Center for Critical Assessment of Genome Interpretation

基因组解释批判性评估中心

• 批准号: 10455661
• 负责人: Steven E Brenner
• 金额: $ 75.3万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-13 至 2025-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10455661
• 关键词: Address; Adoption; Affect; Amino Acid Sequence; Artificial Intelligence; Basic Science; Blinded; Characteristics; Clinical; Communities; Computing Methodologies; Copy Number Polymorphism; Data; Data Set; Development; Disease; Educational workshop; Ensure; Environment; Ethics; Evaluation; Evolution; Foundations; Genetic; Genetic Variation; Genome; Health; Human Genetics; Infrastructure; International; Knowledge; Machine Learning; Malignant Neoplasms; Methods; Modeling; Molecular; Nucleotides; Participant; Performance; Pharmaceutical Preparations; Pharmacogenomics; Phenotype; Privacy; Provider; Publications; RNA Splicing; Rare Diseases; Secure; Structure; Trust; Variant; Work; base; clinical application; clinical practice; computerized tools; data acquisition; data dissemination; exome; experimental study; genetic information; genetic variant; genomic data; genomic variation; high standard; human disease; innovation; meetings; multiple omics; operation; outreach; response; symposium; trait; whole genome

Genomic data hold the promise of revolutionizing our understanding and treatment of human disease. Multiple barriers stand between the acquisition of the data and realizing these and other benefits. Rapid accumulation of genomic data far exceeds our capacity to reliably interpret genomic variation. New developments in artificial intelligence and machine learning, combined with increased computing power and domain knowledge, provide hope for the deployment of enhanced computational tools in both basic research and clinical practice. Use of these methods critically depends upon reliable characterization of their performance. The Center for Critical Assessment of Genome Interpretation (C-CAGI) will address these needs, through objective evaluation of the state of the art in relating human genetic variation and health. CAGI has had five editions since 2010 with 50 challenges posed to the community taken on by hundreds of predictors, leading to scores of publications about prediction methods and their assessment. We propose for C-CAGI to continue to advance the field of variant interpretation through the following Specific Aims: 1. Develop community experiments to evaluate the quality of computational methods for interpreting genomic variation data. C-CAGI will conduct community experiments in which participants make bona fide blinded predictions of disease related phenotypes on the basis of genomic data. We will engage a diverse predictor community to spur innovation. The CAGI Ethics Forum will vet studies to ensure that privacy and sharing maintain the highest standards and will educate the community. 2. Assess the quality of current computational methods for interpreting genomic variation data; highlight innovations and progress at interactive conferences. Predictions will be evaluated by independent assessors, who will be supported by new assessment approaches from C-CAGI. Results will be presented at CAGI experiment conferences with deep technical engagement, which will be interleaved with reflective CAGIâ meetings that create an environment for a comprehensive evaluation of the field, facilitating identification of major bottlenecks and problems faced by the current genome interpretation approaches. 3. Broadly disseminate the results and conclusions from the CAGI experiments and analysis. C-CAGI will outreach to the broader scientific and clinical community through its publications, and the creation of a calibrated reference integrated into the most common workflows for ready adoption. CAGI will also be represented at international meetings with presentations and workshops. 4. Operate effectively and responsively. C-CAGI will operate efficiently as it closely interacts with hundreds of participants. CAGI will build upon a robust information infrastructure that securely facilitates data dissemination, prediction submission, and assessment.

基因组数据有望彻底改变我们对人类疾病的理解和治疗。多 在获取数据与实现这些和其他利益之间存在障碍。快速积累 基因组数据的数量远远超过了我们可靠地解释基因组变异的能力。人工智能的新发展 智能和机器学习，再加上不断增强的计算能力和领域知识， 希望在基础研究和临床实践中部署增强的计算工具。使用 这些方法关键地依赖于其性能的可靠表征。 基因组解读关键评估中心（C-CAGI）将通过以下方式满足这些需求： 客观评价人类遗传变异与健康的关系。CAGI已经有五个 自2010年以来的版本，数百个预测者对社区提出了50个挑战，导致 许多关于预测方法及其评估的出版物。我们建议C-CAGI继续 通过以下具体目标推进变体解释领域： 1.开展社区实验，评估口译计算方法的质量 基因组变异数据。C-CAGI将进行社区实验，参与者将真诚地 基于基因组数据的疾病相关表型的盲法预测。我们将采取多种多样的 预测社区以促进创新。CAGI伦理论坛将审查研究，以确保隐私和 分享可以维持最高的标准，并将教育社区。 2.评估当前用于解释基因组变异数据的计算方法的质量; 在互动会议上突出创新和进步。预测将由以下人员进行评估 独立评估员，他们将得到C-CAGI新评估方法的支持。结果将 在CAGI实验会议上提出，深入的技术参与，这将与 反思性CAGI会议，为实地综合评估创造环境， 确定当前基因组解释方法面临的主要瓶颈和问题。 3.广泛传播CAGI实验和分析的结果和结论。C-CAGI 将通过其出版物和创建一个 校准参考集成到最常见的工作流程中，以便随时采用。CAGI还将在 派代表参加国际会议，作介绍和举办讲习班。 4.有效地运作和响应。C-CAGI将有效运作，因为它与数百个 的参与者。CAGI将建立在一个强大的信息基础设施，安全地促进数据 传播、预测提交和评估。

