“A major bottleneck in the realisation of the dream of personalised medicine is no longer technological. It is computational,” said Doctor John McDonald, director of Georgia Tech’s newly created Integrated Cancer Research Centre. “R-SAP follows a hierarchical decision-making procedure to accurately characterise 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 optimised personal therapies.
Personalised 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 Vinay Mittal of Georgia Tech. “R-SAP can make 100 million reads in just 90 minutes. Running the program simultaneously on multiple CPUs can further decrease that time.”
“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.”
MEDICA.de; Source: Georgia Tech