whats the difference between preparing and rna sample for sequencing as opposed to dna

What is RNA-seq?

RNA-seq (RNA-sequencing) is a technique that can examine the quantity and sequences of RNA in a sample using next-generation sequencing (NGS). It analyzes the transcriptome, indicating which of the genes encoded in our Deoxyribonucleic acid are turned on or off and to what extent. Here, nosotros look at why RNA-seq is useful, how the technique works and the basic protocol that is commonly used today.¹

What are the applications of RNA-seq?
How does RNA-seq work?
RNA-seq vs microarrays: Why RNA-seq is considered superior
An RNA-seq protocol
- Experiment planning
- cDNA library preparation
- cDNA sequencing
- RNA-seq information assay
Challenges of RNA-seq

What are the applications of RNA-seq?

RNA-seq lets us investigate and detect the transcriptome, the total cellular content of RNAs including mRNA, rRNA and tRNA. Understanding the transcriptome is key if we are to connect the information in our genome with its functional protein expression. RNA-seq tin can tell us which genes are turned on in a cell, what their level of transcription is, and at what times they are activated or shut off.² This allows scientists to sympathise the biology of a cell more deeply and assess changes that may indicate disease. Some of the nearly pop techniques that use RNA-seq are transcriptional profiling, unmarried nucleotide polymorphism (SNP) identification,^three RNA editing and differential cistron expression analysis.^four

This can requite researchers vital information virtually the part of genes. For case, the transcriptome tin can highlight all the tissues in which a gene of unknown role is turned on, which might indicate what its role is. It likewise captures information about alternative splicing events (Effigy i), which produce different transcripts from ane unmarried gene sequence. These events would not be picked upwards by Dna sequencing. Information technology can besides identify post-transcriptional modifications that occur during mRNA processing such every bit polyadenylation and 5' capping.²

Figure 1:  RNA-seq information uses short reads of mRNA which is free of intronic not-coding Dna. These reads must so be aligned back to the reference genome.

How does RNA-seq work?

Early on RNA-seq techniques used Sanger sequencing technology, a technique that although innovative at the time was also low-throughput and plush. It is only recently, with the advent and proliferation of NGS technology, have we been able to fully take reward of RNA-seq's potential.⁵

An RNA-seq workflow has several steps, which can be broadly summarized every bit:

1. RNA extraction
2. Reverse transcription into cDNA
3. Adjusted ligation
4. Amplification
5. Sequencing

Once you have obtained your RNA sample for analysis, the first stride in the technique involves converting the population of RNA to be sequenced into complimentary DNA (cDNA) fragments (a cDNA library). This is done past contrary transcription and allows the RNA to exist put into an NGS workflow. The cDNA is then fragmented, and adapters are added to each end of the fragments. These adapters comprise functional elements which permit sequencing, for example, the amplification element (which facilitates clonal distension of the fragments) and the primary sequencing priming site. Following processes of amplification, size choice, clean-up and quality checking, the cDNA library is then analyzed by NGS, producing curt sequences that correspond to all or part of the fragment from which it was derived. The depth to which the library is sequenced varies depending on the purpose for which the output data will be used for. Sequencing may follow either single-end or paired-end sequencing methods. Single-read sequencing is a cheaper and faster technique (for reference, about 1% of the cost of Sanger sequencing) that sequences the cDNA fragments from only one terminate. Paired-end methods sequence from both ends and are therefore more than expensive^six,seven but offering advantages in post-sequencing data reconstruction.

A further choice must exist made between strand-specific and non-strand-specific protocols. The onetime method means the data nearly which DNA strand was transcribed is retained. The value of extra information obtained from strand-specific protocols brand them the favorable pick.

These reads, of which there volition be many millions by the end of the workflow, can then be aligned to a reference genome if available or assembledde novo to produce an RNA sequence map that spans the transcriptome.⁸

RNA-seq vs microarrays: Why RNA-seq is considered superior

RNA-seq is widely regarded as superior to other technologies, such as microarray hybridization. At that place are several reasons for RNA-seq's well-regarded status:

Not limited to genomic sequences –  unlike hybridization-based approaches, which may require species-specific probes, RNA-seq can detect transcripts from organisms with previously undetermined genomic sequences. This makes it fundamentally superior for the detection of novel transcripts, SNPs or other alterations.^9,ten

Low background point –  the cDNA sequences used in RNA-seq tin can be mapped to targeted regions on the genome, which makes it piece of cake to remove experimental noise. Furthermore, issues with cross-hybridization or sub-standard hybridization, which can plague microarray experiments, are not an issue in RNA-seq experiments.

