The transcriptome is the total of all gene transcription products in a cell under a given state or physiological situation such as messenger RNA, ribosomal RNA, transport RNA, and non-coding RNA; in a more limited sense, it is the compilation of all mRNAs.Transcriptomics is the study of gene expression at the RNA level, and it's a useful tool for researching cell phenotype.
The transcriptome is the link between the genome's genetic data and biological processes in the proteome. The most essential and well-studied method of organism regulation is regulation at the transcriptional level. The transcriptome, unlike the genome, is more spatial and temporal. The mechanism of unknown genes can be deduced and the action mechanism of particular regulatory genes can be discovered using the bioinformatics tool of transcriptomics data. Quantitative transcript analysis can be employed to comprehend the activity and expression of specific genes, as well as for disease diagnosis and treatment. Transcriptomics research can also aid in the growth of personalized medical treatments.
Figure 1. Workflow and downstream analysis of transcriptome studies. (Xu, 2017)
The goal of RNA-seq experiments is to understand transcriptomic adjustments in organisms in reply to a specific treatment. They are also used to determine the cause and/or effect of a mutation by analyzing the alterations in gene expression that occur as a result of the mutation. Many innovations in RNA-Seq analysis have been made thanks to some comprehensive algorithms designed primarily to map short stretches of nucleotide sequences to a genome while being informed of the RNA splicing process.
The following are some of the findings of the investigation:
(1) info preprocessing;
(2) info statistics and quality evaluation;
(3) transcript expression analysis;
(4) differentially expressed gene analysis;
(5) differentially expressed gene assessment Cluster assessment, GO enrichment assessment, KEGG enrichment assessment, and interaction network assessment are among the methods used.
1. Quality control, trimming, error correction, and pre-processing of data
The first phase in the RNA-Seq bioinformatics pipeline is to evaluate the quality of the raw data. To ensure a coherent outcome, it is frequently essential to filter data, taking away the low-quality sequences or bases (trimming), adapters, contaminations, overrepresented sequences, or correcting errors.
2. Alignment tools
After quality control, the sequenced reads are aligned to a reference genome (if available) or a transcriptome database in the first phase of RNA assessment. A list of sequence alignment applications is also available.
3. Normalization, quantitative analysis, and differential expression
The abundance of each gene expressed in a specimen is normalized and calculated in this phase. Some of the components used to quantify expression are RPKM, FPKM, and TPM. Some applications are also intended to investigate the differences in genetic expression between specimens (differential expression). The quality of read alignment and the consistency of isoform reconstruction play a big role in quantitative and differential research. Several cases of research comparing differential expression methods are accessible.
4. Workbench (assessment pipeline / integrated solutions)
5. Alternative splicing assessment
6. Fusion genes/chimeras/translocation finders/structural variations
Abnormal genetic adjustments such as fusions or translocations can occur as a consequence of genome arrangements caused by diseases such as cancer. The detection of these alterations is crucial in carcinogenesis research.
7. Copy number variation identification
CNVseq uses a statistical feature extracted from array-comparative genomic hybridization to identify copy number variants. BLAT is used to align sequences, R modules are used to calculate, and Perl is used to fully automate the process.
8. Single-cell RNA-Seq
Sequencing of single cells. Bulk RNA-Seq is a conventional RNA-Seq technique in which RNA is extracted from a collection of cells or tissues rather than from a single cell, as is the case with single-cell techniques. Some instruments accessible to bulk RNA-Seq are also employed for single-cell assessments, but to face the specificity of this method new algorithms were enhanced.
9. RNA-Seq simulators
In silico read generators are effective instruments for comparing and testing the efficacy of RNA-Seq data handling algorithms. Furthermore, some of them allow for the analysis and modeling of RNA-Seq protocols.
10. Transcriptome assemblers
The transcriptome, which includes both non-coding and protein-coding RNAs, is the overall population of RNAs conveyed in a single cell or group of cells. There are two kinds of methods to prepare transcriptomes. Genome-guided techniques utilize a reference genome as a framework for aligning and constructing reads into transcripts (if feasible, a finished and large genome). When a genome is not accessible, genome-independent methods are utilized. Reads are assembled immediately in transcripts in this situation.
11. Co-expression networks
12. miRNA prediction and evaluation
13. Visualization instrument
14. Functional, network, and pathway assessment instrument
15. Further annotation instrument for RNA-Seq data
16. RNA-Seq datasets
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