Short paired-end reads trump long single-end reads for expression analysis

De novo assembly of transcriptomes and differential gene expression analysis using short-read data from emerging model organisms - a brief guide

Many questions in biology benefit greatly from the use of a variety of model systems. High-throughput sequencing methods have been a triumph in the democratization of diverse model systems. They allow for the economical sequencing of an entire genome or transcriptome of interest, and with technical variations can even provide insight into genome organization and the expression and regulation of genes. The analysis and biological interpretation of such large datasets can present significant challenges that depend on the 'scientific status' of the model system. While high-quality genome and transcriptome references are readily available for well-established model systems, the establishment of such references for an emerging model system often requires extensive resources such as finances, expertise and computation capabilities. The de novo assembly of a transcriptome represents an excellent entry point for genetic and molecular studies in emerging model systems as it can efficiently assess gene content while also serving as a reference for differential gene expression studies. However, the process of de novo transcriptome assembly is non-trivial, and as a rule must be empirically optimized for every dataset. For the researcher working with an emerging model system, and with little to no experience with assembling and quantifying short-read data from the Illumina platform, these processes can be daunting. In this guide we outline the major challenges faced when establishing a reference transcriptome de novo and we provide advice on how to approach such an endeavor. We describe the major experimental and bioinformatic steps, provide some broad recommendations and cautions for the newcomer to de novo transcriptome assembly and differential gene expression analyses. Moreover, we provide an initial selection of tools that can assist in the journey from raw short-read data to assembled transcriptome and lists of differentially expressed genes.

Comparative Evolutionary Genomics in Insects

Genome sequencing quality, in terms of both read length and accuracy, is constantly improving. By combining long-read sequencing technologies with various scaffolding techniques, chromosome-level genome assemblies are now achievable at an affordable price for non-model organisms. Insects represent an exciting taxon for studying the genomic underpinnings of evolutionary innovations, due to ancient origins, immense species-richness, and broad phenotypic diversity. Here we summarize some of the most important methods for carrying out a comparative genomics study on insects. We describe available tools and offer concrete tips on all stages of such an endeavor from DNA extraction through genome sequencing, annotation, and several evolutionary analyses. Along the way we describe important insect-specific aspects, such as DNA extraction difficulties or gene families that are particularly difficult to annotate, and offer solutions. We describe results from several examples of comparative genomics analyses on insects to illustrate the fascinating questions that can now be addressed in this new age of genomics research.

Transcriptome-Powered Pluripotent Stem Cell Differentiation for Regenerative Medicine

Pluripotent stem cells are endless sources for in vitro engineering human tissues for regenerative medicine. Extensive studies have demonstrated that transcription factors are the key to stem cell lineage commitment and differentiation efficacy. As the transcription factor profile varies depending on the cell type, global transcriptome analysis through RNA sequencing (RNAseq) has been a powerful tool for measuring and characterizing the success of stem cell differentiation. RNAseq has been utilized to comprehend how gene expression changes as cells differentiate and provide a guide to inducing cellular differentiation based on promoting the expression of specific genes. It has also been utilized to determine the specific cell type. This review highlights RNAseq techniques, tools for RNAseq data interpretation, RNAseq data analytic methods and their utilities, and transcriptomics-enabled human stem cell differentiation. In addition, the review outlines the potential benefits of the transcriptomics-aided discovery of intrinsic factors influencing stem cell lineage commitment, transcriptomics applied to disease physiology studies using patients' induced pluripotent stem cell (iPSC)-derived cells for regenerative medicine, and the future outlook on the technology and its implementation.

Fibrosis in Pathology of Heart and Kidney: From Deep RNA-Sequencing to Novel Molecular Targets

Diseases of the heart and the kidney, including heart failure and chronic kidney disease, can dramatically impair life expectancy and the quality of life of patients. The heart and kidney form a functional axis; therefore, functional impairment of 1 organ will inevitably affect the function of the other. Fibrosis represents the common final pathway of diseases of both organs, regardless of the disease entity. Thus, inhibition of fibrosis represents a promising therapeutic approach to treat diseases of both organs and to resolve functional impairment. However, despite the growing knowledge in this field, the exact pathomechanisms that drive fibrosis remain elusive. RNA-sequencing approaches, particularly single-cell RNA-sequencing, have revolutionized the investigation of pathomechanisms at a molecular level and facilitated the discovery of disease-associated cell types and mechanisms. In this review, we give a brief overview over the evolution of RNA-sequencing techniques, summarize most recent insights into the pathogenesis of heart and kidney fibrosis, and discuss how transcriptomic data can be used, to identify new drug targets and to develop novel therapeutic strategies.

11-Cl-BBQ, a select modulator of AhR-regulated transcription, suppresses lung cancer cell growth via activation of p53 and p27Kip1

Aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor and functions as a tumour suppressor in different cancer models. In the present study, we report detailed characterization of 11-chloro-7H-benzimidazo[2,1-a]benzo[de]iso-quinolin-7-one (11-Cl-BBQ) as a select modulator of AhR-regulated transcription (SMAhRT) with anti-cancer actions. Treatment of lung cancer cells with 11-Cl-BBQ induced potent and sustained AhR-dependent anti-proliferative effects by promoting G1 phase cell cycle arrest. Investigation of 11-Cl-BBQ-induced transcription in H460 cells with or without the AhR expression by RNA-sequencing revealed activation of p53 signalling. In addition, 11-Cl-BBQ suppressed multiple pathways involved in DNA replication and increased expression of cyclin-dependent kinase inhibitors, including p27Kip1 , in an AhR-dependent manner. CRISPR/Cas9 knockout of individual genes revealed the requirement for both p53 and p27Kip1 for the AhR-mediated anti-proliferative effects. Our results identify 11-Cl-BBQ as a potential lung cancer therapeutic, highlight the feasibility of targeting AhR and provide important mechanistic insights into AhR-mediated-anticancer actions.

