Feasibility of sample size calculation for RNA-seq studies

Integrated analysis of the transcriptome and its interaction with the metabolome in metabolic associated fatty liver disease: Gut microbiome signatures, correlation networks, and effect of PNPLA3 genotype

Interactions between communities of the gut microbiome and with the host could affect the onset and progression of metabolic associated fatty liver disease (MAFLD), and can be useful as new diagnostic and prognostic biomarkers. In this study, we performed a multi-omics approach to unravel gut microbiome signatures from 32 biopsy-proven patients (10 simple steatosis -SS- and 22 steatohepatitis -SH-) and 19 healthy volunteers (HV). Human and microbial transcripts were differentially identified between groups (MAFLD vs. HV/SH vs. SS), and analyzed for weighted correlation networks together with previously detected metabolites from the same set of samples. We observed that expression of Desulfobacteraceae bacterium, methanogenic archaea, Mushu phage, opportunistic pathogenic fungi Fusarium proliferatum and Candida sorbophila, protozoa Blastocystis spp. and Fonticula alba were upregulated in MAFLD and SH. Desulfobacteraceae bacterium and Mushu phage were hub species in the onset of MAFLD, whereas the activity of Fonticula alba, Faecalibacterium prausnitzii, and Mushu phage act as key regulators of the progression to SH. A combination of clinical, metabolomic, and transcriptomic parameters showed the highest predictive capacity for MAFLD and SH (AUC = 0.96). In conclusion, faecal microbiome markers from several community members contribute to the switch in signatures characteristic of MAFLD and its progression towards SH.

Medical Education in the Context of the Eastern Mediterranean Region: Professional Development Activity of Health Professionals

Background

The development and maintenance of a high-quality medical education workforce depend on continuing medical education (CME). Traditionally, CME is delivered face-to-face, but due to COVID-19 and geographical distances, it is challenging to conduct professional development activities for several days. Using a webinar on advancement in medical education in the context of the eastern Mediterranean, we aimed to assess the participants' perspectives towards the professional development activity using a synchronous learning approach.

Methods

We used a cross-sectional survey-based study design. We invited faculty members from King Saud bin Abdulaziz University for Health Sciences (KSAU-HS) and United Arab Emirates University for Health Sciences (UAEU). We assessed their perspectives on the relevance of the content and effectiveness of the activity on their knowledge and skills after the two days' webinar series. A self-designed questionnaire was administered post-webinar immediately. Open-ended responses were analyzed thematically.

Results

One hundred thirty-six registered healthcare professionals attended day 1, and 97 registered participants joined on the second day of the webinar. Most participants appreciated the diversity of the contents, the quality of the presentations, and the expertise of the facilitators. They reported that the content optimized their knowledge and understanding of new concepts such as assessment in simulation teaching, programmatic assessment, insight into the implementation of IPE and EPAs in CBME, and so on. The e-learning platform's user accessibility, online tutor interaction, and the addition of more scenario-based case studies were all recommended for improvement.

Conclusion

Overall the two days webinar series presentations were informative and highlighted the transformation in medical practices. Suggestions to improve the quality of the webinars and content were discussed.

Comprehensive circRNA Analyses in Human Vertebrae of GIOP and Its Molecular Mechanism

