{"108801":{"#nid":"108801","#data":{"type":"news","title":"Georgia Tech Develops Computational Algorithm to Assist in Cancer Treatments","body":[{"value":"\u003Cp\u003EHigh-throughput DNA sequencing technologies are leading to\na revolution in how clinicians diagnose and treat cancer. The molecular\nprofiles of individual tumors are beginning to be used in the design of\nchemotherapeutic programs optimized for the treatment of individual patients. The\nreal revolution, however, is coming with the emerging capability to\ninexpensively and accurately sequence the entire genome of cancers, allowing\nfor the identification of specific mutations responsible for the disease in\nindividual patients.\u003C\/p\u003E\n\n\u003Cp\u003EThere is only one downside. Those sequencing technologies\nprovide massive amounts of data that are not easily processed and translated by\nscientists. That\u2019s why Georgia Tech has created a new data analysis algorithm\nthat quickly transforms complex RNA sequence data into usable content for\nbiologists and clinicians. The RNA-Seq analysis pipeline (R-SAP) was developed\nby School of Biology Professor John McDonald and Ph.D. Bioinformatics candidate\nVinay Mittal. Details of the pipeline are published in the journal \u003Ca href=\u0022http:\/\/nar.oxfordjournals.org\/cgi\/reprint\/gks047?%20ijkey=Fd2USew6iX9nbaM\u0026amp;keytype=ref\u0022\u003ENucleic\nAcids Research\u003C\/a\u003E. \n\n\u003C\/p\u003E\u003Cp\u003E\u201cA major bottleneck in the realization of the dream of\npersonalized medicine is no longer technological. It\u2019s computational,\u201d said\nMcDonald, director of Georgia Tech\u2019s newly created Integrated Cancer Research\nCenter. \u201cR-SAP follows a hierarchical decision-making procedure to accurately characterize\nvarious classes of gene transcripts in cancer samples.\u201d \n\n\u003C\/p\u003E\u003Cp\u003EThere are at least 23,000 pieces of RNA in the human\ngenome that encode the sequence of proteins. Millions of other pieces help\nregulate the production of proteins. R-SAP is able to quickly determine every\ngene\u2019s level of RNA expression and provide information about splice variants,\nbiomarkers and chimeric RNAs. Biologists and clinicians will be able to more\nreadily use this data to compare the RNA profiles or \u201ctranscriptomes\u201d of normal\ncells with those of individual cancers and thereby be in a better position to\ndevelop optimized personal therapies. \n\n\u003C\/p\u003E\u003Cp\u003EPersonalized approaches to cancer medicine are already in\nwidespread use for a few \u201ccancer biomarkers\u201d including variants of the BRAC 1\ngene that can be used to identify women with a high risk of developing breast\nand ovarian cancer. \n\n\u003C\/p\u003E\u003Cp\u003E\u201cOur goal was to design a pipeline that is easily\ninstallable with parallel processing capabilities,\u201d said Mittal. \u201cR-SAP can\nmake 100 million reads in just 90 minutes. Running the program simultaneously\non multiple CPUs can further decrease that time.\u201d\n\n\u003C\/p\u003E\u003Cp\u003ER-SAP is open source software, freely accessible at the\nMcDonald Lab \u003Ca href=\u0022http:\/\/www.mcdonaldlab.biology.gatech.edu\/r-sap.htm\u0022\u003Ewebsite\u003C\/a\u003E.\n\n\n\u003C\/p\u003E\u003Cp\u003E\u201cThis is another example of Georgia Tech\u2019s ability to\nmerge computer technology with science to create an essential feature of\nnext-generation bioinformatics tools,\u201d said McDonald. \u201cWe hope that R-SAP will\nbe a useful and user-friendly instrument for scientists and clinicians in the\nfield of cancer biology.\u201d \n\n\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":[{"value":"New software key for personalized cancer medicine"}],"field_summary":[{"value":"\u003Cp\u003EGeorgia Tech has created a new data analysis algorithm that quickly \ntransforms complex RNA sequence data into usable content for biologists \nand clinicians. Scientists will be able to more readily use this data to\n compare the RNA profiles or \u201ctranscriptomes\u201d of normal cells with those\n of individual cancers and thereby be in a better position to develop \noptimized personal therapies.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Georgia Tech has created a new data analysis algorithm that quickly transforms complex RNA sequence data into usable content for cancer biologists and clinicians."}],"uid":"27560","created_gmt":"2012-02-13 13:30:19","changed_gmt":"2016-10-08 03:11:40","author":"Jason Maderer","boilerplate_text":"","field_publication":"","field_article_url":"","dateline":{"date":"2012-02-13T00:00:00-05:00","iso_date":"2012-02-13T00:00:00-05:00","tz":"America\/New_York"},"extras":[],"hg_media":{"101211":{"id":"101211","type":"image","title":"John McDonald","body":null,"created":"1449178159","gmt_created":"2015-12-03 21:29:19","changed":"1475894717","gmt_changed":"2016-10-08 02:45:17"}},"media_ids":["101211"],"related_links":[{"url":"http:\/\/www.cos.gatech.edu\/","title":"College of Sciences"},{"url":"http:\/\/www.biology.gatech.edu\/","title":"School of Biology"}],"groups":[{"id":"1183","name":"Home"}],"categories":[{"id":"140","name":"Cancer Research"}],"keywords":[{"id":"2546","name":"bioinformatics"},{"id":"4896","name":"College of Sciences"},{"id":"2371","name":"John McDonald"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EJason Maderer\u003Cbr \/\u003EGeorgia Tech Media Relations\u003Cbr \/\u003E404-385-2966\u003Cbr \/\u003E\u003Ca href=\u0022mailto:maderer@gatech.edu\u0022\u003Emaderer@gatech.edu\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":["maderer@gatech.edu"],"slides":[],"orientation":[],"userdata":""}}}