Application of Transcriptome Sequencing

Transcriptome sequencing analysis of gene expression levels and differential expression analysis

The expression characteristics of genes in biological cells can be represented by measuring the mRNA level (concentration), and can be detected at different levels in any tissue. Due to post-transcriptional level regulation (interference RNA), the connection between relevant mRNA and relevant proteins may not be strong, but measuring the concentration of mRNA is still an important indicator for detecting cell-related expression levels and health. The only accurate method for determining mutations of individual genes is to compare them with the transcriptome sequence of the germ line. Expression profiling chip technology can be used to study the effects of individuals, time, and genes on expression, including differences in expression of different genes in the same individual at the same time, differences in expression of the same gene in the same individual at different times, and differences in expression of the same gene in different individuals at the same time, mainly reflecting differences in expression levels.

RNA differential expression analysis is mainly the expression difference of cells under different conditions. RNA sequencing can detect the ability of the entire transcriptome, making it an important tool for detecting biological gene expression. Bioinformaticians have invented dedicated automation systems to manage the large amount of sequence data and create new algorithms and software for comparing sequencing results. Databases have been used to find genes in specific pathways. The main advantage of RNA-Seq data analysis on array platforms is that it can cover the entire transcriptome, which may unlock gene regulation networks and be used to detect and predict alternative splicing of genes with the same biological function.

Mining of new genes from transcriptome sequencing

Transcriptome sequencing generally refers to the sequencing of all genes expressible in an organism. By comparing the obtained sequences with known sequences in public databases, new genes can be found and their functions can be roughly predicted. Comparisons of different species can also be used for mining some genes of the target species. After the sequencing of the higher plant genome, the genome of the species can be assembled and spliced, and the genes can be located and functionally pre-annotated. However, the current research level does not have in-depth study of many genes, and the position cannot be accurately determined. Therefore, transcriptome sequencing is needed to optimize the analysis of target genes by correlating with the traits of the species.

Transcriptome sequencing analysis of single nucleotide polymorphisms

After transcriptome sequencing, a large number of SNPs can be found by comparing with the reference genome, and in-depth analysis of SNPs is of great significance to biological research. Early transcriptome SNP mining could be analyzed on platforms, and in the verification process, researchers were able to obtain nearly 5,000 conserved single nucleotide polymorphisms in about 2,400 maize genes.

With the development of sequencing technology, more and more SNPs can be discovered. Transcriptome sequencing has become an important method for studying complex molecular mechanisms such as the impact of biological environment, developmental regulation, and cell type, and also an important prerequisite for the identification of molecular marker polymorphisms such as SSR and SNP.

Transcriptome sequencing gene function annotation

After transcriptome sequencing, gene function annotation can be carried out more directly. Gene function annotation requires the use of bioinformatics methods to compare the unknown gene sequences obtained from sequencing with those in public databases, and to predict the function of the target unknown gene through analysis of clustering or homology with known genes in public databases.

Currently used gene function prediction classification systems are mainly GO classification and KEGG function classification. The idea adopted by GO is clustering analysis. Clustering analysis is to compare objects in the same group with similar other groups (clusters) to infer the function of the target gene. Clustering analysis includes hierarchical clustering, K-means clustering, K-centroid clustering, and some clustering techniques based on networks or models.