical use, most notably nusinersen for the treatment of spinal muscular atrophy. By bringing together the fields of RNA diagnostics and antisense therapeutics, it is becoming feasible to envisage the development of a truly personalised medicine pipeline. This has already been shown to be possible in the case of milasen, an n=1 bespoke antisense drug, and the growth and convergence of these technologies means that similar therapeutic opportunities should arise in the near future.

High-Throughput Sequencing for Trypanosome Transcriptome Characterization.

High-throughput sequencing of cDNA (RNASeq) is now the method of choice for analysis of transcriptomes. This chapter details important considerations in the design of RNASeq experiments for kinetoplastids grown in culture or experimental animals. It contains protocols for obtaining parasites from rodents, and for removal of rRNA from total RNA. In addition, custom pipelines for sequence alignment, and for data analysis and visualization, are described.

A computational method to aid the design and analysis of single cell RNA-seq experiments for cell type identification

BackgroundThe advent of single cell RNA sequencing (scRNA-seq) enabled researchers to study transcriptomic activity within individual cells and identify inherent cell types in the sample. Although numerous computational tools have been developed to analyze single cell transcriptomes, there are no published studies and analytical packages available to guide experimental design and to devise suitable analysis procedure for cell type identification.ResultsWe have developed an empirical methodology to address this important gap in single cell experimental design and analysis into an easy-to-use tool called SCEED (Single Cell Empirical Experimental Design and analysis). With SCEED, user can choose a variety of combinations of tools for analysis, conduct performance analysis of analytical procedures and choose the best procedure, and estimate sample size (number of cells to be profiled) required for a given analytical procedure at varying levels of cell type rarity and other experimental parameters. Using SCEED, we examined 3 single cell algorithms using 48 simulated single cell datasets that were generated for varying number of cell types and their proportions, number of genes expressed per cell, number of marker genes and their fold change, and number of single cells successfully profiled in the experiment.ConclusionsBased on our study, we found that when marker genes are expressed at fold change of 4 or more, either Seurat or SIMLR algorithm can be used to analyze single cell dataset for any number of single cells isolated (minimum 1000 single cells were tested). However, when marker genes are expected to be only up to fold change of 2, choice of the single cell algorithm is dependent on the number of single cells isolated and rarity of cell types to be identified. In conclusion, our work allows the assessment of various single cell methods and also aids in the design of single cell experiments.

Prediction and Characterization of miRNA/Target Pairs in Non-Model Plants Using RNA-seq.

Plant microRNAs (miRNAs) are ∼20‐ to 24‐nucleotide small RNAs that post‐transcriptionally regulate gene expression of mRNA targets. Here, we present a workflow to characterize the miRNA transcriptome of a non‐model plant, focusing on miRNAs and targets that are differentially expressed under one experimental treatment. We cover RNA‐seq experimental design to create paired small RNA and mRNA libraries and perform quality control of raw data, de novo mRNA transcriptome assembly and annotation, miRNA prediction, differential expression, target identification, and functional enrichment analysis. Additionally, we include validation of differential expression and miRNA‐induced target cleavage using qRT‐PCR and modified RNA ligase–mediated 5′ rapid amplification of cDNA ends, respectively. Our procedure relies on freely available software and web resources. It is intended for users that lack programming skills but can navigate a command‐line interface. To enable an understanding of formatting requirements and anticipated results, we provide sample RNA‐seq data and key input/output files for each stage. © 2019 The Authors. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Investigation of candidate genes for metabolic disorders expressed in liver and pituitary gland by comparing the RNA-seq data of Polish-HF and Polish-Red cattle

