RNA-Seq

RNAseq is the most recent transcriptomic arroyo where the total complement of RNAs from a given sample is isolated and sequenced using high-throughput technologies (often called Next-Generation Sequencing).

From: Biomarkers in Toxicology , 2014

Advances in Genetics

Devi Singh , ... Shashi Kumar , in Advances in Genetics, 2012

1 RNA-Seq

RNA-Seq is the sequencing approach that utilizes next-generation sequencing applied science to written report the entire transcriptome. RNA-Seq is a loftier-throughput alternative to the traditional RNA/cDNA cloning and sequencing strategies. Furthermore, RNA-Seq also provides data on the expression levels of the transcripts and the alternating splice variants. Sudini et al. (2011) explored soil bacterial communities in different peanut cropping sequences using molecular approaches such as Ribosomal Intergenic Spacer Assay (RISA) combined with 16S rRNA cloning and sequencing. Genomic sequencing or RNA-Seq can replace these techniques and provide even more information on the bacterial communities. RNA-Seq has been applied to sequence the transcriptomes of several crop plants including, soybean, rice, and grapes (Lu et al., 2010; Severin et al., 2010; Zenoni et al., 2010; Zhang et al., 2010). However, exome sequencing will be of limited importance for such analysis. Host–pathogen interactions and drought and salinity stress responses accept also been successfully characterized by utilizing RNA-Seq (Deyholos, 2010; DiGuistini et al., 2011).

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Genomic and Proteomic Mechanisms and Models in Toxicity and Safety Evaluation of Nutraceuticals

Beena M. Kadakkuzha , ... Youjun Chen , in Nutraceuticals, 2016

RNA-Seq

RNA-Seq is a recently adult transcriptome profiling technology that utilizes side by side-generation sequencing platforms ( Metzker, 2010; Mardis, 2008). RNA-Seq transcripts are reverse-transcribed into cDNA, and adapters are ligated to each end of the cDNA. Sequencing can be done either unidirectional (single-finish sequencing) or bidirectional (paired-end sequencing) and then aligned to a reference genome database or assembled to obtain de novo transcripts, proving a genome-wide expression contour (Wang et al., 2009). RNA-Seq offers many advantages over microarray applied science. Different microarray technology, which depends on already known genes, RNA-Seq is not dependent on existing genome data and can screen novel transcript and analyze transcript structure, including single base-pair resolution and exonic boundaries, which is very valuable while investigating SNPs, thus making information technology useful for genotyping and linkage assay (Wang et al., 2009). The advantages of RNA-Seq and its awarding in studying nervous system and the challenges associated with the applied science are summarized in a previous publication (Kadakkuzha and Puthanveettil, 2013).

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mRNA three' Finish Processing and Metabolism

Zhiqiang Cai , ... Gideon Dreyfuss , in Methods in Enzymology, 2021

ii.vi Processing RNA-seq reads

RNA-seq technologies and data processing tools continue to developed at rapid pace. For the studies describe here RNA-seq was performed on Illumina HiSeq 2500, and we have used the post-obit information analysis pipeline. Paired-end RNA-seq reads were trimmed of whatever adaptor sequences with the FASTX-Toolkit (version 0.0.14). The ii paired reads were merged into one single fragment using PEAR (version 0.9.eight), and and so fragments larger than 150  nt were filtered out. The remaining reads were aligned to the GRCh38/hg38 reference genome using STAR (version 2.5.3a) with the following parameters: twopassMode Bones—alignSJoverhangMin v—alignSJDBoverhangMin 5—outSAMmapqUnique 255—outFilterMultimapNmax i—outSJfilterReads Unique. Reads per exon were grouped, from which RPKM (reads per kilobase per one thousand thousand mapped reads) values were calculated using SAMtools (version 0.1.xix).

In society to straight compare samples that take a different number of mapped reads, the read coverage for each sample was normalized to the total number of mapped reads per meg (RPM). This normalized value was too used to calibration the samples for visualization on the UCSC Genome Browser. External CLIP-seq and Pol II Bit-seq datasets were downloaded in raw format from the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/) and then candy and aligned as described higher up. All RNA-seq datasets mentioned in this commodity were deposited at the same publicly bachelor database.

