By default, the narrow transcriptome only includes mRNA, whereas the whole-transcriptome includes all transcripts in a cell or tissue, such as mRNA and non-coding RNA. Small RNA (represented by miRNA), long non-coding RNA (lncRNA), and circular RNA (circRNA) with regulatory functions are currently the priority of non-coding RNA studies. These kinds of ncRNA all have regulatory objects that are attributed to mRNA.
Figure 1. The data analysis workflow for the whole transcriptome sequencing. (Liu, 2014)
These kinds of ncRNA all have regulatory objects that are attributed to mRNA.
While identifying mRNA, bioinformatic evaluation of whole-transcriptome sequencing data can identify a huge proportion of miRNA, lncRNA, and circRNA. The whole-transcriptome's expression rate and component, as well as the regulatory network that connects them, can be fast, thoroughly, and accurately acquire data information linked to all transcripts of a particular biological mechanism.
The following research can benefit from whole-transcriptome sequencing data analysis: (1) disease progression research, (2) animal and plant development, (3) cell distinctions and advancement, (4) plant stress resistance, and (5) tumor growth and metastasis, to name a few.
The transcriptome is the link between the genome's genetic information and biological processes in the proteome. The most essential and well-studied method of organism regulation is regulation at the transcriptional level. The transcriptome, not like the genome, is more spatial and temporal. The mechanism of correlating unknown genes can be deduced and the state behavior of particular regulatory genes can be discovered using bioinformatic assessment of transcriptomics data. Quantitative transcript assessment can be utilized to comprehend the activity and expression of specific genes, as well as to diagnose and treat diseases. Transcriptomics research can also aid in the growth of personalized medical treatments.
Whole-transcriptome sequencing's bioinformatic assessment content is divided into five major sections. Small RNA data assessment, circular RNA data assessment, mRNA data assessment, lncRNA data assessment, and joint assessment are all examples of this.
The steps for analyzing small RNA data are as follows:
(1) data pre-processing,
(2) plotting to reference genome,
(3) small RNA detection,
(4) sequence assessment,
(5) differential expression assessment,
(6) miRNA target gene assessment,
(7) GO enrichment assessment,
(8) KEGG enrichment assessment.
Data pre-processing, mapping to the reference genome, circular RNA classification, differential expression assessment, circular RNA target gene assessment, GO functional categorization, and KEGG metabolic pathway assessment are all part of circular RNA assessment.
Data quality control, mapping to the reference genome, expression abundance assessment, GO functional categorization, KEGG metabolic pathway assessment, gene differential expression profiling, GO enrichment assessment of differential genes, and KEGG enrichment assessment of differential genes are all steps in the mRNA data assessment process.
Data quality control, lncRNA identification, analysis of genes near lncRNA on the chromosome, lncRNA differential expression analysis, lncRNA target gene prediction, GO enrichment assessment of differentially expressed target genes, and KEGG enrichment assessment of differentially expressed target genes are all procedures used in lncRNA data assessment.
Target gene assessment, contending endogenous RNA interaction assessment, co-expression network assessment, protein interaction network assessment, and key driver gene network assessment are all part of the joint assessment.
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