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Time-resolved RNA sequencing methods are applications of RNA-seq that allow for observations of RNA abundances over time in a biological sample or samples. Second-Generation DNA sequencing has enabled cost effective, high throughput and unbiased analysis of the transcriptome . [ 1 ]
RNA-Seq (named as an abbreviation of RNA sequencing) is a technique that uses next-generation sequencing to reveal the presence and quantity of RNA molecules in a biological sample, providing a snapshot of gene expression in the sample, also known as transcriptome.
The word "transcriptome" was first used in the 1990s. [19] [20] In 1995, one of the earliest sequencing-based transcriptomic methods was developed, serial analysis of gene expression (SAGE), which worked by Sanger sequencing of concatenated random transcript fragments. [21] Transcripts were quantified by matching the fragments to known genes.
A unique barcode sequence used on the cell hashing antibody can be designed to be different from an antibody barcode present on the ADTs used in CITE-seq. This makes it possible to couple cell hashing with CITE-seq on a single sequencing run. [12] Cell hashing allows super-loading of the scRNA-seq platform, resulting in a lower cost of sequencing.
The term transcriptome is a portmanteau of the words transcript and genome; it is associated with the process of transcript production during the biological process of transcription. The early stages of transcriptome annotations began with cDNA libraries published in the 1980s. Subsequently, the advent of high-throughput technology led to ...
Whereas high sequence coverage for a genome may indicate the presence of repetitive sequences (and thus be masked), for a transcriptome, they may indicate abundance. In addition, unlike genome sequencing, transcriptome sequencing can be strand-specific, due to the possibility of both sense and antisense transcripts. Finally, it can be difficult ...
These fragments are sequenced by high-throughput next generation sequencing techniques and the reads are mapped back to the reference genome, providing a count of the number of reads associated with each gene. [13] Normalisation of RNA-seq data accounts for cell to cell variation in the efficiencies of the cDNA library formation and sequencing.
3' mRNA-seq is a quantitative, genome-wide transcriptomic technique based on the barcoding of the 3' untranslated region (UTR) of mRNA molecules. Unlike standard bulk RNA-seq, where short sequencing reads are generated along the entire length of mRNA transcripts, only the 3' end of polyadenylated RNAs are sequenced in 3' mRNA-seq.
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