A Guide for Designing and Analyzing RNA-Seq Data

Evolution and Plasticity of Gene Expression Under Progressive Warming in Drosophila subobscura

Understanding the molecular mechanisms of thermal adaptation is crucial to predict the impacts of global warming. However, there is still a lack of research on the effects of rising temperatures over time and of studies involving different populations from the same species. The present study focuses on these two aspects, which are of great importance in understanding how organisms cope and adapt to ongoing changes in their environment. This study investigates the impact of global warming on the gene expression patterns of Drosophila subobscura populations from two different latitudinal locations after 23 generations of evolution. Our results indicate that evolutionary changes depend on the genetic background of the populations, with different starting points for thermal evolution, and that high-latitude populations show more pronounced evolutionary changes, with some evidence of convergence towards low-latitude populations. We found an interplay between plasticity and selection, with the high-latitude population showing fewer initial plastic genes and lower levels of adaptive plasticity, but a greater magnitude of change in both plastic and selective responses during evolution under warming conditions compared with its low-latitude counterpart. A substantial proportion of the transcriptome was observed to be evolving, despite the lack of observable response at higher-order phenotypic traits. The interplay between plasticity and selection may prove to be an essential component in shaping species' evolutionary responses to climate change. Furthermore, the value of conducting studies on multiple populations of the same species is emphasised, given the identification of differences between populations with different backgrounds in several contexts.

Integrating Large Language Models in Bioinformatics Education for Medical Students: Opportunities and Challenges

Large language models (LLMs) offer transformative opportunities in bioinformatics education for medical students by creating interactive experiences. The integration of LLMs enhances educational outcomes through providing accessible code templates, clarifying the function of coding elements, and assisting in error troubleshooting. Here, we demonstrate the practical applications of LLMs with a case study on transcriptome sequencing data processing, a vital component of medical research. However, the reliability of the content that LLMs generate requires rigorous validation. Ensuring the accuracy and appropriateness of the LLM-generated information requires integrating innovative LLMs with traditional educational methods to prepare medical students effectively for future challenges in bioinformatics.

Single cell RNA-seq: a novel tool to unravel virus-host interplay

Over the last decade, single cell RNA sequencing (scRNA-seq) technology has caught the momentum of being a vital revolutionary tool to unfold cellular heterogeneity by high resolution assessment. It evades the inadequacies of conventional sequencing technology which was able to detect only average expression level among cell populations. In the era of twenty-first century, several epidemic and pandemic viruses have emerged. Being an intracellular entity, viruses totally rely on host. Complex virus-host dynamics result when the virus tend to obtain factors from host cell required for its replication and establishment of infection. As a prevailing tool, scRNA-seq is able to understand virus-host interplay by comprehensive transcriptome profiling. Because of technological and methodological advancement, this technology is capable to recognize viral genome and host cell response heterogeneity. Further development in analytical methods with multiomics approach and increased availability of accessible scRNA-seq datasets will improve the understanding of viral pathogenesis that can be helpful for development of novel antiviral therapeutic strategies.

RNA Sequencing Experimental Analysis Workflow Using Caenorhabditis elegans

RNA sequencing is an approach to transcriptomic profiling that enables the detection of differentially expressed genes in response to genetic mutation or experimental treatment, among other uses. Here we describe a method for the use of a customizable, user-friendly bioinformatic pipeline to identify differentially expressed genes in RNA sequencing data obtained from C. elegans, with attention to the improvement in reproducibility and accuracy of results.

Strategy for RNA-Seq Experimental Design and Data Analysis

Ribonucleic acids (RNAs) are fundamental molecules that control regulation and expression of the genome and therefore the function of a cell. Robust analysis and quantification of RNA transcripts hold critical importance in understanding cell function, altered phenotypes in different biological context, for understanding and targeting diseases. The development of RNA-sequencing (RNA-Seq) now provides opportunities to analyze the expression and function of RNA molecules at an unprecedented scale. However, the strategy for RNA-Seq experimental design and data analysis can substantially differ depending on the biological application. The design choice could also have significant impact for downstream results and interpretation of data. Here we describe key critical considerations required for RNA-Seq experimental design and also describe a step-by-step bioinformatics workflow detailing the different steps required for RNA-Seq data analysis. We believe this article will be a valuable guide for designing and analyzing RNA-Seq data to address a wide range of different biological questions.