Background: Metabolic disorder is a major health problem in dairy cattle, particularly to high milk producing dairy cattle. It is worthily emphasized that metabolic diseases have a very complex etiology and pathogenesis, and the impact of these diseases on hepatic and pituitary gland gene expression and organism oxidative balance is not fully described. The presented study was aimed to determine and predict the hepatic and pituitary gland expression of potential candidate genes in context to maintenance of oxidative balance, negative nitrogen balance, as well as ketosis in Polish HF and Polish Red cattle. Methods: Based on the RNA-seq experimental data, we investigated the candidate genes ( SOD1, SOD2, SOD3, GPx2, GPx3, GPx5, GPx6, GPx7, GPx8, BDH1, FN1, ACSL3 , HMGCL, HMGCS2, BDH2, ACSL6 , ACAT2, IDH3B, ACAT1, HMGCS1, ACSL4 , ACSL1 , PC, CPT1A, OXCT1 and ACSL5 respectively) expressions in liver and pituitary gland tissues of Polish HF and Polish Red cattle. The RNA-seq experimental design comprised of young bulls aged between 6 to 12 months were investigated. For each breed, six liver and six pituitary gland tissues were sequenced using Next-seq 500 illumina platform. The RNA-seq expression data were normalized by the reads per kilobase of exon per million reads mapped (RPKM) method. Results: By comparing the RNA-seq data of liver and pituitary gland tissues, the investigated candidate genes were highly expressed in the hepatic tissues than to pituitary gland in investigated cattle breeds. However, by comparing the Polish HF and Polish Red cattle breeds, results revealed a similar trend of gene expression profiling of all investigated candidate genes for both metabolic tissues. In case of hepatic gene expression profiling, the SOD1, FN1, HMGCL, HMGCS2, ACAT2, ACAT1, HMGCS1, ACSL1 and ACSL5 were highly expressed (FPKM values of >40), followed by SOD2, GPX3, IDH3B, PC and BDH2 as moderately expressed (FPKM values: >10 to 40), followed by SOD2, GPX8, IDH3B, ACAT1, ACSL4 and PC as moderately expressed (FPKM values: >10 to <40), and averagely expressed SOD3, GPX3,GPX5, GPX6, GPX7, GPX2, BDH1, BDH2, ACSL3 , ACSL6 , CPT1A, OXCT1, FN1, HMGCL, HMGCS2, ACAT2, ACAT1, HMGCS1, ACSL1 and ACSL5 respectively, in Polish HF and Polish Red breeds. Conclusions: Based on this presented results on hepatic and pituitary gland gene expression, a further research plan is an essential pre-requisite to validate the identified candidate genes. Study indicated the understanding the genetic factors that predispose metabolic disorders in cattle would benefit the dairy industry as a whole by providing producers, breeding services, and veterinarians a tool to forecast a cow’s susceptibility to metabolic disorders.

RNASeqDesign: A framework for RNA-Seq genome-wide power calculation and study design issues.

Massively parallel sequencing (a.k.a. next-generation sequencing, NGS) technology has emerged as a powerful tool in characterizing genomic profiles. Among many NGS applications, RNA sequencing (RNA-Seq) has gradually become a standard tool for global transcriptomic monitoring. Although the cost of NGS experiments has dropped constantly, the high sequencing cost and bioinformatic complexity are still obstacles for many biomedical projects. Unlike earlier fluorescence-based technologies such as microarray, modelling of NGS data should consider discrete count data. In addition to sample size, sequencing depth also directly relates to the experimental cost. Consequently, given total budget and pre-specified unit experimental cost, the study design issue in RNA-Seq is conceptually a more complex multi-dimensional constrained optimization problem rather than one-dimensional sample size calculation in traditional hypothesis setting. In this paper, we propose a statistical framework, namely "RNASeqDesign", to utilize pilot data for power calculation and study design of RNA-Seq experiments. The approach is based on mixture model fitting of p-value distribution from pilot data and a parametric bootstrap procedure based on approximated Wald test statistics to infer genome-wide power for optimal sample size and sequencing depth. We further illustrate five practical study design tasks for practitioners. We perform simulations and three real applications to evaluate the performance and compare to existing methods.

Guidelines for RNA-seq projects: applications and opportunities in non-model decapod crustacean species

Next-generation sequencing (NGS) has dramatically changed the way biological research is being conducted in the post-genomic era, and they have only been utilized widely over the recent decade for studies of non-model decapod crustacean species, predominantly by sequencing the transcriptome of various tissues across different life stages. Next-generation sequencing can now provide a rapid, cost-effective solution for discovery of genetic markers crucial in many applications that would previously have otherwise taken years to develop. Sequencing of the entire transcriptome (referred to as RNA sequencing; RNA-seq) is one of the most popular NGS tools. RNA-seq studies of non-model species in crustacean taxa, however, have faced some problems, including a lack of “good” experimental study design, a relative paucity of gene annotations, combined with limited knowledge of genomic technologies and analyses. The aim of the current review is to assist crustacean biologists to develop a better appreciation for the applications and scope of RNA-seq analysis, understand the basic requirements for optimal RNA-seq studies and provide an overview of each step, from RNA-seq experimental design to bioinformatics approaches to data analysis. Insights that have resulted from RNA-seq studies across a wide range of non-model decapod species are also summarized.

