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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]
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.
RNA-Seq [1] [2] [3] is a technique [4] that allows transcriptome studies (see also Transcriptomics technologies) based on next-generation sequencing technologies. This technique is largely dependent on bioinformatics tools developed to support the different steps of the process.
For example, to investigate a biological process which is estimated to occur for an hour, a researcher might design an experiment where the process is triggered for five minutes, 15 minutes, 30 minutes, 45 minutes, one hour, and two hours in separate cell culture samples before harvesting the cells for RNA-seq analysis.
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 ...
The three main steps of sequencing transcriptomes of any biological samples include RNA purification, the synthesis of an RNA or cDNA library and sequencing the library. [16] The RNA purification process is different for short and long RNAs. [16] This step is usually followed by an assessment of RNA quality, with the purpose of avoiding ...
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]
To yield the best possible aptamers one must maximize the effectiveness of the discovery process and the library itself. RNA and DNA secondary structure prediction by dynamic programming algorithms such as RNAfold [36] and by machine learning models such as SPOT-RNA, [37] MXfold2 [38] provides the opportunity to assess the ability of sequences ...