Differences Between RNA Sequencing and DNA Sequencing

DNA-seq and RNA-Seq are often used in combination to provide more comprehensive genomic and transcriptomic data to advance disease research, personalized medicine, agricultural breeding, and many other fields.

However, there are major differences between the two:

Purpose

• A comprehensive discussion of the differences between RNA-seq and DNA-seq in terms of process, technology, and applications:

The purpose of DNA-seq is to determine the order of the nucleotides (A, T, C, G) in DNA molecules, so as to comprehensively analyze and study specific genomes. It is capable of sequencing the genome (the entire set of genetic material) of an organism or deciphering the sequence of a particular gene; it can reveal the structure of genes as well as determine which genes are present in the chromosomes and where they are located, which is convenient for us to isolate and identify genes of interest or unknown.

• In short, DNA-seq can be used in three ways:

1. It can reveal the genetic information of an individual or a species, including gene sequences, gene mutations, gene polymorphisms and so on. This information is of great significance for us to understand the pathogenesis of hereditary diseases, predict the risk of diseases, and formulate personalized treatment plans.

2. Through DNA-seq, it can detect whether an individual carries a gene mutation related to a specific disease. For example, in prenatal diagnosis, fetal chromosomal disorders such as Down's syndrome and Edward's syndrome can be screened by detecting fetal free DNA in maternal peripheral blood. In addition, DNA-seq can be used for tumor diagnosis, which provides a basis for tumor typing, prognosis assessment and treatment selection by analyzing the genetic variation of tumor cells.

3. In the fields of ecology and biosystematics, DNA-seq technology is used to analyze the genome sequences of different species, revealing the kinship relationship between species, evolutionary history and biodiversity, which is of great significance for the conservation, utilization and management of biological resources and management of biological resources.

• RNA-Seq: RNA-seq is used to analyze the transcriptome, the complete set of RNA transcripts produced by the genome at any given time. RNA-seq measures gene expression levels and determines which genes are transcribed into RNA, providing information on alternative splicing events, post-transcriptional modifications, and non-coding RNAs, as well as information on transcript differences in a species over a specific period of time or in a specific environment. RNA-seq can provide a deeper understanding of the internal mechanisms of organisms and changes in the course of life activities through the study of expression levels, diversity and regulatory mechanisms.
• In short, there are three uses:

1. Gene expression analysis: RNA-seq can comprehensively determine the expression levels of all genes in cells or tissues, helping us to understand the changes in gene expression under different physiological and pathological conditions. By analyzing transcriptome data, we can study the activity and expression patterns of genes and their roles in specific biological processes (e.g., development, immune response, stress response, etc.).

2. Disease Mechanism Research and Diagnosis: RNA-seq can be used to analyze the changes in gene expression related to diseases and help reveal the molecular mechanisms of diseases. For example, by comparing the transcriptome differences between cancer tissues and normal tissues, RNA-Seq is able to detect abnormal expression of tumor-related genes, providing important information for early diagnosis, prognosis assessment and personalized treatment of cancer. In addition, RNA-seq can also be used to identify mutations or abnormal expression of genes related to genetic diseases, neurodegenerative diseases, etc.

3. Transcript and gene structure research: RNA-seq not only quantitatively analyzes the level of gene expression, but also reveals the whole picture of transcripts, including different splice variants, gene fusions, non-coding RNAs and so on. By deeply analyzing the structure of transcripts, RNA-Seq provides an important tool for gene annotation, functional studies and new gene discovery.

Technologies

• DNA-seq includes whole genome sequencing (WGS), whole exome sequencing (WES or WXS) and targeted sequencing.

WGS means sequencing the entire DNA genome, i.e., the complete genome sequence from the first DNA to the last DNA in the cells of a species, completely detected and in order. This technology is therefore capable of identifying almost any type of mutation in the genome, including SNVs, insertion deletions, structural variants and CNV-coding sequences. With WGS we can explore species without reference genomes or use it for some organisms with poor reference quality.

WES, on the other hand, focuses on sequencing the mRNA coding regions (exons), which usually represent a very small portion of the genome (e.g., 3% in humans), omitting regulatory regions such as promoters and enhancers. WES allows the detection of SNVs and insertion deletions in protein-coding genes as well as other functional elements such as microRNA sequences.

Both WGS and WES can be used to find rare mutations/common variants associated with a disease or phenotype, but WES is much cheaper and faster than WGS.

Targeted sequencing focuses only on a subset of the target gene (or target region) and is faster and cheaper than the previous two. Targeted sequencing focuses on only one area, is easy to analyze and is sensitive enough to detect low-frequency variants, especially in tumor samples where targeted sequencing is often needed.

• RNA-seq includes whole transcriptome sequencing (WTS), mRNA-seq and smRNA-seq.

Unlike DNA-seq, RNA-seq requires reverse transcription of extracted RNA into cDNA before amplification.The most common applications of RNA-seq are to detect changes in gene expression, variable splicing, post-transcriptional modifications, gene fusions, and also to detect mutations and SNPs.