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RNA-Sequencing and Methylome Analysis

Shamika Ketkar , Shashikant Kulkarni , in Clinical Genomics, 2015

Conclusions

RNA-seq and methylome sequencing are techniques that provide the opportunity to analyze the transcriptome and the methylome, their complexities, and their relevance to human pathobiology. The last few years have seen an explosion in the awarding of these technologies in genomic studies of both somatic and inherited disorders. However, many significant technical and bioinformatic hurdles remain before these tools are in routine clinical use. For example, a complete catalog of transcripts in normal and disease states that tin be referred to for interpreting the pathogenic significance of RNA-seq data sets is nevertheless in early development, as are catalogs of methylation changes in normal and disease states. Even so, these technologies are poised to play a pregnant role in personalization of genomic medicine in the near future.

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Genomics-Guided Immunotherapy of Human Epithelial Ovarian Cancer

Sahar Al Seesi , ... Angela Kueck , in Translational Cardiometabolic Genomic Medicine, 2016

3.1 RNA-Seq

RNA-Seq uses adjacent-generation sequencing technologies, such as SOLiD, 454, Illumina, or ION Torrent [36–39]. Effigy 1 depicts the main steps in an RNA-Seq experiment, ending with the beginning stride of assay, which is typically annotating or mapping the data to a reference. The mRNA extracted from a sample is converted to cDNA using reverse transcription and sheared into fragments. Fragments within lengths of a certain range are selected and ligated with sequencing adapters. This is usually followed by an distension footstep subsequently which one or both ends of the cDNA fragments are sequenced to produce either unmarried or paired-end reads. cDNA synthesis and adapter ligation tin exist washed in a strand-specific fashion, in which case the strand of each read is known; this is commonly referred to as directional sequencing. In the more mutual non-directional RNA-Seq protocols, strand specificity is not maintained. The specifics of the sequencing protocols vary from one technology to the other. In particular, the length of produced reads varies depending on the technology, with newer high-throughput technologies typically producing longer reads.

Figure 1. A schematic representation of the RNA-Seq protocol, including the first steps of the analysis. RNA transcripts are converted into double-stranded cDNA, which are then fragmented and their ends sequenced. Analysis starts by mapping reads to a reference genome.

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Tools for the assessment of epigenetic regulation

Lauren A. Eaves , ... Rebecca C. Fry , in Environmental Epigenetics in Toxicology and Public Wellness, 2020

The sequencing approach

miRNA-seq is very like to RNA-seq discussed in " Loftier-throughput sequencing" section. Some examples of specific platforms are the Roche 454 sequencing system, Solexa from Illumina, and SOLiD from ABI [104]. The primary difference between RNA-seq and miRNA-seq is in library preparation for which miRNA present challenges. To brainstorm, the small size of miRNA introduces possible ligation bias. It is harder to perform reverse transcription and PCR amplification on the shorter nucleotide miRNA [105]. To remedy this, protocols include either RNA ligation, which is the process of adding fragments to the RNA sequence, or polyadenylation, which is the process of specially adding a poly(A) tail to the sequence [105]. Both of these processes elongate the miRNA, making information technology a scrap longer and easier to perform reverse transcription or PCR.

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Current and Emerging Technologies for the Diagnosis of Microbial Infections

William Eastward. Yang , ... Ephraim Fifty. Tsalik , in Methods in Microbiology, 2015

two.one.1.2 RNA-seq

RNA-seq, besides known as whole transcriptome sequencing, is the sequencing of a sample's mRNA content. Notably, it is a method by which a bespeak-in-time snapshot of the transcriptome can be obtained. RNA-seq involves conversion of a sample of RNA to a cDNA library, which is and then sequenced and mapped against a reference genome. In add-on to the ability to measure the level of factor expression, information technology provides further information on alternative splicing and non-coding RNA (such as microRNA) ( Chaussabel et al., 2010). The ability to sequence the transcriptome overcomes some limitations of whole-genome microarrays. Specifically, the measures are quantitative and the spectrum of detectable targets is not express past the gene probes present on a microarray.

