RNA-Seq

Next generation sequencing technique already began to revolutionize the analysis of transcriptome.  This technique is referred to as RNA-Seq.  RNA-Seq uses recently developed deep-sequencing technique, Digitial Gene Expression tag profiling (DGE), or a massive parallel sequencing technique.  This application is for mapping and quantifying transcriptome.  For this reason, it already used to investigate genetic variation (Korbel et al, 2007), transcription factors binding sites (Mikkelsen et al, 2007), alternative splicing (Sultan et al, 2008), microRNAs profiling (Morine et al, 2008), and DNA methylation (Cokus et al, 2008). Another word to describe this approach is that this approach for transcriptional profiling by sequencing alone is sure to look more complete ways to describe and measure quantitative of the full mRNA population of transcript in organism (Nagalakshmi et al, 2008). 

RNA-Seq has several key advantages over previous technologies such as inherent limitation of array-based systems, bypass problems inherent to analog measurement, and high costs.  First, RNA-Seq can detect unknown transcripts to correspond to existing genomic sequence.  Second, RNA-Seq approach is highly reliable, with relatively little technical variation than the RNA hybridization array approach.  Third, RNA-Seq is unlike RNA hybridization array approach, it can enable analyses by the low abundances signal detection, the alternative splice variants, the noncoding RNA profiling, and other novel transcripts.  RNA-Seq also indicated highly accurate for quantifying expression levels, as determined using quantitative PCR (qPCR) and spike-in RNA controls of known concentration.  Fourth, RNA-Seq is a simpler, less time-consumng, and low cost to analyze by direct ultra high throughput sequencing. 

Reference:

    Cokus S., Feng, S., Zhange, X., Chen, Z., Merriman, B., Haudenschild, C., Pradhan, S., Nelson, S., Pellegrini, M., and Jacobsen, S. (2008). Shotgun bisulphate sequencing of the  

    Arabidopsis genme reveals DNA methylation patterning.  Nature.  452, 215-219.<o:p></o:p>

Korbel, J., Urban, A., Affourtit, J., Godwin, B., Grubert, F., Simons, J., Kim, P., Palejev, D., Carriero, N., Du, L, et al, (2007).  Paired-end maping reveals extensive structural variation in the human genome.  Science. 318, 420-426.<o:p></o:p>

Mikkelson, T., Ku,  M., Jaffe, D., Issac, B., Lieberman, E., Giannoukos, G., Alvarez, P., Brockman, W., kim, T., Koche, R. et al. (2007).  Genome-wide maps of chromatin state in pluripotent and lineage-committed cells.  Nature.  448. 553-560.<o:p></o:p>

Morin, RD., O’Connor MD., Griffith M, Kuchenbauer, F., Delaney, A., Prabhu, AL., Zhao, Y., McDonald, H., Zeng, T., Hirst, M., Eaves, CJ., and Marra MA. (2008). Application of massively parallel sequencing to microRNA profiling and discovery in human embryonic stem cells.  Genome Research. 18, 610-621.<o:p></o:p>

Nagalashmi U., Wang, Z., Shou, C., Raha, D., Gerstein, M., Snyder M. (2008).  The transcriptional landscape of the yeast genome difined by RNA sequencing.  Science.  320, 1344-1349. <o:p></o:p>

Sultan M., Schulz MH., Richard, H., Magen A., Klingenhoff A., Scherf M., Seifert M., Borodina T., Soldatov, A., Parkhomchuk, D., Schmidt, O’Keeffe, S., Haas, S., Vingron M., Lehrach, H., and Yaspo ML. (2008). A Global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science 321, 956 – 960