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Methods in molecular exercise physiology
Published in Adam P. Sharples, James P. Morton, Henning Wackerhage, Molecular Exercise Physiology, 2022
Adam P. Sharples, Daniel C. Turner, Stephen Roth, Robert A. Seaborne, Brendan Egan, Mark Viggars, Jonathan C. Jarvis, Daniel J. Owens, Jatin G. Burniston, Piotr P. Gorski, Claire E. Stewart
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).
Genetic Basis of Neuromuscular Disorders
Published in Maher Kurdi, Neuromuscular Pathology Made Easy, 2021
When designing RNA-Seq test, the selection of sequencing platform is important and based on clinical goals. Currently, several NGS platforms are commercially available (Table 8.1). The NGS platforms can often be classified as either ensemble-based or single-molecule-based. Currently, the Illumina HiSeq platform is the most commonly applied NGS (ensemble-based) for RNA-Seq and has set the standard for NGS sequencing. Single-molecule-based platforms enable single-molecule real-time (SMRT) sequencing but tend to have a high rate of errors in misalignment and loss of reads.
RNA-seq Analysis
Published in Altuna Akalin, 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.
Overview of gene expression techniques with an emphasis on vitamin D related studies
Published in Current Medical Research and Opinion, 2023
Jeffrey Justin Margret, Sushil K. Jain
Total RNA-Seq yields the most comprehensive transcriptome analysis by sequencing all RNA molecules and provides both qualitative and quantitative information on coding (mRNA) and noncoding RNAs (lncRNA and miRNA)26. It has been shown to be a powerful alternative to microarrays for whole-genome profiling. RNA-Seq has also been used to study regulatory regions, the splicing pattern expression levels of transcripts, and to identify exons and introns and their boundaries27. It has been used in patients with mitochondrial disorders to reveal their genetic causes28. RNA-Seq increases the diagnostic rate by 7.5–36% depending on the type of disease and tissue source24. Although RNA-Seq can detect unknown RNAs with more accurate and sensitive expression profiling, it still has certain limitations; the biases that occur during cDNA library construction affect transcript quantification. Another major limitation is that the expression level changes significantly between the tissues. Even though RNA-Seq has become more affordable, it is still costly for many laboratories29.
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.
Responses to iron oxide and zinc oxide nanoparticles in echinoderm embryos and microalgae: uptake, growth, morphology, and transcriptomic analysis
Published in Nanotoxicology, 2020
Anne-Marie Genevière, Evelyne Derelle, Marie-Line Escande, Nigel Grimsley, Christophe Klopp, Christine Ménager, Aude Michel, Hervé Moreau
In the Supplementary information S1, detailed protocols are reported on RNA-Seq analysis, mapping of sequenced reads and differential expression analysis. Briefly, the read quality of the RNA-Seq libraries was evaluated, a de novo transcriptome was assembled and annotated by BLAST comparison both with the genome sequences of closely related species, Strongylocentrotus purpuratus and Ciona intestinalis or with RefSeq (NCBI). Reads were realigned back to contigs and the contig expression counts were generated. Analysis of differentially expressed (DE) genes and data visualizations were performed in the R statistical environment (https://www.r-project.org/). DE genes were identified using R package edgeR (Robinson et al. 2010) with normalization for RNA composition and pairwise comparisons using the general linearized model likelihood ratio test. Contigs were accepted as significantly DE with a threshold of 1% as the false discovery rate (FDR) and when logCPM > 0 and log2-fold-change (|LFC|) > 1. DE contigs were identified as enriched in Gene Ontology (GO) terms and metabolic pathways by searching against the GO and the KEGG databases (Ashburner et al. 2000; Kanehisa et al. 2006, 2008). For further comparisons, Venn diagrams were made with jvenn (http://jvenn.toulouse.inra.fr/app/example.html) (Bardou et al. 2014) to obtain the number of significantly expressed genes, which were shared among groups or unique in each experimental condition.