Despite RNA-seq's major advantages, the technique is subject to certain biases inherent to the reverse transcription and PCR amplification processes used. In the former, annealing of primers to RNA is not truly random and results in reduced data from the 5′ and 3′ ends of the strands (Hansen, Brenner, & Dudoit, 2010; Roberts, Trapnell, Donaghey, Rinn, & Pachter, 2011), making information technology more than hard to place the starts and ends of transcripts. Meanwhile, PCR tends to amplify long transcripts to a greater caste than brusque transcripts, resulting in transcript-length bias (Oshlack & Wakefield, 2009).

The cost of equipment is a major impediment to more widespread use. In addition to the molecular sequencing equipment, RNA-seq generates a large quantity of data to analyse; in 1 particular case, Hiseq2000 (Illumina) tin generate 200 meg 100-nt reads per lane per sequence run, which is approximately 50   GB (Chu & Corey, 2012). Both calculating power and storage are necessary to fairly analyse the sheer volume of information generated.

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Toxicogenomics – A Drug Development Perspective

Yuping Wang , ... Weida Tong , in Genomic Biomarkers for Pharmaceutical Development, 2014

6.3.1.2 NGS Technologies – Sequencing-based Approaches for Transcriptomics Written report

The arrival of deep sequencing applications for transcriptome analyses, RNA-Seq, may circumvent the above-mentioned disadvantages of microarray platforms. In dissimilarity to microarray, transcriptome sequencing studies have evolved from determining the sequence of individual cDNA clones to more than comprehensive attempts to construct cDNA sequencing libraries representing portions of the species transcriptome [69–72]. The utilise of sequencing technologies to written report the transcriptome is termed RNA-Seq [73,74]. RNA-Seq uses recently developed deep sequencing technologies. In full general, a population of RNA is converted to a library of cDNA fragments by use of adaptors attached to i or both ends. Each molecule, with or without amplification, is and so sequenced in a high-throughput manner to obtain short sequences from one or both ends. In principle, any loftier-throughput sequencing technology can be used for RNA-Seq. This methodology has tremendously reduced the sequencing cost and experimental complication, equally well as improved transcript coverage, rendering sequencing-based transcriptome analysis more readily bachelor and useful to individual laboratories. RNA-Seq technologies take demonstrated some distinct advantages over hybridization-based approaches such as microarrays that likely will enable them to dominate in the near future.

Currently, in that location are four major commercially bachelor NGS technologies: Roche/454, Illumina HiSeq 2000, Applied Biosystems SOLiD, and Helicos HeliScope. Illumina's NGS platforms have a potent presence. Their sequencing-past-synthesis approach [75–78] utilizes fluorescently labeled reversible-terminator nucleotides on clonally amplified DNA templates immobilized to an acrylamide coating on the surface of a glass flow cell. The Illumina Genome Analyzer and the more recent HiSeq 2000 accept been widely used for high-throughput massively parallel sequencing. In 2011, Illumina also released a lower throughput fast-turnaround musical instrument, the MiSeq, aimed at smaller laboratories and the clinical diagnostics marketplace.

Although RNA-Seq is unlikely to completely supplant hybridization-based techniques in the near time to come, information technology offers a number of improvements over these technologies, for case:

one.

unlike hybridization-based approaches, RNA-Seq does not depend on prior knowledge of the transcriptome, and is thus capable of new discovery and could reveal the precise boundaries of transcripts to single base precision [79];

2.

the technique can too yield information about exon junctions, allowing the study of complex transcription units [eighty];

iii.

RNA-Seq has inherently low background and high sensitivity, and the upper detection limits are not constrained, together allowing the study of the transcription across a much wider range than for microarrays [56,81].