Circular RNAs (circRNAs) are a novel class of noncoding RNAs that play important roles in human diseases. However, the regulation of circRNAs in glucocorticoid-induced osteoporosis (GIOP) has not been reported. In this study, we performed high-throughput sequencing to identify altered circRNAs in the vertebrae from GIOP patients. A total of 65 clinical samples were collected in this study. Bioinformatics algorithms were employed to predict the target relationship between circRNAs and miRNAs and the circRNAs-miRNAs regulatory network. We focused on the top 10 significantly up-/downregulated circRNAs (hsa_circ_0004906, hsa_circ_0001172, hsa_circ_0005778, hsa_circ_0004276, hsa_circ_0005729, hsa_circ_0006173, hsa_circ_0007662, hsa_circ_0001451, hsa_circ_0001564, and hsa_circ_0108735) and measured their expression by qRT-PCR in clinical samples. Bioinformatics analyses demonstrated that 87 miRNAs were predicted in upregulated circRNAs and 104 miRNAs were predicted in downregulated circRNAs. The functional enrichment analysis showed these targeted miRNAs were significantly enriched in bone metabolism-related biological processes and pathways, including the MAPK signaling pathway, positive regulation of the metabolic process and metabolic pathways, etc. Collectively, our study revealed circRNA regulation and circRNAs-miRNAs regulatory network in GIOP for the first time, which provides a new perspective on the molecular mechanism of GIOP and lays a foundation for GIOP treatment.

The Vacc-SeqQC project: Benchmarking RNA-Seq for clinical vaccine studies

Introduction

Over the last decade, the field of systems vaccinology has emerged, in which high throughput transcriptomics and other omics assays are used to probe changes of the innate and adaptive immune system in response to vaccination. The goal of this study was to benchmark key technical and analytical parameters of RNA sequencing (RNA-seq) in the context of a multi-site, double-blind randomized vaccine clinical trial.

Methods

We collected longitudinal peripheral blood mononuclear cell (PBMC) samples from 10 subjects before and after vaccination with a live attenuated Francisella tularensis vaccine and performed RNA-Seq at two different sites using aliquots from the same sample to generate two replicate datasets (5 time points for 50 samples each). We evaluated the impact of (i) filtering lowly-expressed genes, (ii) using external RNA controls, (iii) fold change and false discovery rate (FDR) filtering, (iv) read length, and (v) sequencing depth on differential expressed genes (DEGs) concordance between replicate datasets. Using synthetic mRNA spike-ins, we developed a method for empirically establishing minimal read-count thresholds for maintaining fold change accuracy on a per-experiment basis. We defined a reference PBMC transcriptome by pooling sequence data and established the impact of sequencing depth and gene filtering on transcriptome representation. Lastly, we modeled statistical power to detect DEGs for a range of sample sizes, effect sizes, and sequencing depths.

Results and Discussion

Our results showed that (i) filtering lowly-expressed genes is recommended to improve fold-change accuracy and inter-site agreement, if possible guided by mRNA spike-ins (ii) read length did not have a major impact on DEG detection, (iii) applying fold-change cutoffs for DEG detection reduced inter-set agreement and should be used with caution, if at all, (iv) reduction in sequencing depth had a minimal impact on statistical power but reduced the identifiable fraction of the PBMC transcriptome, (v) after sample size, effect size (i.e. the magnitude of fold change) was the most important driver of statistical power to detect DEG. The results from this study provide RNA sequencing benchmarks and guidelines for planning future similar vaccine studies.

scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies

Single cell RNA-seq has revolutionized transcriptomics by providing cell type resolution for differential gene expression and expression quantitative trait loci (eQTL) analyses. However, efficient power analysis methods for single cell data and inter-individual comparisons are lacking. Here, we present scPower; a statistical framework for the design and power analysis of multi-sample single cell transcriptomic experiments. We modelled the relationship between sample size, the number of cells per individual, sequencing depth, and the power of detecting differentially expressed genes within cell types. We systematically evaluated these optimal parameter combinations for several single cell profiling platforms, and generated broad recommendations. In general, shallow sequencing of high numbers of cells leads to higher overall power than deep sequencing of fewer cells. The model, including priors, is implemented as an R package and is accessible as a web tool. scPower is a highly customizable tool that experimentalists can use to quickly compare a multitude of experimental designs and optimize for a limited budget.