Temporally integrated transcriptome analysis reveals ASFV pathology and host response dynamics

African swine fever virus (ASFV) causes a lethal swine hemorrhagic disease and is currently responsible for widespread damage to the pig industry. The pathogenesis of ASFV infection and its interaction with host responses remain poorly understood. In this study, we profiled the temporal viral and host transcriptomes in porcine alveolar macrophages (PAMs) with virulent and attenuated ASFV strains. We identified profound differences in the virus expression programs between SY18 and HuB20, which shed light on the pathogenic functions of several ASFV genes. Through integrated computational analysis and experimental validation, we demonstrated that compared to the virulent SY18 strain, the attenuated HuB20 quickly activates expression of receptors, sensors, regulators, as well as downstream effectors, including cGAS, STAT1/2, IRF9, MX1/2, suggesting rapid induction of a strong antiviral immune response in HuB20. Surprisingly, in addition to the pivotal DNA sensing mechanism mediated by cGAS-STING pathway, infection of the DNA virus ASFV activates genes associated with RNA virus response, with stronger induction by HuB20 infection. Taken together, this study reveals novel insights into the host-virus interaction dynamics, and provides reference for future mechanistic studies of ASFV pathogenicity.

Bioinformatics analysis reveals potential biomarkers associated with the occurrence of intracranial aneurysms

To better understand the molecular mechanisms of intracranial aneurysm (IA) pathogenesis, we used gene coexpression networks to identify hub genes and functional pathways associated with IA onset. Two Gene Expression Omnibus (GEO) datasets encompassing intracranial aneurysm tissue samples and cerebral artery control samples were included. To discover functional pathways and potential biomarkers, weighted gene coexpression network analysis was employed. Next, single-gene gene set enrichment analysis was employed to investigate the putative biological roles of the chosen genes. We also used receiver operating characteristic analysis to confirm the diagnostic results. Finally, we used a rat model to confirm the hub genes in the module of interest. The module of interest, which was designated the green module and included 115 hub genes, was the key module that was most strongly and negatively associated with IA formation. According to gene set variation analysis results, 15 immune-related pathways were significantly activated in the IA group, whereas 7 metabolic pathways were suppressed. In two GEO datasets, SLC2A12 could distinguish IAs from control samples. Twenty-nine hub genes in the green module might be biomarkers for the occurrence of cerebral aneurysms. SLC2A12 expression was significantly downregulated in both human and rat IA tissue. In the present study, we identified 115 hub genes related to the pathogenesis of IA onset and deduced their potential roles in various molecular pathways; this new information may contribute to the diagnosis and treatment of IAs. By external validation, the SLC2A12 gene may play an important role. The molecular function of SLC2A12 in the process of IA occurrence can be further studied in a rat model.

Advancements in Genomic and Behavioral Neuroscience Analysis for the Study of Normal and Pathological Brain Function

Psychiatric and neurological disorders are influenced by an undetermined number of genes and molecular pathways that may differ among afflicted individuals. Functionally testing and characterizing biological systems is essential to discovering the interrelationship among candidate genes and understanding the neurobiology of behavior. Recent advancements in genetic, genomic, and behavioral approaches are revolutionizing modern neuroscience. Although these tools are often used separately for independent experiments, combining these areas of research will provide a viable avenue for multidimensional studies on the brain. Herein we will briefly review some of the available tools that have been developed for characterizing novel cellular and animal models of human disease. A major challenge will be openly sharing resources and datasets to effectively integrate seemingly disparate types of information and how these systems impact human disorders. However, as these emerging technologies continue to be developed and adopted by the scientific community, they will bring about unprecedented opportunities in our understanding of molecular neuroscience and behavior.