A discussion of the considerable differences betwixt available RNA-Seq technologies is across the scope of this chapter. Nevertheless these technologies share many common features. First, the RNA sample is either mRNA enriched or ribosomal RNA depleted. The pick depends on the intent of the experiment. A cistron expression profiling experiment would enrich the mRNA and ignore the other RNA species, while an experiment focused on transcriptome characterization would deplete the ribosomal RNA leaving the mRNA, ncRNA, miRNA, and siRNA. Next, the RNA is fragmented and size selected. The size of RNA fragments required depends on the specific technology. 3rd, the fragments are contrary-transcribed into cDNA and are clonally amplified and tagged so that they tin be attached to beads. The bead-bound fragments are then placed in a fluidics chamber, placed in the sequencer, and sequenced. The chemistry of sequencing varies between the platforms. However, each chemical modify in the fluidics sleeping accommodation (pH in the case of Ion Torrent, fluorescence for the other technologies) corresponds to a specific base and the sequence is recorded. The technologies described above all rely on the amplification of fragments via polymerase chain reaction (PCR), which will introduce bias and change the relative proportions of the RNA species present. Other technologies, referred to as 'single-molecule sequencing' or '3rd-generation sequencing', avert this amplification pace and its attendant bias. However, these technologies have non still been widely adopted by the scientific community.

Taking all of these advantages into account, RNA-Seq represents a paradigm shift in transcriptomics studies, with concomitant benefits for toxicogenomics. This technology has already been extensively applied to biological enquiry, resulting in pregnant and remarkable insights into the molecular biology of cells [82–84]. The pharmaceutical industry has already embraced sequence-based technologies, and it is likely that these technologies will take their impact throughout the drug discovery process [85–87].

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High-Density Sequencing Applications in Microbial Molecular Genetics

Vânia Pobre , Cecília M. Arraiano , in Methods in Enzymology, 2018

Abstract

Differential RNA-Seq is a next-generation technology method to decide the significant transcriptomic differences between two and more samples. With this method information technology is possible to analyze the total RNA content of different samples making it the best global analysis method currently available to written report the roles of exoribonucleases in the cell. These enzymes are responsible for the RNA processing and degradation in the cells and therefore affect the total RNA pool in ways not yet fully understood. In Escherichia coli there are three principal degradative exoribonucleases RNase II, RNase R, and PNPase that degrade the RNA from the 3′ to the v′-end. These enzymes take several roles in the cell and fifty-fifty though they are degradative enzymes RNase II and PNPase tin can also protect some RNAs from degradation and PNPase can likewise human activity as an RNA polymerase under some conditions. The multiplicity of roles of these exoribonucleases leads to a very loftier number of transcripts that are affected by their absenteeism in the cell. With the differential RNA-Seq it is possible to obtain a much deeper understanding of how these enzymes work and regulate the bacterial gene expression. In this chapter nosotros have described a differential RNA-Seq data analysis protocol applied to the study of exoribonucleases. We besides included the protocol for experimental validation of the RNA-Seq information using qPCR and motility assays. Although the methods described in this affiliate were applied to the study of the exoribonucleases, they tin likewise exist used for other differential RNA-Seq studies.

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Multifactorial Inheritance and Complex Diseases

Allison Fialkowski , ... Hemant Chiliad. Tiwari , in Emery and Rimoin's Principles and Practice of Medical Genetics and Genomics (Seventh Edition), 2019

