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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. [2] [3]
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The earliest RNA-Seq work was published in 2006 with one hundred thousand transcripts sequenced using 454 technology. [40] This was sufficient coverage to quantify relative transcript abundance. RNA-Seq began to increase in popularity after 2008 when new Solexa/Illumina technologies allowed one billion transcript sequences to be recorded.
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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]
Rail-RNA Scalable analysis of RNA-seq splicing and coverage. RAP RNA-Seq Analysis Pipeline, a new cloud-based NGS web application. RSEQtools "RSEQtools consists of a set of modules that perform common tasks such as calculating gene expression values, generating signal tracks of mapped reads, and segmenting that signal into actively transcribed ...
In bioinformatics, sequence analysis is the process of subjecting a DNA, RNA or peptide sequence to any of a wide range of analytical methods to understand its features, function, structure, or evolution. It can be performed on the entire genome, transcriptome or proteome of an organism, and can also involve only selected segments or regions ...
SHAPE has been used to analyze diverse RNA structures, including that of an entire HIV-1 genome. [25] The best approach is to use a combination of chemical probing reagents and experimental data. [26] In SHAPE-Seq SHAPE is extended by bar-code based multiplexing combined with RNA-Seq and can be performed in a high-throughput fashion. [27]