Saturday, February 18, 2012

Computational Algorithm Developed To Assist In Cancer Treatments


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High-throughput DNA sequencing technologies are leading to a revolution in how clinicians diagnose and treat cancer. The molecular profiles of individual tumors are beginning to be used in the design of chemotherapeutic programs optimized for the treatment of individual patients. The real revolution, however, is coming with the emerging capability to inexpensively and accurately sequence the entire genome of cancers, allowing for the identification of specific mutations responsible for the disease in individual patients.

There is only one downside. Those sequencing technologies provide massive amounts of data that are not easily processed and translated by scientists. That's why Georgia Tech has created a new data analysis algorithm that quickly transforms complex RNA sequence data into usable content for biologists and clinicians. The RNA-Seq analysis pipeline (R-SAP) was developed by School of Biology Professor John McDonald and Ph.D. Bioinformatics candidate Vinay Mittal. Details of the pipeline are published in the journal Nucleic Acids Research.

"A major bottleneck in the realization of the dream of personalized medicine is no longer technological. It's computational," said McDonald, director of Georgia Tech's newly created Integrated Cancer Research Center. "R-SAP follows a hierarchical decision-making procedure to accurately characterize various classes of gene transcripts in cancer samples."

There are at least 23,000 pieces of RNA in the human genome that encode the sequence of proteins. Millions of other pieces help regulate the production of proteins. R-SAP is able to quickly determine every gene's level of RNA expression and provide information about splice variants, biomarkers and chimeric RNAs. Biologists and clinicians will be able to more readily use this data to compare the RNA profiles or "transcriptomes" of normal cells with those of individual cancers and thereby be in a better position to develop optimized personal therapies.

Personalized approaches to cancer medicine are already in widespread use for a few "cancer biomarkers" including variants of the BRAC 1 gene that can be used to identify women with a high risk of developing breast and ovarian cancer.

"Our goal was to design a pipeline that is easily installable with parallel processing capabilities," said Mittal. "R-SAP can make 100 million reads in just 90 minutes. Running the program simultaneously on multiple CPUs can further decrease that time."

R-SAP is open source software, freely accessible at the McDonald Lab website*.

"This is another example of Georgia Tech's ability to merge computer technology with science to create an essential feature of next-generation bioinformatics tools," said McDonald. "We hope that R-SAP will be a useful and user-friendly instrument for scientists and clinicians in the field of cancer biology."

Additional References Citations Article adapted by Medical News Today from original press release. Click 'references' tab above for source.
Visit our cancer / oncology section for the latest news on this subject. *http://www.mcdonaldlab.biology.gatech.edu/r-sap.htm
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Visitor Opinions In Chronological Order (1)Molecular Cancer Medicine is Still Waiting for its First Killer "App"posted by Gregory D. Pawelski on 15 Feb 2012 at 1:39 pm

One of the high-paying glamorous jobs of the new millenium was bioinformatics. Some statisticians have said that we're going to cure diseases in the near time frame. Those same statisticians reacted that we didn't know how to handle one gene (and they still don't), never mind 23,000 genes or the millions of other pieces that help regulate the production of proteins.

In molecular cancer medicine, as it involves drug selection, things are not nearly so clear cut as they appear. Biological technology seems so elegant and beautiful, but a beautiful biological technology is no different than a beautiful computer technology, it's not worth much without some very good applications ("apps"), and personalized molecular medicine is still waiting for its first killer app.

Informatics and computer technology can do much to improve patients care. However, while this bottom-up approach generates data that may be much more quantitative, we will still have to show that it can have an impact from the top-down.

The top-down approach in systems biology focuses on addressing questions that need to be answered and optimizing the path to their solution. In other words, top-down research (i.e. functional profiling) has important implications for understanding human disease.

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