eleven.10.one Cistron Expression and RNA-Seq Information

RNA-Seq analysis has get a standard method for global cistron expression analysis. Although microarray cistron expression statistical methods are well established, there does not exist a gold standard pipeline for analyzing RNA-Seq data. Unlike the microarrays, RNA-Seq does not operate on predetermined selection of cDNA probes, thereby offering a perfect proxy not just for expressed transcript abundance for known genes, but also for the detection and quantification of (1) splice isoforms, (2) novel transcripts, and (iii) protein–RNA bounden sites. Once RNA abundance is quantified, comparison of cistron expression differences and changes between samples representing different treatment/biological atmospheric condition has become a well-established application of RNA-Seq. The major steps involved in RNA-Seq information assay are (1) experimental blueprint consideration, (2) QC, (3) read alignment, (4) quantification of cistron and transcript levels, (five) visualization6,(six) differential gene expression, (vii) alternative splicing, (8) functional analysis, and (nine) gene fusion detection. The experimental pattern involves randomization and option of the library type, sequencing depth, and number of replicates. The number of replicates and sequencing depth are crucial to detect meaning differences in a transcript or gene betwixt experimental groups. The statistical power to observe differential expression varies with desired consequence size, sequencing depth, and number of replicates. A number of ability calculation tools are available according to experimental design; for example, the Bioconductor package RNASeqPower could be used to calculate ability assuming effect size, sequencing depth, and number of replicates ([196], https://bioconductor.org/packages/release/bioc/html/RNASeqPower.html). Too, RNAseqPS ([197] with web interface at https://cqs.mc.vanderbilt.edu/shiny/RnaSeqSampleSize/) is a user-friendly tool to summate ability and sample size, and Scotty (http://scotty.genetics.utah.edu/) calculates the ability based on the number of biological replicates, sequencing depth, and cost per replicate and per million reads sequenced. Illumina software generates FASTQ files (sequence and quality score). The QC pipeline is crucial in obtaining the highest quality data for all subsequent analyses. QC metrics include sequence quality, GC content, presence of adapters, overrepresentation of grand-mers and duplicated reads, amount of ribosomal RNA remaining subsequently poly(A) selection, quantification of 3′ end bias, detection of viral RNAs through alignment of sequencing reads to a viral genome database, and per centum of reads that align to the genome and transcriptome. Percent of mapped reads is a global indicator of overall sequencing accurateness and the presence of contamination. Another important metric is the uniformity of read coverage on exons and the mapped strand. FASTQC (http://world wide web.bioinformatics.babraham.air conditioning.great britain/projects/fastqc/) is a commonly used tool to perform QC for Illumina RNA-Seq information. The side by side step involves alignment of reads to a reference genome or mapping to an annotated transcriptome. TopHat2 [198]/STAR2 [199]/Bowtie2 [200] are pop for mapping to gene and transcriptome. The transcript-specific notation tin can be washed using GenomicFeatures ([201], http://www.bioconductor.org/packages/2.12/bioc/html/GenomicFeatures.html). The adjacent step is to estimate gene and transcriptome expression quantification. Programs such equally HTSeq or featureCounts tin provide a table of aggregate raw counts of mapped reads [202]. However, the raw read counts are affected past factors such as transcript length, total number of reads, and sequencing biases. Since longer transcripts and deeper sequencing requite more reads, the initial solution was proposed to calculate the reads per kilobase per million mapped reads (RPKM) [203]. Subsequent measures were proposed, such as FPKM (fragments per kilobase of exon per meg fragments mapped) or TPM (transcripts per kilobase one thousand thousand) [204]. Another consequence is that HTSeq discards reads mapping to multiple locations, and so an alternate method, RSEM (RNA-Seq by expectation maximization), that assigns the reads to different locations tin can exist used to quantify the expression from the transcriptome [205]. In add-on, the RSEM algorithm returns the TPM values for downstream differential gene expression analysis. RPKM, FPKM, and TPM normalize away the most important factor for comparing samples for differential expression analysis. In that location are a number of normalization methods that have been proposed for adjusting length and total reads, such every bit TMM [206], DESeq [207], UpperQuartile [208], etc. A good review of comparisons of these approaches is given in Dillies et al. [209]. NOISeq is a useful R package that contains a variety of diagnostic plots to identify sources of biases in RNA-Seq information and and so applies appropriate normalization procedures in each example [210]. Since RNA-Seq data are read count data, they must be modeled with a Poisson, negative binomial, or null-inflated negative binomial distribution. 1 proposed method is a Bayesian hierarchical model that uses a Poisson distribution, conditional on baseline expression and posttreatment expression level fold change [211]. Another method uses a negative binomial model that assumes common dispersion across all genes and executes an exact test for differential expression [212,213]. The negative binomial model was later extended to generalized linear models, making the method applicative to general experiments [214]. Last, the Bioconductor R parcel edgeR [215,216] and DESeq2 [217] provide several tools for differential gene expression assay. TopHat2/Bowtie2 and STAR2 aligners also offer born options to quantify alternate splicing patterns and gene fusion detections. To identify molecular pathways that are differentially expressed between the biological conditions, the superlative differentially expressed genes are further investigated for their coexpression design in several biological, process, and metabolic pathways. Equally function of downstream functional analysis, tools like Ingenuity Pathway Analysis framework [218], WebGestalt [219], and DAVID [220–223] can be used to conduct pathway-level analysis.

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