RNA-Seqfrom Genetic Engineering News

Next-generation sequencing (NGS) allows the interrogation of genomes and transcriptomes at unparalleled resolution. NGS is becoming a powerful tool to identify cancer mutations that will eventually be translated to the clinic.

Further, second-generation RNA-Seq technology permits the simultaneous evaluation of gene expression and transcript structure at a high level of accuracy and at a single-nucleotide level. RNA-Seq has been called a revolutionary tool for transcriptomics. It works by utilizing NGS high-throughput technology to characterize cDNAs representative of the cell’s transcriptome.

RNA-Seq can be a valuable analytical tool for a variety of applications, notes Erik K. Flemington, Ph.D., professor of pathology, Tulane Health Sciences Center. “In my laboratory, we have utilized this technology to identify and analyze the transcriptomes of infectious viral organisms, to characterize tumor microbiomes, and for microRNA (miRNA) target analysis studies.”

Dr. Flemington says that often viruses have a high gene density, making it difficult to discriminate overlapping transcripts using RNA-Seq. “Newer RNA-Seq methodologies are allowing us to overcome those challenges. Using these methods, we are finding out that the old dogma that there are only a small number of transcripts isn’t true. In fact, we have identified an abundance of previously unannotated and/or undescribed transcripts in viromes.”

As an example, Dr. Flemington and colleagues studied Epstein-Barr virus (EBV), a human pathogen that causes malignancies such as Burkitt lymphoma and Hodgkin disease. “We used second-generation RNA-Seq pipeline tools and developed new tools to customize the approaches for the analysis of viromes in the context of their host.

“Among other things, these new strategies allowed for the identification of new viral genes and transcript isoforms important for EBV to establish infection. Overall, these studies allowed us to identify a whole new set of transcripts that are potentially related to such processes as cell fate determination and inflammatory events.”

Another use of RNA-Seq is to characterize tumor microbiomes. “Clinical samples may contain exogenous agents such as viruses. This is important to know because some of these contribute to tumor development. By assessing clinical samples with RNA-Seq we can discover if the tumor has viruses associated with it.

“An example is the analysis of stomach cancers. The identification of viruses in clinical samples is highly tractable, and instead of needing to perform numerous assays looking for each virus one at a time, RNA-Seq allows identification in one assay alone. As this technology is used more and more in clinical samples, we may be able to better determine which viruses are associated with which tumors and what the clinical significance of these interactions is.”

Dr. Flemington also employs RNA-Seq for miRNA-targeting studies. “The regulation of gene expression by miRNAs is a fundamental mechanism for controlling a number of biological processes. We used RNA-Seq to study, for example, miRNA-155. The gene encoding miRNA-155 was classified as an oncogene long before it was identified as an miRNA. It is now implicated in a wide variety of cancers.

“Previous studies have utilized microarrays to assess miRNA-mediated decreases in target RNA. But this approach suffers from technical limitations. We employed RNA-Seq because of its high level of accuracy, broad dynamic range, ability to assess transcript structure, and because it can sensitively assess transcriptome alterations. Using this approach, we were able to identify a large inferred targetome, and more interestingly, we could readily study the role that transcript structure plays in microRNA targeting.” (read more… )

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Comments

One Response to “NGS & RNA-Seq Form Dynamic Duo”

  1. María on August 8th, 2012 10:37 am

    Hello!
    I would like to design an experiment with RNA-SEQ in which I want to see differential expression between differents tissues and differents timepoints and I also want detect polymorphism. My question is, can I use the same data of the same samples to carry out these approaches. I’m reading different articles about RNA-seq, but I’m not sure about that and about the number of replicates that i have use.
    Thank you very much,

    María

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