All you need to know about analyzing gene expression with RNA Seq analysis

The goal of RNA sequencing involves the identification of expressed genomic locus/loci in a cell of interest. In most of the cases, differential analysis between the genomic expressions of cell populations is necessary to determine which protein expression is upregulated or downregulated in given conditions. It is more sensitive than microarrays, and it can detect minute levels of expression in given cell populations either in healthy or diseased states. 

What is an RNA sequence analysis?

The typical RNA-sequence analysis looks like this –

  1. The first step of any DGE study is sequencing. This step involves RNA extraction, library preparation, and sequencing. 
  2. The next step includes multiple complex sub-steps. It includes processing of sequencing reads, the estimation of individual expression levels for each gene, normalization, and the identification of the genes differentially expressed under different conditions.

What is the purpose of an RNA sequence analysis? 

Theoretically, RNA-sequence analysis of gene expression can not only quantify the expression of proteins in different conditions, but it can also meet the following goals –

  1. The detection and quantification of the transcripts that do not code for proteins. 
  2. Identification of splice isoforms. 
  3. The determination of novel transcripts.
  4. Determination of the sites of protein-RNA interactions.

Although the determination of differential genomic expression (DGE) between cell populations or different experimental conditions is the primary goal of most RNA-seq experiments, the application lacks standardized steps and widely accepted tools for the final analysis. The availability of myriads of premium as well as amateur software makes the task of choosing the right one much more difficult for the designer of the experiment. 

What should your automated RNA Seq analysis software do for you? 

The advances in library preparation methods allow the inclusion or exclusion of specific types of RNA sequences. You can choose to target actively translated RNA directly during library preparation. You can select any typical library preparation method that complies with the latest practices and publication standards. 

Most high-throughput data is stored in the NCBI Sequence Read Archive (SRA). The standard format for download and storing sequence reads from SRA is FASTQ. This format is readily compatible with most sequencing and RNA Seq analysis software available. The standardized FASTQ format files come with sequence names, nucleotides, and associated quality scores. 

There was a time when the subsequent steps required endless time and patience, but now, you can automate the entire sequencing and alignment process. Here’s what your RNA sequencing alignment and analysis tool can do for you –

Aligning the sequence reads

The first step towards RNA Seq analysis is sequence alignment. Manually aligning megabases of data generated from NGS of RNA samples is next to impossible. Most importantly, the automated alignment algorithms of the RNA Seq analysis software can accommodate customizations for the proper alignment of unique RNA sequences, including 3’UTR regions, AT-rich regions, and non-coding transcripts. 

SOLiD is computationally expensive, and it requires extensive expertise of bioinformatics for achieving the correct alignment of RNA sequence reads. The advanced automated RNA Seq analysis options allow options for the proper alignment of DNA polymorphisms, IN/DEL mutations, and adaptor sequences without any additional time. 

Here’s how your state-of-the-art RNA Seq analysis software makes aligning the reads easy and accurate –

 

  • Align the reads to exon-exon junctions

 

You will find more than one read that will overlap an exon-exon junction. That is there will be positions that will refer to an excised intron. It is difficult to map these reads directly onto the genome. Conventional methods of de novo discovery of the reads can only map parts of the read to these junctions. Manual application takes considerable time and knowledge. The automated sequencing and alignment tools can complete the mapping of the exon regions with minimal error and maximum speed. 

 

  • De novo splice junction discovery

 

The discovery of splice junctions can reduce the accuracy of the alignment reads as compared to referring to a library of known exon-exon junctions. Choosing longer anchor lengths can save your resulting analysis data from adding false positives to the report. 

The standard publication-ready alignment tool will offer files in SAM and BAM formats. Before you begin working with your RNA Seq analysis software platform, always ensure that you get the choice of file format option for downloading and saving your sequence alignment data. 

Visualization of the RNA seq data

The visualization of the RNA sequence data enables its rapid assessment. Although you can utilize web-based visualization of the RNA sequence alignment of your cell of interest, it is ideal to use cloud-based technology for the ease of access. Cloud-based RNA Seq analysis software offers all the necessary tools for the alignment and analysis of different types of RNA sequences. They support all file formats, including BAM, SAM, and BED. Additionally, the cloud-based technologies for NGS RNA sequence analysis offer PDF and EPS formats during the export of the analysis reports. That makes the task of creating illustrations for publication much easier than it was before. 

Generation of volcano plots and heatmaps

Automated RNA Seq analysis workflows typically include heatmaps and volcano plots. A volcano plot can help you determine the up-regulation and down-regulation of target genes. You can use available sets of filters and p-value sliders to create a heatmap for your differential expression study. Additionally, you should be able to download high-resolution images for your publication instantly and save copies on your desktop or cloud storage for offline access. The modern RNA sequencing and analysis tools can help you determine the expression count and represent the same, instantly in a neat table format. It aligns the number of reads to the exons of a gene. The results include expression count charts as well as a table for the practical overview of metrics for individual genes under scrutiny. 

 

What will determine the sensitivity of your RNA sequencing and analysis? 

The sensitivity of your RNA-sequencing experiment will depend upon the sequencing depth. Only a few million reads are enough for the detection of transcripts that are expressed in differential conditions. Even the unexpressed regions of the genome will indeed contain a few reads. Therefore, a single read mapping to a transcript does not qualify as a detection. The best way to get around this problem is by setting the threshold of detection to the 95-percentile of background detection. Make sure that your experiment design and choice of RNA Seq analysis software is not a trade-off between the number of experimental conditions, biological replicates, and the RNA sequencing depth.

 



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