A Workflow Guide to RNA-seq Analysis of Chaperone Function and Beyond.

RNA sequencing (RNA-seq) is a powerful method of transcript analysis that allows for the sequence identification and quantification of cellular transcripts. RNA-seq has many applications including differential gene expression (DE) analysis, gene fusion detection, allele-specific expression, isoform and splice variant quantification, and identification of novel genes. These applications can be used for downstream systems biology analyses such as gene ontology analysis to provide insights into cellular processes altered between biological conditions. Given the wide range of signaling pathways subject to chaperone activity as well as numerous chaperone functions in RNA metabolism, RNA-seq may provide a valuable tool for the study of chaperone proteins in biology and disease. This chapter outlines an example RNA-seq workflow to determine differentially expressed (DE) genes between two or more sample conditions and provides some considerations for RNA-seq experimental design.

Dual RNA-Sequencing to Elucidate the Plant-Pathogen Duel.

RNA-sequencing technology has been widely adopted to investigate host responses during infection with pathogens. Dual RNA-sequencing (RNA-seq) allows the simultaneous capture of pathogen specific transcripts during infection, providing a more complete view of the interaction. In this review, we focus on the design of dual RNA-seq experiments and the application of downstream data analysis to gain biological insight into both sides of the interaction. Recent literature in this area demonstrates the power of the dual RNA-seq approach and shows that it is not limited to model systems where genomic resources are available. A reduction in sequencing cost and single cell transcriptomics coupled with protein and metabolite level dual approaches are set to enhance our understanding of plant-pathogen interactions. Sequencing costs continue to decrease and single cell transcriptomics is becoming more feasible. In combination with proteomics and metabolomics studies, these technological advances are likely to contribute to our understanding of the temporal and spatial aspects of dynamic plant-pathogen interactions.

PowsimR: power analysis for bulk and single cell RNA-seq experiments.

Power analysis is essential to optimize the design of RNA-seq experiments and to assess and compare the power to detect differentially expressed genes in RNA-seq data. PowsimR is a flexible tool to simulate and evaluate differential expression from bulk and especially single-cell RNA-seq data making it suitable for a priori and posterior power analyses. The R package and associated tutorial are freely available at https://github.com/bvieth/powsimR. vieth@bio.lmu.de or hellmann@bio.lmu.de. Supplementary data are available at Bioinformatics online.

Inference and Sample Size Calculations Based on Statistical Tests in a Negative Binomial Distribution for Differential Gene Expression in RNAseq Data

The high throughput RNA sequencing (RNA-seq) technology has become the popular method of choice for transcriptomics and the detection of differentially expressed genes. Sample size calculations for RNA-seq experimental design are an important consideration in biological research and clinical trials. Currently, the sample size formulas derived from the Wald and the likelihood ratio statistical tests with a Poisson distribution to model RNA-seq data have been developed. However, since the mean read counts in the real RNA-seq data are not equal to the variance, an extended method to calculate sample sizes based on a negative binomial distribution using an exact test statistic was proposed by Li et al. in 2013. In this study, we alternatively derive five sample size calculation methods based on the negative binomial distribution using the Wald test, the log-transformed Wald test and the log-likelihood ratio test statistics. A comparison of our five methods and an existing method was performed by calculating the sample sizes and the simulated power in different scenarios. We first calculated the sample sizes for testing a single gene using the six methods given a nominal significance level α at 0.05 and 80% power. Then, we calculated the sample sizes for testing multiple genes given a false discovery rate (FDR) at 0.05 and 0.10. The empirical power and true prognostic genes for differential gene expression analysis corresponding to the estimated sample sizes from the six methods are also estimated via the simulation studies. Using the sample size formulas derived from log-transformed and Wald-based tests, we observed smaller sample properties while maintaining the nominal power close to or higher than 80% in all the settings compared to other methods. Moreover, the Wald test based sample size calculation method is easier to compute and faster in an RNA-seq experimental design.

In Papyro Comparison of TMM (edgeR), RLE (DESeq2), and MRN Normalization Methods for a Simple Two-Conditions-Without-Replicates RNA-Seq Experimental Design.

