Back
Original link:Metabolomics - Transcriptomics
Transcriptome sequencing (RNA Seq) refers to the use of second-generation high-throughput sequencing technology for cDNA sequencing, which comprehensively and rapidly obtains almost all transcripts of a specific organ or tissue of a species in a certain state. With the advent of the post genomic era, various omics technologies such as transcriptomics, proteomics, metabolomics, etc. have emerged one after another, among which transcriptomics is the first to develop and the most widely used technology.
RNA Seq has the following advantages:
(1) High throughput, using second-generation sequencing platforms can obtain several to tens of billions of base sequences, which can meet the requirements of covering the entire genome or transcriptome;
(2) High sensitivity, capable of detecting rare transcripts with as few copies in cells;
(3) High resolution, the resolution of RNA Seq can reach a single base, with good accuracy, and there is no cross reaction or background noise problem caused by traditional microarray hybridization fluorescence simulation signals;
(4) Unrestricted, full transcriptome analysis can be performed on any species without the need for pre designed specific probes, allowing for direct transcriptome analysis of any species. At the same time, it can detect unknown genes, discover new transcripts, and accurately identify variable splicing sites, SNPs, and UTR regions.
RNA seq technology can detect the overall transcriptional activity of a specific species at the single nucleic acid level, thereby comprehensively and rapidly obtaining almost all transcriptional information of the species in a certain state. Due to its ability to obtain abundance information of all RNA transcripts and high accuracy, transcriptome sequencing has a wide range of applications. Mainly used for:
(1) Detecting new transcripts, including unknown transcripts and rare transcripts;
(2) Research on gene transcription levels, such as gene expression levels and differential expression between different samples;
(3) Functional research on non coding regions, such as microRNA, non coding long RNA (IncRNA), and RNA editing;
(4) Research on transcriptional structural variations, such as alternative splicing and gene fusion;
(5) Develop SNPs and SSRs, etc.