Harmonization of quality metrics and power calculation in multi-omic studies

Multi-omic studies combine measurements at different molecular levels to build comprehensive models of cellular systems. The success of a multi-omic data analysis strategy depends largely on the adoption of adequate experimental designs, and on the quality of the measurements provided by the different omic platforms. However, the field lacks a comparative description of performance parameters across omic technologies and a formulation for experimental design in multi-omic data scenarios. Here, we propose a set of harmonized Figures of Merit (FoM) as quality descriptors applicable to different omic data types. Employing this information, we formulate the MultiPower method to estimate and assess the optimal sample size in a multi-omics experiment. MultiPower supports different experimental settings, data types and sample sizes, and includes graphical for experimental design decision-making. MultiPower is complemented with MultiML, an algorithm to estimate sample size for machine learning classification problems based on multi-omic data.

Seq-ing answers: Current data integration approaches to uncover mechanisms of transcriptional regulation

Advancements in the field of next generation sequencing lead to the generation of ever-more data, with the challenge often being how to combine and reconcile results from different OMICs studies such as genome, epigenome and transcriptome. Here we provide an overview of the standard processing pipelines for ChIP-seq and RNA-seq as well as common downstream analyses. We describe popular multi-omics data integration approaches used to identify target genes and co-factors, and we discuss how machine learning techniques may predict transcriptional regulators and gene expression.

Differential Expression Analysis of RNA-seq Reads: Overview, Taxonomy, and Tools

Analysis of RNA-sequence (RNA-seq) data is widely used in transcriptomic studies and it has many applications. We review RNA-seq data analysis from RNA-seq reads to the results of differential expression analysis. In addition, we perform a descriptive comparison of tools used in each step of RNA-seq data analysis along with a discussion of important characteristics of these tools. A taxonomy of tools is also provided. A discussion of issues in quality control and visualization of RNA-seq data is also included along with useful tools. Finally, we provide some guidelines for the RNA-seq data analyst, along with research issues and challenges which should be addressed.

BRB-seq: ultra-affordable high-throughput transcriptomics enabled by bulk RNA barcoding and sequencing

Despite its widespread use, RNA-seq is still too laborious and expensive to replace RT-qPCR as the default gene expression analysis method. We present a novel approach, BRB-seq, which uses early multiplexing to produce 3' cDNA libraries for dozens of samples, requiring just 2 hours of hands-on time. BRB-seq has a comparable performance to the standard TruSeq approach while showing greater tolerance for lower RNA quality and being up to 25 times cheaper. We anticipate that BRB-seq will transform basic laboratory practice given its capacity to generate genome-wide transcriptomic data at a similar cost as profiling four genes using RT-qPCR.

Empirical assessment of the impact of sample number and read depth on RNA-Seq analysis workflow performance

BACKGROUND

RNA-Sequencing analysis methods are rapidly evolving, and the tool choice for each step of one common workflow, differential expression analysis, which includes read alignment, expression modeling, and differentially expressed gene identification, has a dramatic impact on performance characteristics. Although a number of workflows are emerging as high performers that are robust to diverse input types, the relative performance characteristics of these workflows when either read depth or sample number is limited-a common occurrence in real-world practice-remain unexplored.

RESULTS

Here, we evaluate the impact of varying read depth and sample number on the performance of differential gene expression identification workflows, as measured by precision, or the fraction of genes correctly identified as differentially expressed, and by recall, or the fraction of differentially expressed genes identified. We focus our analysis on 30 high-performing workflows, systematically varying the read depth and number of biological replicates of patient monocyte samples provided as input. We find that, in general for most workflows, read depth has little effect on workflow performance when held above two million reads per sample, with reduced workflow performance below this threshold. The greatest impact of decreased sample number is seen below seven samples per group, when more heterogeneity in workflow performance is observed. The choice of differential expression identification tool, in particular, has a large impact on the response to limited inputs.

CONCLUSIONS

Among the tested workflows, the recall/precision balance remains relatively stable at a range of read depths and sample numbers, although some workflows are more sensitive to input restriction. At ranges typically recommended for biological studies, performance is more greatly impacted by the number of biological replicates than by read depth. Caution should be used when selecting analysis workflows and interpreting results from low sample number experiments, as all workflows exhibit poorer performance at lower sample numbers near typically reported values, with variable impact on recall versus precision. These analyses highlight the performance characteristics of common differential gene expression workflows at varying read depths and sample numbers, and provide empirical guidance in experimental and analytical design.

