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A Community Driven Framework for Genome Based Clinical Diagnostics

A Community Driven Framework for Genome Based Clinical Diagnostics
基于基因组的临床诊断的社区驱动框架
批准号:
9293355
负责人:
Karen Louise Eilbeck
金额:
$61.26万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2015
资助国家:
美国
项目状态:
已结题
起止时间:
2015-09-21 至 2020-06-30

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中文摘要
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英文摘要
 DESCRIPTION (provided by applicant): Personal genome sequences from next generation sequencing technologies are permeating clinical care via diagnostic testing. Clinical labs are under pressure to handle this data in unambiguous, reproducible ways, and provide interpretations that are comparable across testing sites, but face many difficulties to do so. Over the last several years the 'variant file', has emerged as the common currency for exchange and analyses of personal genome sequences for research, and now clinical purposes. These files describe every position in a personal genome that differs from the reference GenBank genome sequence. Given their widespread use, the variant file is a logical starting point for designing a format suitable for clinical applications. Currently variant file styles are widely divergent, ther is not uniformity in the way that complex variants are annotated and genomics has not embraced the use of medical data standards. The diagnostic genomics community, aware of these issues, has mobilized a working group to provide recommendations and requirements to unify variant annotation, to improve public health and clinical applications. For example, without clear standards for capturing variant sequence data, sharing data for the following applications is hindered: across laboratories for quality assurance, with databases for making an interpretation, with a patient record for future use. This proposal addresses the problems with polymorphic variant description by providing novel algorithms to define sequence variants and by developing file formats and software to communicate this information, using guidance from the clinical diagnostic community. The standardized format, VCFclin, and co-developed software tools will end information loss and ambiguity as genomics data flow from sequencing machines, through variant calling and analysis pipelines to interpretation and clinical use.
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University of Utah Interdisciplinary Training Program in Computational Approaches to Diabetes and Metabolism Research
  • 批准号:
    9930879
  • 项目类别:
  • 资助金额:
    $2.46万
  • 财政年份:
    2016
  • 负责人:
    Karen Louise Eilbeck
  • 依托单位:
University of Utah Interdisciplinary Training Program in Computational Approaches to Diabetes and Metabolism Research
  • 批准号:
    10172496
  • 项目类别:
  • 资助金额:
    $26.1万
  • 财政年份:
    2016
  • 负责人:
    Karen Louise Eilbeck
  • 依托单位:
University of Utah Interdisciplinary Training Program in Computational Approaches to Diabetes and Metabolism Research
  • 批准号:
    10438611
  • 项目类别:
  • 资助金额:
    $24.89万
  • 财政年份:
    2016
  • 负责人:
    Karen Louise Eilbeck
  • 依托单位:
University of Utah Interdisciplinary Training Program in Computational Approaches to Diabetes and Metabolism Research
  • 批准号:
    10654554
  • 项目类别:
  • 资助金额:
    $29.2万
  • 财政年份:
    2016
  • 负责人:
    Karen Louise Eilbeck
  • 依托单位:
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