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SYNOPSIS: With each successive discovery in genetics, the true dynamic complexity of the genome has become increasingly apparent, requiring relatively consistent updates to the technical definition of the word "gene." It is now understood that the majority of human genes produce multiple functional products, or isoforms, primarily through alternative transcription and alternative splicing. Different isoforms within the same gene have been shown to participate in different functional pathways, and the altered expression of specific isoforms have been associated with numerous diseases. While the recent advances in NGS are facilitating the goal of studying gene regulation at isoform-level, there are a number of informatics challenges and difficulties that need to be addressed to improve the current state and fulfill the promise of studying gene regulation at gene isoform-level. Dr. Davuluri will present some of the recent approaches developed by our group, with an emphasis on how those methods have led to the development of a diagnostic assay for molecular sub-typing of cancer patients. In particular, he will challenge the use of basic gene-centric approaches in cancer genomics and argue that one should go beyond simple gene-based analyses but also consider isoform-level information that include gene expression/regulation of splice-variants. Looking forward, Dr. Davuluri will discuss the integrative application of different statistical and data-mining approaches to derive platform-independent classification models for identification of isoform-level gene signatures for cancer subtyping.
Semantic MEDLINE integrates information retrieval, advanced natural language processing, automatic summarization, and visualization into a single Web portal. The application is intended to help manage the results of PubMed searches by condensing core semantic content in the citations retrieved. Output is presented as a connected interactive graph of semantic relations, with links to the original MEDLINE citations.
The ability to manipulate salient information across documents helps users keep up with the research literature and discover connections which might otherwise go unnoticed. Such an ability can have an impact on biomedicine by supporting scientific research. Researchers can use Semantic MEDLINE to implement the literature-based discovery methodology for hypothesis generation; in addition, they can use the discovery browsing paradigm to elucidate poorly understood biomedical topics.
In this talk, Mr. Madduri will describe Globus Genomics, a system that was developed for rapid analysis of large quantities of next-generation sequencing (NGS) genomic data. This system is notable for its high degree of end-to-end automation, which encompasses every stage of the data analysis pipeline from initial data access (from remote sequencing center or database, by the Globus file transfer system) to on-demand resource acquisition by a specialized elastic provisioner (on Amazon EC2); specification, configuration, and reuse of multi-step processing pipelines (via the Galaxy workflow system); creation of custom Amazon Machine Images; and efficient scheduling of these pipelines over many processors (via the HTCondor scheduler). The system allows biomedical researchers to perform rapid analysis of large NGS datasets using just a web browser in a fully automated manner, without software installation or a need for any local computing infrastructure.
In the era of big data, effective use of increasingly larger, complex, and diverse datasets has become a critical challenge for healthcare transformation. To meet the challenge, the scientific community must deliver innovative and scalable frameworks for interpreting the influx of information to keep pace with rapid scientific developments. The mission of a national lab is to enable scientific innovations and transformative technical breakthroughs for grand challenges by leveraging unique resources. ORNL is taking on this "Big Data to Knowledge" challenge for health innovations via its Health Data Sciences Institute (HDSI). In this presentation Dr. Tourassi will discuss informatics innovations coordinated by the institute to expand and accelerate biomedical knowledge discovery. Dr. Tourassi will illustrate the value of these innovations with two cancer-related examples from precision medicine and population health. The first example will demonstrate how linking of heterogeneous information across The Cancer Genome Atlas (TCGA) can provide novel insights into cancer-specific mutations at the individual level that can then directly inform molecular epidemiology of specific tumor states. The second example will demonstrate the use of cyber-informatics to accelerate discoveries in environmental cancer epidemiology. Underlying the two applications is a powerful semantic reasoning framework built at ORNL that enables seamless hypotheses generation for exploratory research.
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