In the past 5 years, RNA-Seq has become a powerful tool in transcriptome analysis even though computational methods dedicated to the analysis of high-throughput sequencing data are yet to be standardized. It is, however, now commonly accepted that the choice of a normalization procedure is an important step in such a process, for example in differential gene expression analysis. The present article highlights the similarities between three normalization methods: TMM from edgeR R package, RLE from DESeq2 R package, and MRN. Both TMM and DESeq2 are widely used for differential gene expression analysis. This paper introduces properties that show when these three methods will give exactly the same results. These properties are proven mathematically and illustrated by performing in silico calculations on a given RNA-Seq data set.

Statistical modeling of isoform splicing dynamics from RNA-seq time series data.

Isoform quantification is an important goal of RNA-seq experiments, yet it remains problematic for genes with low expression or several isoforms. These difficulties may in principle be ameliorated by exploiting correlated experimental designs, such as time series or dosage response experiments. Time series RNA-seq experiments, in particular, are becoming increasingly popular, yet there are no methods that explicitly leverage the experimental design to improve isoform quantification. Here, we present DICEseq, the first isoform quantification method tailored to correlated RNA-seq experiments. DICEseq explicitly models the correlations between different RNA-seq experiments to aid the quantification of isoforms across experiments. Numerical experiments on simulated datasets show that DICEseq yields more accurate results than state-of-the-art methods, an advantage that can become considerable at low coverage levels. On real datasets, our results show that DICEseq provides substantially more reproducible and robust quantifications, increasing the correlation of estimates from replicate datasets by up to 10% on genes with low or moderate expression levels (bottom third of all genes). Furthermore, DICEseq permits to quantify the trade-off between temporal sampling of RNA and depth of sequencing, frequently an important choice when planning experiments. Our results have strong implications for the design of RNA-seq experiments, and offer a novel tool for improved analysis of such datasets. Python code is freely available at http://diceseq.sf.net G.Sanguinetti@ed.ac.uk Supplementary data are available at Bioinformatics online.

Sample size calculation while controlling false discovery rate for differential expression analysis with RNA-sequencing experiments.

BackgroundRNA-Sequencing (RNA-seq) experiments have been popularly applied to transcriptome studies in recent years. Such experiments are still relatively costly. As a result, RNA-seq experiments often employ a small number of replicates. Power analysis and sample size calculation are challenging in the context of differential expression analysis with RNA-seq data. One challenge is that there are no closed-form formulae to calculate power for the popularly applied tests for differential expression analysis. In addition, false discovery rate (FDR), instead of family-wise type I error rate, is controlled for the multiple testing error in RNA-seq data analysis. So far, there are very few proposals on sample size calculation for RNA-seq experiments.ResultsIn this paper, we propose a procedure for sample size calculation while controlling FDR for RNA-seq experimental design. Our procedure is based on the weighted linear model analysis facilitated by the voom method which has been shown to have competitive performance in terms of power and FDR control for RNA-seq differential expression analysis. We derive a method that approximates the average power across the differentially expressed genes, and then calculate the sample size to achieve a desired average power while controlling FDR. Simulation results demonstrate that the actual power of several popularly applied tests for differential expression is achieved and is close to the desired power for RNA-seq data with sample size calculated based on our method.ConclusionsOur proposed method provides an efficient algorithm to calculate sample size while controlling FDR for RNA-seq experimental design. We also provide an R package ssizeRNA that implements our proposed method and can be downloaded from the Comprehensive R Archive Network (http://cran.r-project.org).Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0994-9) contains supplementary material, which is available to authorized users.

The power and promise of RNA-seq in ecology and evolution.

Reference is regularly made to the power of new genomic sequencing approaches. Using powerful technology, however, is not the same as having the necessary power to address a research question with statistical robustness. In the rush to adopt new and improved genomic research methods, limitations of technology and experimental design may be initially neglected. Here, we review these issues with regard to RNA sequencing (RNA-seq). RNA-seq adds large-scale transcriptomics to the toolkit of ecological and evolutionary biologists, enabling differential gene expression (DE) studies in nonmodel species without the need for prior genomic resources. High biological variance is typical of field-based gene expression studies and means that larger sample sizes are often needed to achieve the same degree of statistical power as clinical studies based on data from cell lines or inbred animal models. Sequencing costs have plummeted, yet RNA-seq studies still underutilize biological replication. Finite research budgets force a trade-off between sequencing effort and replication in RNA-seq experimental design. However, clear guidelines for negotiating this trade-off, while taking into account study-specific factors affecting power, are currently lacking. Study designs that prioritize sequencing depth over replication fail to capitalize on the power of RNA-seq technology for DE inference. Significant recent research effort has gone into developing statistical frameworks and software tools for power analysis and sample size calculation in the context of RNA-seq DE analysis. We synthesize progress in this area and derive an accessible rule-of-thumb guide for designing powerful RNA-seq experiments relevant in eco-evolutionary and clinical settings alike.