More quantifiable - Microarray data is only ever displayed as values relative to other signals detected on the array, whilst RNA-seq data is quantifiable. RNA-seq likewise avoids the issues microarrays have in detecting very high or very low transcription levels.

Figure 2:  A workflow for RNA-seq

An RNA-seq protocol

Experiment planning

Grooming prior to starting your RNA-seq experiment is essential. Questions to respond before starting include:^xi

•           What method of RNA purification are you using?

•           What read depth will you need?

•           Which platform will you use?

•           Is there a reference genome available and which will you apply?

•           How are you lot assessing the quality of your RNA?

•           Practice you need to enrich your target RNA?

•           Will you barcode your RNA?

•           Have I got enough biological and technical replicates?

•           Single-end or paired-cease sequencing?

•           What read length will you use?

•           Do I want to retain strand-specific data?

cDNA library grooming

Afterward these points have been considered, you can outset preparing your cDNA library. This will require fragmentation of the cDNA, addition of the platform-specific "adapter sequences" and amplification of the cDNA, but the verbal process will be very specific to the platform used at this stage. For strand-specific protocols, the amplification of the cDNA involves a reverse transcriptase-mediated beginning strand synthesis followed by a Dna polymerase-mediated second strand synthesis.^11,12 Barcodes may also exist added that enable multiplexing, so numerous samples can be sequenced in a unmarried run. It tin be benign to quantify your library at the end of the library preparation phase to ensure the protocol has been successful and check the quality and concentration of your library to enable optimal sequencing functioning.

cDNA sequencing

Once the library is prepared, you can use your chosen sequencing platform to sequence your cDNA library to your desired depth and requirements. Once your transcript information has been produced, you can map the data to your reference genome or assemble itde novo if no reference is available. The alignment process can be complicated by the presence of splice variants and modifications, and the pick of reference genome used volition also vary how hard this phase is. Software packages such as STAR are useful at this phase, as are quality control tools like Picard or Qualimap.^thirteen De novo assembly will let for the discovery of novel transcripts in addition to those already known.

RNA-seq data assay

Later the alignment stage, y'all can focus on analyzing your data. Tools like Sailfish, RSEM and BitSeq¹³ will assistance you quantify your transcription levels, whilst tools like MISO, which quantifies alternatively spliced genes, are available for more than specialized analysis.¹⁴ There is a library of these tools out at that place, and reading reviews and roundups are your all-time fashion to observe the right tool for your research.

To sum upward, modern-mean solar day RNA-seq is well established every bit the superior option to microarrays and will likely remain the preferred option for the time existence.

Challenges of RNA-seq

Significant progress has been fabricated in the field of RNA-seq over the last decade or then. The associated costs take reduced significantly while throughput has increased, sequence fidelity is far superior to earlier iterations of the NGS technologies and the availability of data assay tools and pipelines has improved tremendously. However, there remain a number of challenges for scientists to bear in mind when considering RNA-seq experiments. These include:

Isolating sufficient, high-quality  RNA – while the sample quantity requirements for RNA-seq assay have reduced drastically, it is still important to ensure you are able to obtain sufficient RNA to fulfill all your analysis requirements, including repeats if necessary. It is also important to deport in mind that, while you may isolate total RNA, depending upon your experimental question, you are probable merely to be sequencing a fraction of this (typically messenger RNA (mRNA)), farther reducing your sample quantity. This must likewise be of high quality and purity equally poor samples are likely to atomic number 82 to poor results, or in some cases failure inside the library training protocol. The quality and concentration of RNA can be determined using UV-visible spectroscopy. Unlike DNA, RNA degrades rapidly so it important to treat samples with care at all stages of isolation and purification. Degradation may not be compatible, hindering the comparing of transcription levels between genes. Low-level transcripts may be lost from the sequenced population altogether.

