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).
Biological individualisation of radiotherapy
Michael C. Joiner, Albert J. van der Kogel in Basic Clinical Radiobiology, 2018
Non-coding RNAs are not translated into protein. Examples are microRNAs (miRNA), snoRNA and siRNA. miRNA are small non-coding RNAs of 19–24 nucleotides that generally downregulate gene expression by inhibiting protein translation. It is estimated that miRNAs affect expression of up to 30% of the genes in the mammalian genome. miRNAs play a role in development, differentiation, apoptosis and the initiation and progression of human cancers. miRNA profiling is showing potential to provide information on radiobiologically relevant features (e.g. hypoxia and radiosensitivity) and prognosis (27,32). Current research is moving away from microarray approaches for gene expression analyses to using next-generation sequencing technologies. RNA sequencing allows for quantification of absolute levels of normal and mutant transcripts and is used for gene expression profiling of different mRNA species, including small non-coding RNAs.
Introduction to R for Genomic Data Analysis
Altuna Akalin in Computational Genomics with R, 2020
This step refers to processing the data into a format that is suitable for exploratory analysis and modeling. Oftentimes, the data will not come in a ready-to-analyze format. You may need to convert it to other formats by transforming data points (such as log transforming, normalizing, etc.), or subset the data set with some arbitrary or pre-defined condition. In terms of genomics, processing includes multiple steps. Following the sequencing analysis example above, processing will include aligning reads to the genome and quantification over genes or regions of interest. This is simply counting how many reads are covering your regions of interest. This quantity can give you ideas about how much a gene is expressed if your experimental protocol was RNA sequencing. This can be followed by some normalization to aid the next step.
Fusobacterium nucleatum promotes colorectal cancer metastasis through miR-1322/CCL20 axis and M2 polarization
Published in Gut Microbes, 2021
Chaochao Xu, Lina Fan, Yifeng Lin, Weiyi Shen, Yadong Qi, Ying Zhang, Zhehang Chen, Lan Wang, Yanqin Long, Tongyao Hou, Jianmin Si, Shujie Chen
RNA sequencing was performed as described previously.15 Briefly, after incubated with F. nucleatum (MOI = 100:1) or PBS for 24 h, total RNAs of LoVo cells were extracted and subjected to cDNA synthesis followed by adaptor ligation and enrichment. The RNA sequencing was paired-end sequenced at Guangzhou RiboBio Co., Ltd. (China). The whole samples expression levels were presented as RPKM (Reads Per Kilobase per Million). All differentially expressed genes were ploted with “pheatmap” and “ggplot2” R packages. For KEGG and GSEA enrichment analysis, a p value <.05 was used as the threshold to determine the significant enrichment of the gene sets with “clusterProfiler”R package. For immune cells infiltration estimations, the whole samples expression levels were presented TPKM (Transcripts Per Kilobase Million). The RNA sequencing dataset has been deposited in the Gene Expression Omnibus (GEO) accession: GSE173549.
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.
Kernel partial correlation: a novel approach to capturing conditional independence in graphical models for noisy data
Published in Journal of Applied Statistics, 2018
Jihwan Oh, Faye Zheng, R. W. Doerge, Hyonho Chun
However, most of the previously mentioned methods have limitations when they are applied to extremely noisy data. One important example is the single-cell RNA-sequencing (scRNA-seq) data which we focus on in this paper. As a recent application of next-generation sequencing experiments [47,51,56], the scRNA-seq enables researchers to sequence individual cells one-at-a-time to investigate cell-to-cell variations. In scRNA-seq, it is necessary to undergo repetitive copying of the RNA molecules [44] because the amount of the RNA molecules in a single cell is tiny (1–50 pg) and much lower than the amount required by the sequencing technology (5–20 µg) [36]. This amplification process drops out several molecules, resulting in some genes having moderate or high expression levels in one cell but not being detected at all in another cell [28]. For example, Figure 1 illustrates dropout events via log-scaled gene expression levels between pairs of genes in a scRNA-sequencing experiment [26].
Related Knowledge Centers
- Alternative Splicing
- DNA Sequencing
- Fusion Gene
- Gene Expression
- Microrna
- Rna
- Transcriptome
- Transfer Rna
- Post-Transcriptional Modification
- Single-Nucleotide Polymorphism