Significant research on the sequencing of RNA has provided an abundance of knowledge, which proves to be a powerful tool for analysis of transcription. The protocols regarding the expression of gene analysis are divided into two categories. There is a traditional approach, which uses a 3′ tag digital gene expression, in which oligo-dT was utilized for the synthesis of cDNA, this consequently results in the enrichment of 3’ end polyadenylated fragments. These fragments are later digested by restriction endonuclease enzymes for the production of short cDNA tags.
The tiny or short tags then undergo sequencing by massive parallel technology to give ‘digital counts’ of mRNA molecules that originated from the gene.
RNA-Sequential Has Been Beneficial
There are a number of approaches of DGE that vary in their choices of the enzyme endonuclease. The approaches like these are extremely adapted from original serial analysis of the expression of genes. The well-expressed sequence tag approaches are revolutionized by extremely high throughput by the next generation. The recent RNA-sequencing allows entire transcription to take place in random fragments of hundreds of nucleotides, as well as reverse transcribed into libraries of cDNA. The fragments of cDNA are amplified by PCR and are then sequenced in parallel. This technology demands higher coverage to reach the power detection of lowly abundant transcripts. However, there is more information provided by DGE on transcript structure and dynamics by analyzing transcripts. There is a sharp reduction in the cost of sequencing, and the ever-increasing depth of sequencing.
In order to quantify the abundance of RNA, RNA-sequencing technology seems like a promising alternative. RNA-Sequence, however, poses a great technical challenge, while the challenge of microarray has already been there. A challenge by RNA-Sequence is always a complex one and it requires a protocol-specific influence of the nucleotide sequence.
A perfect experiment depicts the number of RNA-sequence mapping to a specific position in the genome and its function depends on RNA abundance. This must not rely on a further sequence of the position. While this is not the case, as the frequency of nucleotides is mapped relative to the start. With each map read, respecting strand and are then grouped by the platform. (Bias detection and correction in RNA-Sequencing data, 2011)
Microarray quality data sets consist of gene expression data, through the numerous quantitative platforms. These data sets are used to assess the performance of the platform and are further utilized for various ways of data processing methods. The data is collected through three different platforms for the two samples, which are Stratagene Universal Human Reference RNA and Ambion Human Brain Reference RNA. (Cheng Yang, 2014).
The two samples are then named as MAQC2 and MAQC3. The MAQC2 is composed of seven technical replicas of brain reference. The RNA samples and the replicates of UHR RNA samples sequenced on fourteen different lanes of two flow cells. Whereas, the MAQC3 has UHR RNA samples from around 4 various library preparations, which are also sequenced on 14 lanes of two cells flow. All these experiments use the ideal Illumina RNA-Seq protocol.
RNA-Seq Data From Liver and Kidney
The data set from the liver and kidney are extensively used in the literature of RNA-Seq data analysis approaches. Raw files of sequence files that are received from the liver and kidney are downloaded from Sequence Read Archive.
Yeast RNA-seq Data
The data set from yeast is used to demonstrate the sequencing biases caused occasionally by hexamer priming. There is the requirement of downloading raw sequence files of around a couple of technical replicates of an isogenic strain of wild-type.
Yeast RNA-Seq data which uses the alternative protocols.
This dataset is unique in its own way and it is described through the procedure of library preparation (Zhao et al., 2011).
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Bias detection and correction in RNA-Sequencing data. (2011, July 19). BMC Part of Springer Nature. https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-12-290.
Zhao, H., Zheng, W., & Chung, L. M. (2011). Bias detection and correction in RNA-Sequencing data. BMC Bioinformatics, 1(12), 290. https://www.researchgate.net/publication/51504202_Bias_detection_and_correction_in_RNA-Sequencing_data