If you want to know more about RNA-seq, please click "RNA Sequencing Techniques".

Workflow

• Sample Type: DNA-seq involves the extraction of DNA from cells, tissues, or organisms, which is relatively stable, does not degrade rapidly, and is easily obtained from a variety of sources (e.g., blood, saliva, tissue biopsies).RNA-seq involves the extraction of RNA, which is more fickle than DNA and degrades rapidly, so extracted RNA must be carefully preserved.
• Data output and analysis: The output of DNA-seq is the nucleotide (A, T, C, G) sequences that make up the genome. By outputting the results, we can study:

(1) By reading the DNA fragments in the genome, accurate nucleotide sequences can be obtained, which can directly reflect the genetic background and genetic information of the individual.

(2) DNA-seq can detect various types of genetic variation, such as : SNP, Indel, CNV, structural variation including chromosome rearrangement, inversion, translocation, etc. Through these information, we can analyze the genetic background of individuals, identify disease-related gene mutations, and study the evolutionary relationship of species.

• The output data of RNA-seq usually includes the count of RNA fragments (reads) aligned with a particular gene or transcript. Through these readings, we can study:

(1) By calculating the reads count or transcript expression of specific genes (such as FPKM, TPM and other standardized values), we can understand the activity of genes in specific cells or tissues.

(2) Different exon combinations of a gene can produce multiple transcripts ; a gene may have many different splicing forms to produce multiple transcripts.

(3) RNA-seq data are usually compared to the target reference genome to determine the location and structure of each transcript. Each reading corresponds to a specific gene or transcript, so the expression level of the gene can be speculated by counting these readings.

(4) By comparing RNA sequence data under different conditions (such as health and disease, different tissues or different experimental groups), DEGs can be identified and important changes in biological processes can be revealed. For example, in cancer research, RNA-seq can identify genes with significantly altered expression levels in cancer cells, providing clues for early diagnosis and targeted therapy of cancer.

(5) RNA-seq not only focuses on mRNAs encoding proteins, but also analyzes non-coding RNAs, such as lncRNAs and miRNAs.

Overview of RNA-Seq (Kukurba KR et al., 2015)

Tools

• DNA-seq analysis focuses on identifying mutations, sequence variants, and structural variants (e.g., copy number variants). Commonly used tools include comparison software (e.g., BWA, Bowtie), mutation and variant calling software (e.g., GATK, Samtools mpileup), structural variation tools (CNVnator, Lumpy), and functional annotation tools (ANNOVAR, VEP).
• RNA-seq analysis focuses on analyzing the transcriptome for information such as gene expression patterns, alternative splicing, and transcript structure and diversity. Commonly used tools include comparison and alignment tools (STAR, HISAT2), tools for quantifying gene expression levels (Cufflinks, Kallisto), tools for differentially expressed gene analysis (DESeq2, EdgeR), tools for alternative splicing analysis (DEXSeq, rMATS), tools for de novo transcript identification (StringTie, Cufflinks), and functional annotation tools (Blast2GO, g:Profiler).

Through the use of these different tools, we can also see that DNA-seq and RNA-seq have different focuses and have very different analysis processes.

Sequencing platform

• RNA and DNA-seq can use a variety of sequencing platforms, but these platforms are basically common. Such as Illumina, Ion Torrent, PacBio and Oxford Nanopore platforms (long-read sequencing technology for both DNA and RNA). However, each platform also has its own applicable points. For example, the Illumina platform is suitable for conventional short-reading RNA-seq, with high accuracy and high throughput. PacBio and Oxford Nanopore provide long read sequencing, which is suitable for complex transcriptome analysis or full-length RNA-seq. The Ion Torrent platform is fast and low-cost, and is suitable for small and medium-sized RNA-seq projects.

In addition to the above differences in these processes, RNA-seq also requires additional steps such as RNA separation, reverse transcription (general sequencing technology is based on DNA and cannot directly detect RNA), and sometimes RNA needs to be fragmented before sequencing.

Applications

• DNA-seq can be used to study the entire genome of an organism, identify mutations associated with disease, human genomics, personalized medicine and evolutionary studies, and population genetics.
• RNA-seq can be used to understand which genes are active or repressed in specific conditions or tissues, to study differences in gene expression under different conditions (e.g., healthy versus diseased tissues), to investigate splicing variants and RNA editing, to identify new transcripts including non-coding RNAs, and to study the regulatory mechanisms that control gene expression.

The fields of application of both DNA-seq and RNA-seq are cross-cutting, especially in disease research, but there are still differences. For example, generally, cancer patients need RNA-seq after DNA-seq. DNA testing is done to detect gene mutations in order to find possible therapeutic targets. However, DNA testing may miss something, so RNA-seq is also needed.

In particular, the presence of fusion genes is found to be an important form of mutation that drives tumor development. Currently, the FDA or NMPA has approved a number of corresponding targeted drugs for clinical treatment against fusion targets such as ALK, RET, ROS1, and NTRK1/2/3. Therefore, it is very important to detect fusion genes more efficiently in order to obtain highly effective targeting information.