Bibliometric review of ATAC-Seq and its application in gene expression

With recent advances in high-throughput next-generation sequencing, it is possible to describe the regulation and expression of genes at multiple levels. An assay for transposase-accessible chromatin using sequencing (ATAC-seq), which uses Tn5 transposase to sequence protein-free binding regions of the genome, can be combined with chromatin immunoprecipitation coupled with deep sequencing (ChIP-seq) and ribonucleic acid sequencing (RNA-seq) to provide a detailed description of gene expression. Here, we reviewed the literature on ATAC-seq and described the characteristics of ATAC-seq publications. We then briefly introduced the principles of RNA-seq, ChIP-seq and ATAC-seq, focusing on the main features of the techniques. We built a phylogenetic tree from species that had been previously studied by using ATAC-seq. Studies of Mus musculus and Homo sapiens account for approximately 90% of the total ATAC-seq data, while other species are still in the process of accumulating data. We summarized the findings from human diseases and other species, illustrating the cutting-edge discoveries and the role of multi-omics data analysis in current research. Moreover, we collected and compared ATAC-seq analysis pipelines, which allowed biological researchers who lack programming skills to better analyze and explore ATAC-seq data. Through this review, it is clear that multi-omics analysis and single-cell sequencing technology will become the mainstream approach in future research.

Optimization of RNA storage in a biobank, as well as methods for manual and automated isolation of RNA from whole blood and leukocyte fraction

Aim. To optimize the technique for the isolation and storage of ribonucleic acid (RNA) from whole blood and leukocyte fraction.

Materials and methods. Comparison of isolation quality was carried out for RNA samples obtained from 228 leukocyte samples and 198 whole blood samples. Isolation was performed from fresh and frozen samples using ExtractRNA™ reagent and a MagNA Pure Compact automated system. Various methods of removing erythrocytes (centrifugation and treatment with hemolytic agents from two manufacturers) were tested, as well as freezing with and without preservatives for subsequent RNA isolation.

Results. Twenty-one combinations of conditions were tested. The highest quality RNA was isolated by manual extraction using the ExtractRNA™ reagent from a fresh leukocyte fraction, purified by the Amplisens hemolytic agent (successful extraction — 94%, median RIN=8,4); frozen in IntactRNA™, purified by leukocyte fraction centrifugation (successful extraction — 100%, median RIN=8); frozen in ExtractRNA™, purified by leukocyte fraction centrifugation (successful extraction — 100%, median RIN=9,3).

Conclusion. RNA can be isolated from frozen blood fractions, which is not inferior in quality to that isolated from fresh samples. Thus, it is not necessary to isolate RNA immediately after the receipt of biological material.

Mouse Embryonic Fibroblast Adipogenic Potential: A Comprehensive Transcriptome Analysis

Our understanding of adipose tissue has progressed from an inert tissue for energy storage to be one of the largest endocrine organs regulating metabolic homoeostasis through its ability to synthesize and release various adipokines that regulate a myriad of pathways. The field of adipose tissue biology is growing due to this association with various chronic metabolic diseases. An important process in the regulation of adipose tissue biology is adipogenesis, which is the formation of new adipocytes. Investigating adipogenesis in vitro is currently a focus for identifying factors that might be utilized in clinically. A powerful tool for such work is high-throughput sequencing which can rapidly identify changes at gene expression level. Various cell models exist for studying adipogenesis and has been used in high-throughput studies, yet little is known about transcriptome profile that underlies adipogenesis in mouse embryonic fibroblasts. This study utilizes RNA-sequencing and computational analysis with DESeq2, gene ontology, protein–protein networks, and robust rank analysis to understand adipogenesis in mouse embryonic fibroblasts in-depth. Our analyses confirmed the requirement of mitotic clonal expansion prior to adipogenesis in this cell model and highlight the role of Cebpa and Cebpb in regulating adipogenesis through interactions of large numbers of genes.

Loss of Gene Information: Discrepancies between RNA Sequencing, cDNA Microarray, and qRT-PCR

Molecular analyses of normal and diseased cells give insight into changes in gene expression and help in understanding the background of pathophysiological processes. Years after cDNA microarrays were established in research, RNA sequencing (RNA-seq) became a key method of quantitatively measuring the transcriptome. In this study, we compared the detection of genes by each of the transcriptome analysis methods: cDNA array, quantitative RT-PCR, and RNA-seq. As expected, we found differences in the gene expression profiles of the aforementioned techniques. Here, we present selected genes that exemplarily demonstrate the observed differences and calculations to reveal that a strong RNA secondary structure, as well as sample preparation, can affect RNA-seq. In summary, this study addresses an important issue with a strong impact on gene expression analysis in general. Therefore, we suggest that these findings need to be considered when dealing with data from transcriptome analyses.