The affect of sample pooling  – pooling samples prior to library preparation (without the use of barcoding) tin can reduce sequencing effort and costs or enable sequencing in cases where sample quantities are very limited. However, it is important to business relationship for this during data analysis, with one such pool considered to exist i biological replicate, non however many samples went in to making up the pool. Variations between the pooled samples can lead to misleading results and statistical issues so possible implications should be considered during the experimental design procedure.

Trading-off sequencing depth against sample number  – Information technology may seem appealing to get as many samples done in a single sequencing run equally possible to reduce costs and machine time. Nevertheless, this comes at a price. The more samples are multiplexed, the fewer reads will be obtained for each of those samples. With reducing read depth comes mounting doubt every bit to the reliability of the sequences obtained. Sequencing technologies are still far from perfect, and mistakes are made in reads. It is therefore important to observe the sweet spot between obtaining sufficient read depth to give confidence in the quality and fidelity of the sequencing data obtained and maximizing sequencing capacity to ensure sufficient biological replicates tin can be analyzed to give meaningful data.

References

one.         Wang Z, Gerstein M, & Snyder Grand. RNA-seq: a revolutionary tool for transcriptomics.Nat. Rev. Genet, 2009;10(i), 57–63. doi:10.1038/nrg2484

2.         Ozsolak F, & Milos PM. RNA sequencing: advances, challenges and opportunities.Nat. Rev. Genet,2011; 12(2), 87–98. doi:10.1038/nrg2934

3.         Bakhtiarizadeh MR, Alamouti AA. RNA-Seq based genetic variant discovery provides new insights into controlling fat deposition in the tail of sheep.Sci Rep 10, 13525 (2020). doi:10.1038/s41598-020-70527-viii

four.         Han Y, Gao S, Muegge 1000, Zhang W, & Zhou B. Advanced applications of RNA sequencing and challenges.Bioinform. Biol. Insights , 2015;9(Suppl 1), 29–46. doi:ten.4137/BBI.S28991

five.         Schuster SC. Next-generation sequencing transforms today's biological science.Nat. Methods, 2008;5(ane), sixteen–eighteen. doi:10.1038/nmeth1156

half dozen.         JP Sulzberger Columbia Genome Center.Genome sequencing: Defining your experiment. Columbia Systems Biology. https://systemsbiology.columbia.edu/genome-sequencing-defining-your-experiment. Accessed Baronial 24, 2021.

7.         Functional genomics Two. EMBL-EBI. https://www.ebi.ac.united kingdom/training/online/courses/functional-genomics-ii-mutual-technologies-and-data-assay-methods/rna-sequencing/performing-a-rna-seq-experiment/design-considerations/. Accessed September 6, 2021.

8.         Zhao Due south, Zhang Y, Gordon Due west et al. Comparison of stranded and not-stranded RNA-seq transcriptome profiling and investigation of gene overlap.BMC Genomics, 2015;16(1). doi:x.1186/s12864-015-1876-seven

nine.         Zhao S, Fung-Leung W-P, Bittner A, Ngo M, & Liu X. Comparing of RNA-seq and microarray in transcriptome profiling of activated T cells.PLOS ONE, 2014;9(i), e78644. doi:ten.1371/journal.pone.0078644

ten.       Rao MS, Van Vleet TR, Ciurlionis R, et al. Comparison of RNA-seq and microarray gene expression platforms for the toxicogenomic evaluation of liver from short-term rat toxicity studies.Front end. Genet. 2019;9:636. doi:x.3389/fgene.2018.00636

11.       Kukurba KR, Montgomery SB. RNA sequencing and analysis.Common cold Spring Harb Protoc. 2015;2015(xi):951-969. doi:x.1101/pdb.top084970

12.       The Cresko Lab of the Academy of Oregon. RNA-seqlopedia. University of Oregon. https://rnaseq.uoregon.edu/#library-prep-stranded-libraries. Accessed August 24, 2021.

thirteen.       Conesa A, Madrigal P, Tarazona Southward, et al. A survey of best practices for RNA-seq information analysis.Genome Biol., 2016;17. doi:ten.1186/s13059-016-0881-8

14.       Katz Y, Wang ET, Airoldi EM, & Burge CB. Analysis and blueprint of RNA sequencing experiments for identifying isoform regulation.Nat. Methods, 2010;7(12), 1009–1015. doi:10.1038/nmeth.1528

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