Mechanisms in RNA sequencing
RNA sequencing is one of the most powerful tools to study the whole transcriptome of an organism. This is one of cost-effective then than traditional RNA/cDNA cloning and sequencing techniques and also more cost-effective then microarray technique.
Several crop plants, including rice, grape, and soybean, have had their transcriptomes sequenced using RNA-Seq (RNA-Seq – an Overview | ScienceDirect Topics, n.d.). The genetic mechanisms governing plant growth and development as well as responses to various stresses like salinity and drought have been key revelations from this. RNA-Seq makes it possible to study non-coding sections of the genome, enabling a better understanding of complex gene regulation and alternative splicing events compared to exome sequencing.
RNA sequencing steps
RNA-seq procedures typically entail the following actions:
RNA extraction and RNA quality control:
RNA extraction is the first step when this step the desired RNA molecules are isolated from the interest cell or tissue. this is conducted by various methods to extract quality RNA.
The extracted RNA is tested for amount, purity, and integrity using techniques like spectrophotometry and electrophoresis. This step assures that the RNA is appropriate for downstream processing.
RNA library preparation and library quality control:
collected RNA molecules are stored in a sequel sequencing library. this process contains several steps which are:
- mRNA enrichment
- RNA fragmentation
- reverse transcription into cDNA
- addition of adapters for sequencing
- PCR amplification to create a sufficient amount of library for sequencing.
qPCR or fragment analysis is used to measure the quantity and quality of the produced RNA library. This step makes sure the library is excellent and ready for sequencing.
Sequencing:
The RNA library is placed onto a sequencing platform, like Oxford Nanopore or Illumina, where the sequencing procedure is carried out. Depending on the platform, high-throughput sequencing produces millions to billions of short or long sequence reads.
Read alignment
using specialized algorithms such as Bowtie, BWA, STAR, HISAT, and TopHat to align or map to a reference genome or transcriptome. Genomic alignment algorithms like Bowtie and BWA utilize Burroughs-Wheeler indexing to achieve rapid genome alignment (Grant et al., 2011). At this stage, they assign the readings to specific genomic regions or transcripts.
Quantification and analysis
Gene expression levels were calculated using aligned reads. Abundance of transcripts, detection of differentially expressed genes, gene ontology analysis, and other aspects of gene expression and regulation are all explored using a variety of computational techniques and tools.
Data interpretation
Researchers evaluate the analysis findings to gain insights into specific gene expression patterns, biological pathways, or functions. They often employ visualization tools and statistical analyses to assist in interpreting the data.
RNA sequencing Advantages
RNA sequencing offers high throughput and better resolution than Sanger sequencing and microarrays (Kukurba & Montgomery, 2015). It is highly precise and sensitive, enabling accurate transcript level estimation. RNA-seq can detect under-expressed transcripts effectively, contributing to a comprehensive transcriptome analysis.
Background noise limits gene expression quantification using the array hybridization technique at the low end, while signal saturation occurs at the high end. The discrete, digital sequencing read counts generated by RNA-Seq technology allow expression to be quantified over a wide dynamic range (RNA-Seq vs Microarrays | Compare Technologies, n.d.).
Unlike microarrays, RNA sequencing does not depend on existing reference genomes or annotation databases. This is particularly useful for organisms with poorly annotated genomes or for studying non-model organisms.
RNA sequencing Disadvantages
Interpreting the large amounts of complex data generated by RNA sequencing requires thorough bioinformatics analysis. According to a study by Guo et al. (2018), a major obstacle for researchers using RNA sequencing was the lack of bioinformatics skills.
Short read lengths make it difficult to accurately detect multiple isoforms from a given gene because RNA-seq technologies have inherent biases and limitations when sequencing library preparation and short-read assemblies. Biases and defects in library preparation affect how RNA molecules are represented and can result in inaccurate measurement of gene expression. Additionally, different isoforms produced by alternative splicing or genetic variants are difficult to distinguish due to short read lengths. As a result, these techniques struggle to provide a complete assessment of the entire isoform type for a given gene (Hong et al., 2020).
RNA seq process is a highly time-consuming process and a large number of samples are required. Also, it is computationally intensive, and the interpretation of results can be complex and challenging. When library preparation processes are complex and challenging process compare to others.
RNA seq does not completely cover the transcriptome, especially for rare or low-expressed transcript isoforms. In some situations, other techniques such as microarrays may be preferable to RNA sequencing because of lower sensitivity for low-expressed transcripts, according to a study by Marguerite et al. (2012)
