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The binding of the ADAR2 enzyme to the RNA timestamp initiates the gradual conversion of adenosine to inosine molecules. Over time, these edits accumulate and are then read through RNA-seq. This technology allows us to glean cell-type specific temporal information associated with RNA-seq data, that until now, has not been accessible. [1]
RNA-Seq can also be used to determine exon/intron boundaries and verify or amend previously annotated 5' and 3' gene boundaries. Recent advances in RNA-Seq include single cell sequencing, bulk RNA sequencing, [6] 3' mRNA-sequencing, in situ sequencing of fixed tissue, and native RNA molecule sequencing with single-molecule real-time sequencing. [7]
Currently RNA-Seq relies on copying RNA molecules into cDNA molecules prior to sequencing; therefore, the subsequent platforms are the same for transcriptomic and genomic data. Consequently, the development of DNA sequencing technologies has been a defining feature of RNA-Seq. [ 78 ] [ 80 ] [ 81 ] Direct sequencing of RNA using nanopore ...
RNA Seq Experiment. The single-cell RNA-seq technique converts a population of RNAs to a library of cDNA fragments. 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]
RNA-seq is emerging (2013) as the method of choice for measuring transcriptomes of organisms, though the older technique of DNA microarrays is still used. [1] RNA-seq measures the transcription of a specific gene by converting long RNAs into a library of cDNA fragments. The cDNA fragments are then sequenced using high-throughput sequencing ...
The data produced by single-cell RNA-seq can consist of thousands of cells each with expression levels recorded across thousands of genes. [7] In order to efficiently process data with such high dimensionality many trajectory inference algorithms employ a dimensionality reduction procedure such as principal component analysis (PCA), independent component analysis (ICA), or t-SNE as their first ...
The tools used at this stage depend on the sequencing platform. For instance, FastQC checks the quality of short reads (including RNA sequences), Nanoplot or PycoQC are used for long read sequences (e.g. Nanopore sequence reads), and MultiQC aggregates the result of FastQC in a webpage format. [11] [12] [13]
Another limitation of alignment-based approaches is their computational complexity and are time-consuming and thus, are limited when dealing with large-scale sequence data. [5] The advent of next-generation sequencing technologies has resulted in generation of voluminous sequencing data. The size of this sequence data poses challenges on ...