Release date: 2014-10-28
Genomics is setting off a data storm, not only the innovation of sequencing platforms, but also the upsurge of genetic mutation and disease association research. The Global Genome and Health Alliance (composed of Google, BGI, the Canadian Genome Research Center, the National Institutes of Health, and the Wellcome Genealogy) is developing a standard form of data sharing. They believe that these efforts will lead innovation in data exploration and analysis.
This year, the American Society of Human Genetics (ASHG) spokesperson said: Genomics has a promising future in the field of personalized medicine, and the transformation of genomics into medical applications relies heavily on big data analysis.
Now more and more people are sequencing the genome, so Google's David Glazer said at the conference: "How should we apply big data transformation to various fields?"
Genomics is setting off a data storm, not only the innovation of sequencing platforms, but also the upsurge of genetic mutation and disease association research. At the same time, some researchers are working to build a standard to facilitate the sharing of clinical and omics data.
IBM's Ajay Royyuru pointed out at the meeting that about 6,000 to 10,000 cancer-related articles were published each year. With so many articles, even if researchers or doctors follow up in time, they can't complete such a large reading. Royyuru believes that "this problem needs to be solved." He said that the solution to this problem lies in the comprehensive and objective and rapid processing of these published studies. In addition, he also stated that a causal link must be clearly stated. He and his colleagues at IBM are using large computers to process these articles, summarizing the findings in the article and the relationship with the disease.
Through the precision tumor research process developed by Royyuru and colleagues, the patient's sequencing data will be transmitted to the Watson system, and the data will be compared with the PubMed, National Cancer Center exchange database, DrugBank and other databases. By comparison, the Watson system outputs a conceptual model of the disease and gives some treatment options. The Watson system also provides a selection of medical options that can be used as a reference for expert workshops. Royyuru said that the entire analysis process plus output report takes about 10 minutes. In addition, Royyuru said that the Watson system can also record the medical plan chosen by the patient and the patient's clinical response to the program.
IBM is currently taking this process into a prototype, working with the New York Genome Center and planning to test it next year.
In addition to the Watson system, other data technology processing techniques supported by computer science can also be used to analyze genomic data.
Glazer pointed out that Google is very experienced in big data processing. For example, YouTube updates 100 hours of video per minute, and the number of Gmail users is 150 times that of American doctors. Glazer and his colleagues used 1000 genomic data to test their data analysis systems (like the Dremel and BigQuery systems). The first step in analyzing the 1000 data is to build a matrix that takes up 60 eight-core computers and takes 2 hours.
Glazer said that constantly discovering the problems in research is the only way to achieve innovation. In addition, he said that the personalized medical application of genomics needs to establish a standard from the current "manualization" to "factory" transformation. The Global Genome and Health Alliance (composed of Google, BGI, the Canadian Genome Research Center, the National Institutes of Health, and the Wellcome Genealogy) is developing a standard form of data sharing. Glazer believes that these efforts will lead to innovation in data exploration and analysis.
Source: Shell Society
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