Transcriptional Reprogramming and Constitutive PD-L1 Expression in Melanoma Are Associated with Dedifferentiation and Activation of Interferon and Tumour Necrosis Factor Signalling Pathways

Simple Summary

Melanoma, an aggressive form of skin cancer, is frequently associated with drug resistance in the advanced stages. For instance, frequently resistance is observed in sequential treatment of melanoma with targeted therapy and immunotherapy. In this research, the authors investigated whether potential transcriptional mechanisms and pathways associated with PD-L1 protein expression could underlie targeted therapy drug resistance in melanoma. The authors found a PD-L1 expression transcriptional pattern underlies resistance to targeted therapy in a subgroup of melanomas. These melanomas were markedly dedifferentiated, as compared to melanomas that were not drug resistant. Understanding changes in transcription and molecular pathways that lead to drug resistance could allow researchers to develop interventions to prevent drug resistance from occurring in melanoma, which could also be relevant to other cancer types.

Abstract

Melanoma is the most aggressive type of skin cancer, with increasing incidence worldwide. Advances in targeted therapy and immunotherapy have improved the survival of melanoma patients experiencing recurrent disease, but unfortunately treatment resistance frequently reduces patient survival. Resistance to targeted therapy is associated with transcriptomic changes and has also been shown to be accompanied by increased expression of programmed death ligand 1 (PD-L1), a potent inhibitor of immune response. Intrinsic upregulation of PD-L1 is associated with genome-wide DNA hypomethylation and widespread alterations in gene expression in melanoma cell lines. However, an in-depth analysis of the transcriptomic landscape of melanoma cells with intrinsically upregulated PD-L1 expression is lacking. To determine the transcriptomic landscape of intrinsically upregulated PD-L1 expression in melanoma, we investigated transcriptomes in melanomas with constitutive versus inducible PD-L1 expression (referred to as PD-L1_CON and PD-L1_IND). RNA-Seq analysis was performed on seven PD-L1_CON melanoma cell lines and ten melanoma cell lines with low inducible PD-L1_IND expression. We observed that PD-L1_CON melanoma cells had a reprogrammed transcriptome with a characteristic pattern of dedifferentiated gene expression, together with active interferon (IFN) and tumour necrosis factor (TNF) signalling pathways. Furthermore, we identified key transcription factors that were also differentially expressed in PD-L1_CON versus PD-L1_IND melanoma cell lines. Overall, our studies describe transcriptomic reprogramming of melanomas with PD-L1_CON expression.

Advancing clinical genomics and precision medicine with GVViZ: FAIR bioinformatics platform for variable gene-disease annotation, visualization, and expression analysis

Background

Genetic disposition is considered critical for identifying subjects at high risk for disease development. Investigating disease-causing and high and low expressed genes can support finding the root causes of uncertainties in patient care. However, independent and timely high-throughput next-generation sequencing data analysis is still a challenge for non-computational biologists and geneticists.

Results

In this manuscript, we present a findable, accessible, interactive, and reusable (FAIR) bioinformatics platform, i.e., GVViZ (visualizing genes with disease-causing variants). GVViZ is a user-friendly, cross-platform, and database application for RNA-seq-driven variable and complex gene-disease data annotation and expression analysis with a dynamic heat map visualization. GVViZ has the potential to find patterns across millions of features and extract actionable information, which can support the early detection of complex disorders and the development of new therapies for personalized patient care. The execution of GVViZ is based on a set of simple instructions that users without a computational background can follow to design and perform customized data analysis. It can assimilate patients’ transcriptomics data with the public, proprietary, and our in-house developed gene-disease databases to query, easily explore, and access information on gene annotation and classified disease phenotypes with greater visibility and customization. To test its performance and understand the clinical and scientific impact of GVViZ, we present GVViZ analysis for different chronic diseases and conditions, including Alzheimer’s disease, arthritis, asthma, diabetes mellitus, heart failure, hypertension, obesity, osteoporosis, and multiple cancer disorders. The results are visualized using GVViZ and can be exported as image (PNF/TIFF) and text (CSV) files that include gene names, Ensembl (ENSG) IDs, quantified abundances, expressed transcript lengths, and annotated oncology and non-oncology diseases.