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

Summary

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.

Availability and implementation

The R package and associated tutorial are freely available at https://github.com/bvieth/powsimR.

Contact

vieth@bio.lmu.de or hellmann@bio.lmu.de.

Supplementary information

Supplementary data are available at Bioinformatics online.

Maternal Choline Supplementation during Normal Murine Pregnancy Alters the Placental Epigenome: Results of an Exploratory Study

The placental epigenome regulates processes that affect placental and fetal development, and could be mediating some of the reported effects of maternal choline supplementation (MCS) on placental vascular development and nutrient delivery. As an extension of work previously conducted in pregnant mice, the current study sought to explore the effects of MCS on various epigenetic markers in the placenta. RNA and DNA were extracted from placentas collected on embryonic day 15.5 from pregnant mice fed a 1X or 4X choline diet, and were subjected to genome-wide sequencing procedures or mass-spectrometry-based assays to examine placental imprinted gene expression, DNA methylation patterns, and microRNA (miRNA) abundance. MCS yielded a higher (fold change = 1.63-2.25) expression of four imprinted genes (Ampd3, Tfpi2, Gatm and Aqp1) in the female placentas and a lower (fold change = 0.46-0.62) expression of three imprinted genes (Dcn, Qpct and Tnfrsf23) in the male placentas (false discovery rate (FDR) ≤ 0.05 for both sexes). Methylation in the promoter regions of these genes and global placental DNA methylation were also affected (p ≤ 0.05). Additionally, a lower (fold change = 0.3; P_unadjusted = 2.05 × 10^-4; FDR = 0.13) abundance of miR-2137 and a higher (fold change = 1.25-3.92; p < 0.05) expression of its target genes were detected in the 4X choline placentas. These data demonstrate that the placental epigenome is responsive to maternal choline intake during murine pregnancy and likely mediates some of the previously described choline-induced effects on placental and fetal outcomes.

Optimization of an RNA-Seq Differential Gene Expression Analysis Depending on Biological Replicate Number and Library Size

RNA-Seq is a widely used technology that allows an efficient genome-wide quantification of gene expressions for, for example, differential expression (DE) analysis. After a brief review of the main issues, methods and tools related to the DE analysis of RNA-Seq data, this article focuses on the impact of both the replicate number and library size in such analyses. While the main drawback of previous relevant studies is the lack of generality, we conducted both an analysis of a two-condition experiment (with eight biological replicates per condition) to compare the results with previous benchmark studies, and a meta-analysis of 17 experiments with up to 18 biological conditions, eight biological replicates and 100 million (M) reads per sample. As a global trend, we concluded that the replicate number has a larger impact than the library size on the power of the DE analysis, except for low-expressed genes, for which both parameters seem to have the same impact. Our study also provides new insights for practitioners aiming to enhance their experimental designs. For instance, by analyzing both the sensitivity and specificity of the DE analysis, we showed that the optimal threshold to control the false discovery rate (FDR) is approximately 2^-r, where r is the replicate number. Furthermore, we showed that the false positive rate (FPR) is rather well controlled by all three studied R packages: DESeq, DESeq2, and edgeR. We also analyzed the impact of both the replicate number and library size on gene ontology (GO) enrichment analysis. Interestingly, we concluded that increases in the replicate number and library size tend to enhance the sensitivity and specificity, respectively, of the GO analysis. Finally, we recommend to RNA-Seq practitioners the production of a pilot data set to strictly analyze the power of their experimental design, or the use of a public data set, which should be similar to the data set they will obtain. For individuals working on tomato research, on the basis of the meta-analysis, we recommend at least four biological replicates per condition and 20 M reads per sample to be almost sure of obtaining about 1000 DE genes if they exist.