项目成果

Determination of disease phenotypes and pathogenic variants from exome sequence data in the CAGI 4 gene panel challenge.

在CAGI 4基因面板挑战中，从外显子组序列数据中确定疾病表型和致病变异。

• DOI: 10.1002/humu.23249
• 发表时间: 2017-09
• 期刊: Human mutation
• 影响因子: 3.9
• 作者: Kundu K;Pal LR;Yin Y;Moult J
• 通讯作者: Moult J

Precision Medicine: Addressing the Challenges of Sharing, Analysis, and Privacy at Scale.

精准医学：应对大规模共享、分析和隐私的挑战。

• 发表时间: 2020
• 期刊: Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
• 作者: Brenner,StevenE;Bulyk,MarthaL;Crawford,DanaC;Morgan,AlexanderA;Radivojac,Predrag;Tatonetti,NicholasP
• 通讯作者: Tatonetti,NicholasP

CAGI6 ID-Challenge: Assessment of phenotype and variant predictions in 415 children with Neurodevelopmental Disorders (NDDs).

CAGI6 ID-Challenge：评估 415 名患有神经发育障碍 (NDD) 的儿童的表型和变异预测。

• DOI: 10.21203/rs.3.rs-3209168/v1
• 发表时间: 2023
• 期刊: Research square
• 作者: Aspromonte,MariaCristina;Conte,AlessioDel;Zhu,Shaowen;Tan,Wuwei;Shen,Yang;Zhang,Yexian;Li,Qi;Wang,MaggieHaitian;Babbi,Giulia;Bovo,Samuele;Martelli,PierLuigi;Casadio,Rita;Althagafi,Azza;Toonsi,Sumyyah;Kulmanov,Maxat;Hoehnd
• 通讯作者: Hoehnd

Working toward precision medicine: Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges.

• DOI: 10.1002/humu.23280
• 发表时间: 2017-09
• 期刊: Human mutation
• 影响因子: 3.9
• 作者: Daneshjou R;Wang Y;Bromberg Y;Bovo S;Martelli PL;Babbi G;Lena PD;Casadio R;Edwards M;Gifford D;Jones DT;Sundaram L;Bhat RR;Li X;Pal LR;Kundu K;Yin Y;Moult J;Jiang Y;Pejaver V;Pagel KA;Li B;Mooney SD;Radivojac P;Shah S;Carraro M;Gasparini A;Leonardi E;Giollo M;Ferrari C;Tosatto SCE;Bachar E;Azaria JR;Ofran Y;Unger R;Niroula A;Vihinen M;Chang B;Wang MH;Franke A;Petersen BS;Pirooznia M;Zandi P;McCombie R;Potash JB;Altman RB;Klein TE;Hoskins RA;Repo S;Brenner SE;Morgan AA
• 通讯作者: Morgan AA

Predicting gene expression in massively parallel reporter assays: A comparative study.

• DOI: 10.1002/humu.23197
• 发表时间: 2017-09
• 期刊: Human mutation
• 影响因子: 3.9
• 作者: Kreimer A;Zeng H;Edwards MD;Guo Y;Tian K;Shin S;Welch R;Wainberg M;Mohan R;Sinnott-Armstrong NA;Li Y;Eraslan G;Amin TB;Tewhey R;Sabeti PC;Goke J;Mueller NS;Kellis M;Kundaje A;Beer MA;Keles S;Gifford DK;Yosef N
• 通讯作者: Yosef N