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Two biological techniques are used to study the transcriptome, namely DNA microarray, a hybridization-based technique and RNA-seq, a sequence-based approach. [1] RNA-seq is the preferred method and has been the dominant transcriptomics technique since the 2010s.
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.
In the 1980s, low-throughput sequencing using the Sanger method was used to sequence random transcripts, producing expressed sequence tags (ESTs). [ 2 ] [ 14 ] [ 15 ] [ 16 ] The Sanger method of sequencing was predominant until the advent of high-throughput methods such as sequencing by synthesis (Solexa/Illumina).
CITE-Seq (Cellular Indexing of Transcriptomes and Epitopes by Sequencing) is a method for performing RNA sequencing along with gaining quantitative and qualitative information on surface proteins with available antibodies on a single cell level. [1] So far, the method has been demonstrated to work with only a few proteins per cell.
Originally, transcriptome-wide RNA abundance could only be assessed using methods such as DNA microarrays or serial analysis of gene expression (SAGE). [4] [5] These methods are prohibitive in differing regards; microarrays, while cheap, provide inconsistent results [6] and SAGE is based on sanger sequencing, which provides limited throughput ...
Spatial transcriptomics is a method that captures positional context of transcriptional activity within intact tissue [1].The historical precursor to spatial transcriptomics is in situ hybridization [2], where the modernized omics terminology refers to the measurement of all the mRNA in a cell rather than select RNA targets.
Normalisation of RNA-seq data accounts for cell to cell variation in the efficiencies of the cDNA library formation and sequencing. One method relies on the use of extrinsic RNA spike-ins (RNA sequences of known sequence and quantity) that are added in equal quantities to each cell lysate and used to normalise read count by the number of reads ...
Trajectory inference as implemented in Slingshot for (a) a simulated two-dimensional dataset and (b) a single-cell RNA-seq dataset of the olfactory epithelium.. Trajectory inference or pseudotemporal ordering is a computational technique used in single-cell transcriptomics to determine the pattern of a dynamic process experienced by cells and then arrange cells based on their progression ...