Experimental Design and Power Calculation for RNA-seq Experiments.

Power calculation is a critical component of RNA-seq experimental design. The flexibility of RNA-seq experiment and the wide dynamic range of transcription it measures make it an attractive technology for whole transcriptome analysis. These features, in addition to the high dimensionality of RNA-seq data, bring complexity in experimental design, making an analytical power calculation no longer realistic. In this chapter we review the major factors that influence the statistical power of detecting differential expression, and give examples of power assessment using the R package PROPER.

RNAseqPS: A Web Tool for Estimating Sample Size and Power for RNAseq Experiment.

Sample size and power determination is the first step in the experimental design of a successful study. Sample size and power calculation is required for applications for National Institutes of Health (NIH) funding. Sample size and power calculation is well established for traditional biological studies such as mouse model, genome wide association study (GWAS), and microarray studies. Recent developments in high-throughput sequencing technology have allowed RNAseq to replace microarray as the technology of choice for high-throughput gene expression profiling. However, the sample size and power analysis of RNAseq technology is an underdeveloped area. Here, we present RNAseqPS, an advanced online RNAseq power and sample size calculation tool based on the Poisson and negative binomial distributions. RNAseqPS was built using the Shiny package in R. It provides an interactive graphical user interface that allows the users to easily conduct sample size and power analysis for RNAseq experimental design. RNAseqPS can be accessed directly at http://cqs.mc.vanderbilt.edu/shiny/RNAseqPS/.

Evolutionary Ecological Genomics

The recognition that evolution can happen on ecological timescales (Hairston et al. 2005; Pelletier et al. 2009; Ellner et al. 2011; Becks et al. 2012) has prompted the integration of ecology and evolution, while easier access to high-throughput sequencing technologies has increased the number of genetic nonmodel species entering the ‘omics’ era (e.g. Turner et al. 2010; Colbourne et al. 2011; Jones et al. 2012). We are now in a position to identify the genetic basis of adaptation and the mechanisms of adaptive responses in the wild. It nonetheless remains a challenge to go beyond descriptive measures of patterns of genetic variation and to identify the evolutionary processes driving species adaptation and evolution. This special issue represents a broad cross-section of research into evolutionary adaptation at the genetic level. The approaches used vary from classic QTL studies to RAD sequencing and RNAseq. Given the rapid advance of sequencing technology, we fully expect that ‘genomic’ as defined here will be merely ‘genetic’ in a few years, but we nonetheless hope that the results and methods described in this special issue will serve as a blueprint for future work in this field.

Challenges and strategies in transcriptome assembly and differential gene expression quantification. A comprehensive in silico assessment of RNA‐seq experiments

Transcriptome Shotgun Sequencing (RNA-seq) has been readily embraced by geneticists and molecular ecologists alike. As with all high-throughput technologies, it is critical to understand which analytic strategies are best suited and which parameters may bias the interpretation of the data. Here we use a comprehensive simulation approach to explore how various features of the transcriptome (complexity, degree of polymorphism π, alternative splicing), technological processing (sequencing error ε, library normalization) and bioinformatic workflow (de novo vs. mapping assembly, reference genome quality) impact transcriptome quality and inference of differential gene expression (DE). We find that transcriptome assembly and gene expression profiling (EdgeR vs. BaySeq software) works well even in the absence of a reference genome and is robust across a broad range of parameters. We advise against library normalization and in most situations advocate mapping assemblies to an annotated genome of a divergent sister clade, which generally outperformed de novo assembly (Trans-Abyss, Trinity, Soapdenovo-Trans). Transcriptome complexity (size, paralogs, alternative splicing isoforms) negatively affected the assembly and DE profiling, whereas the effects of sequencing error and polymorphism were almost negligible. Finally, we highlight the challenge of gene name assignment for de novo assemblies, the importance of mapping strategies and raise awareness of challenges associated with the quality of reference genomes. Overall, our results have significant practical and methodological implications and can provide guidance in the design and analysis of RNA-seq experiments, particularly for organisms where genomic background information is lacking.