Web tools for physician-driven diagnostic interpretation of genomic patient data

用于医生驱动的基因组患者数据诊断解释的网络工具

• 批准号: 9376874
• 负责人: Gabor T Marth
• 金额: $ 71.89万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9376874
• 关键词: Address; Architecture; Area; Bioinformatics; Clinic; Clinical; Clinical Investigator; Communities; Computer Analysis; Computer software; Data; Data Analyses; Data Collection; Data Quality; Data Set; Development; Diagnosis; Diagnostic; Disease; Dropout; Ensure; Environment; Etiology; Exons; Failure; Family Relationship; Feedback; Gender; Genes; Genetic; Genome; Genomic medicine; Genomics; Healthcare; Hereditary Disease; Hospitals; Inheritance Patterns; Inherited; Institution; Internet; Intuition; Investigation; Knowledge; Libraries; Medical Research; Outcome; Pathologist; Patient Data Privacy; Patients; Physicians; Process; Protocols documentation; Quality Control; Recommendation; Reporting; Research; Sampling; Secure; Security; Sequence Alignment; Software Tools; System; Technology; Time; Training; Transcript; Variant; Visual; Work; base; clinical Diagnosis; clinical diagnostics; clinical phenotype; cost; design; disease diagnosis; exome; exome sequencing; firewall; flexibility; genetic disorder diagnosis; genetic variant; genome sequencing; genomic data; improved; improved functioning; novel; patient privacy; point of care; precision medicine; prevent; skills; success; tool; user-friendly; web app

SUMMARY/ABSTRACT Genomic sequencing provides definitive disease diagnoses for many patients with suspected genetic disease, ending or preventing lengthy and costly diagnostic odysseys. However, despite extensive efforts of research clinicians and all current computational analysis technologies, the genetic cause of disease remains unresolved for over half of the sequenced patients in genetics clinics today. All too often, diagnosis from whole- exome or genome sequencing data remains elusive even for patients suffering from diseases with well- understood clinical presentation and genetic architecture. Although diagnostic failure can have multiple causes, we hypothesize that two reasons contribute significantly. First, current variant prioritization tools work by reductive filtering on annotations and inheritance patterns to reduce sets of exonic or genomic variants to small, prioritized lists of candidates. This approach works when clear causative variants are present, but offers minimal capacity to remove highly ranked but false positive candidates, and provides little guidance when causative variants have been missed, typically because of unrecognized data quality problems such as low sequence coverage or exon dropouts. When the first round of analysis yields no plausible candidates, current tools don't have the ability to suggest a sensible “next step”, e.g. to deepen or expand the search for causative variants in the data, and the result is analysis dead-end. Second, because of onerous IT expertise and bioinformatic skill requirements, physicians currently rely on bioinformatics experts to analyze genomic data. However, the bioinformatician does not possess the physician's clinical expertise or detailed knowledge of disease presentation, clinical phenotype, and time course of the disease, all of which can be critical in making a diagnosis. This gap between clinical and computational expertise hinders diagnostic success and disease discovery. Here we propose to build a set of web tools that offer novel functionality for deeper, systematic re- examination of the data for disease-causing variants, but are also intuitive and easy to use so clinicians can themselves analyze their patients' genomic datasets. These tools will be based on our already popular IOBIO system available at http://iobio.io, and will offer diagnostic analysts the ability to rapidly examine the quality of their genomic datasets, and visually and in real time search the patient's data for disease causing variants. A unique aspect of this development is that it will be physician-driven from the outset: a large team of clinical Investigators will help design, prioritize, and evaluate software features, and integrate the tools into physician practice and training, ensuring these tools will be usable by clinicians, and they address the most relevant analysis steps for successful clinical diagnosis. Our tools and training materials will be made widely available, drastically lowering the barrier to participation in genomic data analysis for all clinicians who can benefit from genomic data of their patients, helping genomic sequencing to reach its potential as a means to make definitive clinical diagnoses.

摘要/摘要 基因组测序为许多疑似遗传病的患者提供了明确的疾病诊断， 结束或阻止漫长且昂贵的诊断过程然而，尽管进行了大量的研究工作。 Fortress 和所有当前的计算分析技术，疾病的遗传原因仍然存在 如今，在遗传学诊所中，超过一半的测序患者都没有得到解决，而诊断往往是从整体上进行的。 即使对于患有健康状况良好的疾病的患者来说，外显子组或基因组测序数据仍然难以捉摸。 了解临床表现和遗传结构 尽管诊断失败可能有多种原因， 我们认为有两个重要原因：首先，当前的变体优先排序工具的工作原理是： 对注释和遗传模式进行还原过滤，以减少外显子或基因组变异集 当存在明确的致病变异时，这种方法会起作用，但会提供少量的、优先考虑的候选者列表。 删除排名靠前但误报的候选者的能力极低，并且在以下情况下提供很少的指导： 致病变异被遗漏，通常是由于未识别的数据质量问题，例如低 当第一轮分析没有产生合理的候选序列时，当前的序列覆盖或外显子丢失。 工具无法建议合理的“下一步”，例如加深或扩大对因果关系的搜索。 数据的变异，导致分析陷入死胡同；其次，由于 IT 专业知识繁琐。 生物信息学技能要求，医生目前依靠生物信息学专家来分析基因组数据。 然而，生物信息学家不具备医生的临床专业知识或详细的知识 疾病表现、临床表型和疾病的时间进程，所有这些对于做出决定都至关重要。 临床和计算专业知识之间的差距阻碍了诊断的成功和疾病。 在这里，我们建议构建一套网络工具，为更深入、系统的重新发现提供新颖的功能。 检查数据中是否存在致病变异，但也直观且易于使用，因此忠诚度可以 这些工具将基于我们已经流行的 IOBIO。 系统可在 http://iobio.io 上找到，并将为诊断分析师提供快速检查质量的能力 他们的基因组数据集，并直观地实时搜索患者的数据以查找疾病变异 A。 这一发展的一个独特之处在于，它将从一开始就由医生驱动：一个庞大的临床团队 研究人员将帮助设计、确定优先级和评估软件功能，并将这些工具集成到医生中 练习和培训，确保这些工具可供上校使用，并且它们解决了最相关的问题 我们的工具和培训材料将被广泛使用，以实现成功的临床诊断。 大大降低了所有受益者参与基因组数据分析的障碍 患者的基因组数据，帮助基因组测序发挥其作为确定诊断手段的潜力 临床诊断。