Methods in molecular exercise physiology
Adam P. Sharples, James P. Morton, Henning Wackerhage in Molecular Exercise Physiology, 2022
RNA-sequencing, typically referred to as ‘RNA-seq’, is a thorough method for analysing all RNAs simultaneously. Unlike microarrays, RNA-seq does not require prior knowledge of the RNA sequence that is explored and, therefore, has the capacity to detect single nucleotide variants, alternate splicing, post-transcriptional modifications and silencing RNAs (also called micro-RNAs/miRNA), non-coding RNA (ncRNA), exon-intron boundaries and pre-mRNA via NGS technology. The present section will focus on the use of RNA-seq for quantifying gene expression, as this is the analysis that is becoming more frequently used in molecular exercise physiology. To undertake RNA-seq, sequencing ‘libraries’ are first created using the isolated RNA. As with RT-qPCR and microarrays, RNA is first isolated, and reverse transcribed to cDNA. The cDNA is then fragmented into small consistent sizes and sequencing ‘adaptors’ are added or ‘ligated’ to the ends of the cDNA fragments. These adaptors contain constant sequences that enable the sequencers to recognise where to start sequencing. NGS is then performed similar to that explained in the above sections. Prior to these crucial steps, the total RNA obtained from the muscle biopsy may be first treated to remove ribosomal RNA that makes up the majority (~90%) of extracted RNA. Therefore, if left untreated, sequencing data would be representative of predominantly rRNA rather than other RNAs of interest (e.g. pre-mRNA, mRNA, miRNA and ncRNA).
RNA-seq Analysis
Altuna Akalin in Computational Genomics with R, 2020
RNA sequencing (RNA-seq) has proven to be a revolutionary tool since the time it was introduced. The throughput, accuracy, and resolution of data produced with RNA-seq has been instrumental in the study of transcriptomics in the last decade (Wang et al., 2009). There is a variety of applications of transcriptome sequencing and each application may consist of different chains of tools each with many alternatives (Conesa et al., 2016). In this chapter, we are going to demonstrate a common workflow for doing differential expression analysis with downstream applications such as GO term and gene set enrichment analysis. We assume that the sequencing data was generated using one of the NGS sequencing platforms. Where applicable, we will try to provide alternatives to the reader in terms of both the tools to carry out a demonstrated analysis and also the other applications of the same sequencing data depending on the different biological questions.
Precision medicine in stroke and other related neurological diseases
Debmalya Barh in Precision Medicine in Cancers and Non-Communicable Diseases, 2018
RNA sequencing (RNA-seq) provides the information regarding transcript structure, including single base-pair resolution of transcript boundaries and boundaries between exons. The differential transcription is examined using RNA-seq in Alzheimer's disease in brain tissue, which provides the information about the protein coding genes, non-coding RNAs, and splicing. Networks produced may contribute to underlying neurological disease mechanisms. Several studies using animal models have observed the participation of the noncoding in the progression of Alzheimer's disease (Tan et al., 2014a). Next-generation sequencing can explain not only the complete molecular signatures of cells by transcriptome analyses but also the cascade of events that induces or maintains such signatures by epigenetic analyses.
Transcriptome sequencing of Salvia miltiorrhiza after infection by its endophytic fungi and identification of genes related to tanshinone biosynthesis
Published in Pharmaceutical Biology, 2019
Yan Jiang, Lei Wang, Shaorong Lu, Yizhe Xue, Xiying Wei, Juan Lu, Yanyan Zhang
RNA sequencing (RNA-Seq) is the high-throughput sequencing of mRNA in a species. Its resolution has the accuracy of a single nucleotide, it can dynamically reflect gene transcription levels and it provides specific sequence-structure information of transcripts in samples (Hansen et al. 2011). Currently, RNA-Seq is being extensively applied in all fields, including basic biological research, medical research and drug development (Kawahara et al. 2012; Foth et al. 2014; Zhang et al. 2014). This study performed RNA-Seq on sterile plantlets of S. miltiorrhiza and endophytic fungi to examine the differential gene expression after infection of tissue-cultured plantlets with endophytic fungi, to understand the underlying molecular mechanism of interaction, then analyzed to provide new ideas and methods for studying the regulation of secondary metabolism in medicinal plants.
Aberrant dysregulated circular RNAs in the peripheral blood mononuclear cells of patients with rheumatoid arthritis revealed by RNA sequencing: novel diagnostic markers for RA
Published in Scandinavian Journal of Clinical and Laboratory Investigation, 2019
Xuan Yang, Jingyi Li, Yuzhang Wu, Bing Ni, Bei Zhang
Gene expression profiling has been widely applied in a variety of disease studies to identify biomarkers for clinical diagnosis, prognosis and treatment [26–28]. Microarray and RNA-seq are the major two methods employed for genome-wide transcriptome profiling. Over the past two decades, microarrays have been the primary biomarker platform ubiquitously applied in biomedical and clinical research as the major biomarker identification tool. Nonetheless, microarrays have several limitations. For example, background hybridization limits the accuracy of expression measurements, particularly for transcripts present at a low abundance [19]. Furthermore, the characteristics of microarray sequencing determine that this method will lead to the omission of previously unidentified RNAs [29]. RNA-Seq is the direct sequencing of transcripts by high-throughput sequencing technologies. Unlike microarrays, RNA-Seq does not rely upon pre-determined probes designed against known target sequences, allowing its use in discovering novel gene expression at previously uncharacterized loci. Thus, RNA-seq provides a powerful tool to decipher global gene expression patterns far beyond the limitations of microarrays [30,31]. In addition, RNA-Seq also demonstrated a broader dynamic range than microarrays, which allowed for the detection of more differentially expressed genes with higher fold-change values [32].
Applications of Transcriptomics in the Research of Antibody-Mediated Rejection in Kidney Transplantation: Progress and Perspectives
Published in Organogenesis, 2022
RNA-Seq has clear advantages over the existing approaches and revolutionized the manner of transcriptome analysis.47 In a typical RNA-seq experiment, a population of RNA is extracted from samples, converted into cDNA, made into an adaptor-ligated sequencing library, and then sequenced to a depth of 10–30 million reads per sample.48 Exploiting the advances in computational methods, the resulting sequencing reads are either aligned to a reference genome or assembled de novo without the genomic sequence to produce a genome-scale transcription map for analyzing DGE.47,48 RNA-seq has been increasingly utilized in the investigation of different types of renal allograft rejection, including ABMR, albeit currently available studies are relatively limited compared to microarray method. One study performing RNA-seq on biopsy-paired peripheral blood samples from patient cohorts with different types of rejection identified 102 genes with enrichment in the regulation of endoplasmic reticulum stress, adaptive immunity, and Ig class-switching to be associated with ABMR, including the SIGLEC17P pseudogene and the related coding genes,49 of which the expression is almost exclusively in NK cells.50 Dooley et al.51 performed RNA-seq with human urine samples matched to TCMR, ABMR, as well as non-rejection biopsies and identified three novel mRNAs (ITM2A, SLAMF6, and IKZF3) of which the expressions were significantly higher in urine matched to TCMR or ABMR than in the non-rejection biopsies.
Related Knowledge Centers
- Alternative Splicing
- DNA Sequencing
- Fusion Gene
- Gene Expression
- Microrna
- Rna
- Transcriptome
- Transfer Rna
- Post-Transcriptional Modification
- Single-Nucleotide Polymorphism