Conclusions

We emphasize that automated and interactive visualization should be an indispensable component of modern RNA-seq analysis, which is currently not the case. However, experts in clinics and researchers in life sciences can use GVViZ to visualize and interpret the transcriptomics data, making it a powerful tool to study the dynamics of gene expression and regulation. Furthermore, with successful deployment in clinical settings, GVViZ has the potential to enable high-throughput correlations between patient diagnoses based on clinical and transcriptomics data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40246-021-00336-1.

Prognostic and immunological roles of Fc fragment of IgG binding protein in colorectal cancer

Valuable diagnostic and prognostic biomarkers are urgently needed for colorectal cancer (CRC), which is one of the leading causes of mortality worldwide. Previous studies have reported altered expression of a mucin-like protein Fc fragment of IgG binding protein (FCGBP) in various types of cancer, but its potential diagnostic, prognostic and immunological roles in CRC remain to be determined. Therefore, the aim of current study was to investigate the potential roles of FCGBP in CRC. The present study investigated FCGBP mutations and changes in its expression levels using a combination of microarray and public dataset analyses, as well as immunohistochemistry. The results demonstrated a 10.5% mutation frequency in the FCGBP coding sequence in CRC tissues, and identified decreased FCGBP mRNA or protein expression levels in colorectal adenoma and CRC (compared with those in normal colorectal tissues from healthy control subjects), including pathologically advanced CRC (stage III+IV vs. I+II). Survival analysis using the GEPIA and Kaplan-Meier Plotter databases revealed that low FCGBP expression levels were associated with short overall, disease-free, relapse-free and event-free survival times in patients with CRC. Notably, analysis using the online Tumor IMmune Estimation Resource database revealed a positive correlation between FCGBP expression levels and the extent of infiltrating immune cells, such as B cells and dendritic cells. Consistently, the expression levels of most markers (51/57) for various types of immune cells were significantly correlated with FCGBP expression levels in CRC tissues. These findings suggested that FCGBP may serve as a diagnostic and prognostic biomarker, and that FCGBP may be associated with immune infiltration in CRC.

Processing and Analysis of RNA-seq Data from Public Resources

Advances in next generation sequencing (NGS) technologies resulted in a broad array of large-scale gene expression studies and an unprecedented volume of whole messenger RNA (mRNA) sequencing data, or the transcriptome (also known as RNA sequencing, or RNA-seq). These include the Genotype Tissue Expression project (GTEx) and The Cancer Genome Atlas (TCGA), among others. Here we cover some of the commonly used datasets, provide an overview on how to begin the analysis pipeline, and how to explore and interpret the data provided by these publicly available resources.

RNA sequencing: new technologies and applications in cancer research

Over the past few decades, RNA sequencing has significantly progressed, becoming a paramount approach for transcriptome profiling. The revolution from bulk RNA sequencing to single-molecular, single-cell and spatial transcriptome approaches has enabled increasingly accurate, individual cell resolution incorporated with spatial information. Cancer, a major malignant and heterogeneous lethal disease, remains an enormous challenge in medical research and clinical treatment. As a vital tool, RNA sequencing has been utilized in many aspects of cancer research and therapy, including biomarker discovery and characterization of cancer heterogeneity and evolution, drug resistance, cancer immune microenvironment and immunotherapy, cancer neoantigens and so on. In this review, the latest studies on RNA sequencing technology and their applications in cancer are summarized, and future challenges and opportunities for RNA sequencing technology in cancer applications are discussed.

Advancing brain barriers RNA sequencing: guidelines from experimental design to publication

BACKGROUND

RNA sequencing (RNA-Seq) in its varied forms has become an indispensable tool for analyzing differential gene expression and thus characterization of specific tissues. Aiming to understand the brain barriers genetic signature, RNA seq has also been introduced in brain barriers research. This has led to availability of both, bulk and single-cell RNA-Seq datasets over the last few years. If appropriately performed, the RNA-Seq studies provide powerful datasets that allow for significant deepening of knowledge on the molecular mechanisms that establish the brain barriers. However, RNA-Seq studies comprise complex workflows that require to consider many options and variables before, during and after the proper sequencing process.