Differences between DNA-seq and RNA-seq in Fusion Gene Detection

DNA-seq and RNA-seq are two important technologies for detecting fusion genes, but they are very different. DNA-seq is based on the DNA level, but the breakpoints of fusion genes usually occur in long intronic regions, and the breakpoints of fusion genes vary across patients and diseases in real life. However, DNA-seq cannot accurately cover the long intronic regions, which contain a large number of repetitive sequences, making it difficult to identify fusion genes.

Limitations of DNA-seq

Moreover, DNA-seq is limited in its ability to detect some DNA fragments with high GC content, as the designed probes cannot effectively capture these fragments. Additionally, the intronic sequences of most genes in organisms contain very similar repetitive sequences, making it difficult for DNA-seq to accurately identify these sequences in different genes. Lastly, genes undergo a complex post-transcriptional splicing process, which affects the location of fusion gene breakpoints. Based on these reasons, DNA-seq is defective in detecting fusion genes and needs to be supplemented with RNA-seq.

Advantages of RNA-seq in Fusion Gene Detection

On the other hand, RNA-seq can be more effective for detecting fusion genes. This is because the expression of fusion genes at the RNA level connects the two exons before and after, and the fusion breakpoint is relatively fixed. This feature facilitates the design of primers or probes more efficiently and accurately. At the RNA level, we can also detect the expression of fusion genes more effectively and detect functional changes, which can lead to more accurate follow-up treatment or medication.

Furthermore, evidence shows that detecting fusion genes at the RNA level is easier and more reliable than at the DNA level.

Studies have shown that MET inhibitors achieved good anti-tumor effects in patients with MET exon 14 jumping mutations, which suggests that MET exon 14 jumping mutations have the potential to serve as a new and important target for the treatment of NSCLC patients, and therefore the accurate detection of MET exon 14 jumping mutations is of great significance in guiding the clinic.Kurtis et al. respectively used DNA-seq and RNA-seq on tumor tissues from NSCLC patients, DNA-seq detected MET exon 14 jump mutations in 11 out of 856 NSCLC samples (1.3%), while RNA-seq detected MET exon 14 jump mutations in 17 out of 404 samples (4.2%), and RNA-seq was more sensitive than DNA- seq detection was more sensitive(Davies KD et al,.2019).

The causative factor of NSCLC is also mostly the emergence of fusion genes, and the fusion genes found to cause NSCLC are ALK, ROS1, RET, NTRK, NRG1, etc. These discovered gene fusions also provide us with precise therapeutic targets for the treatment of NSCLC. Then accurate detection of fusion genes has significance for us, most of them were previously detected by using DNA-seq, but it was found that DNA NGS could not detect all gene fusion events very accurately, the researchers further detected more than 2500 lung adenocarcinomas by using RNA NGS, and found that 195 (7.7%) gene fusions were detected and 119 (4.7%) MET exon 14 jump mutations. Moreover, when testing tissues previously tested by DNA NGS, only 254 of the 275 materials were available for RNA testing, but of these 254, alterations in mitogenic drivers that were not detected by DNA NGS were detected in 36 (14%) of the 254. 33 of the 36 patients were amenable to treatment, and the final 10 patients received specialized targeted therapy. Eight patients showed favorable clinical outcomes. This suggests that RNA NGS can detect gene fusion events not detected by DNA NGS(Benayed R et al,.2019).

More applications, refer to "Overview of RNA Sequencing Applications"

Summary

Summary table comparing DNA sequencing (DNA-seq) and RNA sequencing (RNA-seq):

Key Takeaways:

• DNA-seq focuses on studying the genetic code (mutations, variants) in DNA, helpful for disease diagnosis and evolutionary studies.
• RNA-seq focuses on studying gene activity (expression, splicing) and is especially useful for disease research, including cancer and gene fusion detection.

References:

1. Davies KD, Lomboy A, Lawrence CA, Yourshaw M, Bocsi GT, Camidge DR, Aisner DL."DNA-Based versus RNA-Based Detection of MET Exon 14 Skipping Events in Lung Cancer. " J Thorac Oncol. 2019;14(4):737-741. doi: 10.1016/j.jtho.2018.12.020
2. Benayed R, Offin M, Mullaney K, Sukhadia P, Rios K, Desmeules P, Ptashkin R, Won H, Chang J, Halpenny D, Schram AM, Rudin CM, Hyman DM, Arcila ME, Berger MF, Zehir A, Kris MG, Drilon A, Ladanyi M. "High Yield of RNA Sequencing for Targetable Kinase Fusions in Lung Adenocarcinomas with No Mitogenic Driver Alteration Detected by DNA Sequencing and Low Tumor Mutation Burden. "Clin Cancer Res.2019;25(15):4712-4722. doi: 10.1158/1078-0432.CCR-19-0225