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All transcriptomic techniques have been particularly useful in identifying the functions of genes and identifying those responsible for particular phenotypes. Transcriptomics of Arabidopsis ecotypes that hyperaccumulate metals correlated genes involved in metal uptake, tolerance, and homeostasis with the phenotype. [156]
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.
High-throughput next-generation sequencing has become a popular technique in transcriptomics, which represent a snapshot of gene expression. In eukaryotes, making phylogenetic inferences using RNA is complicated by alternative splicing, which produces multiple transcripts from a single gene.
While these breakthroughs in transcriptomics technologies have enabled the generation of single-cell transcriptomic data, they also presented new computational and analytical challenges. Bioinformaticians can use techniques from bulk RNA-seq for single-cell data.
Single-cell transcriptomics uses sequencing techniques similar to single-cell genomics or direct detection using fluorescence in situ hybridization. The first step in quantifying the transcriptome is to convert RNA to cDNA using reverse transcriptase so that the contents of the cell can be sequenced using NGS methods as was done in genomics.
Spatial transcriptomics, or spatially resolved 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.
Imagery in this article has been manipulated using degradative rendering techniques causing loss of data. Background transitions are also created through the removal and replacement of data. This is fun for design. It’s unacceptable when the data concerned is the paper trail of your country’s governance.
Several transcriptomics technologies can be used to generate the necessary data to analyse. DNA microarrays [1] measure the relative activity of previously identified target genes. Sequence based techniques, like RNA-Seq, provide information on the sequences of genes in addition to their expression level.