Evaluation of methods to detect circular RNAs from single-end RNA-sequencing data

Background

Circular RNA (circRNA), a class of RNA molecule with a loop structure, has recently attracted researchers due to its diverse biological functions and potential biomarkers of human diseases. Most of the current circRNA detection methods from RNA-sequencing (RNA-Seq) data utilize the mapping information of paired-end (PE) reads to eliminate false positives. However, much of the practical RNA-Seq data such as cross-linking immunoprecipitation sequencing (CLIP-Seq) data usually contain single-end (SE) reads. It is not clear how well these tools perform on SE RNA-Seq data.

Results

In this study, we present a systematic evaluation of six advanced RNA-based methods and two CLIP-Seq based methods for detecting circRNAs from SE RNA-Seq data. The performances of the methods are rigorously assessed based on precision, sensitivity, F1 score, and true discovery rate. We investigate the impacts of read length, false positive ratio, sequencing depth and PE mapping information on the performances of the methods using simulated SE RNA-Seq simulated datasets. The real datasets used in this study consist of four experimental RNA-Seq datasets with ≥100bp read length and 124 CLIP-Seq samples from 45 studies that contain mostly short-read (≤50bp) RNA-Seq data. The simulation study shows that the sensitivities of most of the methods can be improved by increasing either read length or sequencing depth, and that the levels of false positive rates significantly affect the precision of all methods. Furthermore, the PE mapping information can improve the method’s precision but can not always guarantee the increase of F1 score. Overall, no method is dominant for all SE RNA-Seq data. The RNA-based methods perform better for the long-read datasets but are worse for the short-read datasets. In contrast, the CLIP-Seq based methods outperform the RNA-Seq based methods for all the short-read samples. Combining the results of these methods can significantly improve precision in the CLIP-Seq data.

Conclusions

The results provide a systematic evaluation of circRNA detection methods on SE RNA-Seq data that would facilitate researchers’ strategies in circRNA analysis.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12864-022-08329-7).

how_are_we_stranded_here: quick determination of RNA-Seq strandedness

Background

Quality control checks are the first step in RNA-Sequencing analysis, which enable the identification of common issues that occur in the sequenced reads. Checks for sequence quality, contamination, and complexity are commonplace, and allow users to implement steps downstream which can account for these issues. Strand-specificity of reads is frequently overlooked and is often unavailable even in published data, yet when unknown or incorrectly specified can have detrimental effects on the reproducibility and accuracy of downstream analyses.

Results

To address these issues, we developed how_are_we_stranded_here, a Python library that helps to quickly infer strandedness of paired-end RNA-Sequencing data. Testing on both simulated and real RNA-Sequencing reads showed that it correctly measures strandedness, and measures outside the normal range may indicate sample contamination.

Conclusions

how_are_we_stranded_here is fast and user friendly, making it easy to implement in quality control pipelines prior to analysing RNA-Sequencing data. how_are_we_stranded_here is freely available at https://github.com/betsig/how_are_we_stranded_here.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04572-7.

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.

Comparison of EM-seq and PBAT methylome library methods for low-input DNA

DNA methylation is the most studied epigenetic mark involved in regulation of gene expression. For low input samples, a limited number of methods for quantifying DNA methylation genome-wide has been evaluated. Here, we compared a series of input DNA amounts (1–10ng) from two methylome library preparation protocols, enzymatic methyl-seq (EM-seq) and post-bisulfite adaptor tagging (PBAT) adapted from single-cell PBAT. EM-seq takes advantage of enzymatic activity while PBAT relies on conventional bisulfite conversion for detection of DNA methylation. We found that both methods accurately quantified DNA methylation genome-wide. They produced expected distribution patterns around genomic features, high C-T transition efficiency at non-CpG sites and high correlation between input amounts. However, EM-seq performed better in regard to library and sequencing quality, i.e. EM-seq produced larger insert sizes, higher alignment rates and higher library complexity with lower duplication rate compared to PBAT. Moreover, EM-seq demonstrated higher CpG coverage, better CpG site overlap and higher consistency between input series. In summary, our data suggests that EM-seq overall performed better than PBAT in whole-genome methylation quantification of low input samples.

HRT Atlas v1.0 database: redefining human and mouse housekeeping genes and candidate reference transcripts by mining massive RNA-seq datasets

Housekeeping (HK) genes are constitutively expressed genes that are required for the maintenance of basic cellular functions. Despite their importance in the calibration of gene expression, as well as the understanding of many genomic and evolutionary features, important discrepancies have been observed in studies that previously identified these genes. Here, we present Housekeeping and Reference Transcript Atlas (HRT Atlas v1.0, www.housekeeping.unicamp.br) a web-based database which addresses some of the previously observed limitations in the identification of these genes, and offers a more accurate database of human and mouse HK genes and transcripts. The database was generated by mining massive human and mouse RNA-seq data sets, including 11 281 and 507 high-quality RNA-seq samples from 52 human non-disease tissues/cells and 14 healthy tissues/cells of C57BL/6 wild type mouse, respectively. User can visualize the expression and download lists of 2158 human HK transcripts from 2176 HK genes and 3024 mouse HK transcripts from 3277 mouse HK genes. HRT Atlas also offers the most stable and suitable tissue selective candidate reference transcripts for normalization of qPCR experiments. Specific primers and predicted modifiers of gene expression for some of these HK transcripts are also proposed. HRT Atlas has also been integrated with a regulatory elements resource from Epiregio server.