MAIN BODY

In the current manuscript, we build on the interdisciplinary experience of the European PhD Training Network BtRAIN ( https://www.btrain-2020.eu/ ) where bioinformaticians and brain barriers researchers collaborated to analyze and establish RNA-Seq datasets on vertebrate brain barriers. The obstacles BtRAIN has identified in this process have been integrated into the present manuscript. It provides guidelines along the entire workflow of brain barriers RNA-Seq studies starting from the overall experimental design to interpretation of results. Focusing on the vertebrate endothelial blood-brain barrier (BBB) and epithelial blood-cerebrospinal-fluid barrier (BCSFB) of the choroid plexus, we provide a step-by-step description of the workflow, highlighting the decisions to be made at each step of the workflow and explaining the strengths and weaknesses of individual choices made. Finally, we propose recommendations for accurate data interpretation and on the information to be included into a publication to ensure appropriate accessibility of the data and reproducibility of the observations by the scientific community.

CONCLUSION

Next generation transcriptomic profiling of the brain barriers provides a novel resource for understanding the development, function and pathology of these barrier cells, which is essential for understanding CNS homeostasis and disease. Continuous advancement and sophistication of RNA-Seq will require interdisciplinary approaches between brain barrier researchers and bioinformaticians as successfully performed in BtRAIN. The present guidelines are built on the BtRAIN interdisciplinary experience and aim to facilitate collaboration of brain barriers researchers with bioinformaticians to advance RNA-Seq study design in the brain barriers community.

Group A Streptococcus Transcriptome Analysis

Global gene expression analyses in bacteria have undergone a dramatic transformation. Prior to the development of high-throughput sequencing technologies, real-time PCR or microarray studies were the mainstay of assessing differences in gene expression in bacteria. Real-time PCR remains a critical tool for targeted gene expression analyses. However, microarray studies have given way to the plethora of advantages in RNA sequencing (RNA-seq) for the determination of global gene expression (i.e., transcriptome). Increased accessibility to high-throughput sequencing and user-friendly bioinformatics data analysis software have made RNA-seq technology use more widespread. Here, we provide comprehensive methods to perform RNA sequencing of Streptococcus pyogenes strains grown in vitro in standard laboratory media, including cell growth, RNA extraction, ribosomal RNA depletion, and library construction. Considerations for library sequencing and data analysis are also provided.

A statistical simulator scDesign for rational scRNA-seq experimental design

MOTIVATION

Single-cell RNA sequencing (scRNA-seq) has revolutionized biological sciences by revealing genome-wide gene expression levels within individual cells. However, a critical challenge faced by researchers is how to optimize the choices of sequencing platforms, sequencing depths and cell numbers in designing scRNA-seq experiments, so as to balance the exploration of the depth and breadth of transcriptome information.

RESULTS

Here we present a flexible and robust simulator, scDesign, the first statistical framework for researchers to quantitatively assess practical scRNA-seq experimental design in the context of differential gene expression analysis. In addition to experimental design, scDesign also assists computational method development by generating high-quality synthetic scRNA-seq datasets under customized experimental settings. In an evaluation based on 17 cell types and 6 different protocols, scDesign outperformed four state-of-the-art scRNA-seq simulation methods and led to rational experimental design. In addition, scDesign demonstrates reproducibility across biological replicates and independent studies. We also discuss the performance of multiple differential expression and dimension reduction methods based on the protocol-dependent scRNA-seq data generated by scDesign. scDesign is expected to be an effective bioinformatic tool that assists rational scRNA-seq experimental design and comparison of scRNA-seq computational methods based on specific research goals.

AVAILABILITY AND IMPLEMENTATION

We have implemented our method in the R package scDesign, which is freely available at https://github.com/Vivianstats/scDesign.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Going the Distance: Optimizing RNA-Seq Strategies for Transcriptomic Analysis of Complex Viral Genomes

Transcriptome profiling has become routine in studies of many biological processes. However, the favored approaches such as short-read Illumina RNA sequencing are giving way to long-read sequencing platforms better suited to interrogating the complex transcriptomes typical of many RNA and DNA viruses. Here, we provide a guide-tailored to molecular virologists-to the ins and outs of viral transcriptome sequencing and discuss the strengths and weaknesses of the major RNA sequencing technologies as tools to analyze the abundance and diversity of the viral transcripts made during infection.