CRCNS: Computational Approach to Assess Replicability of Neurobehavior Phenotypes

CRCNS：评估神经行为表型可重复性的计算方法

• 批准号: 9471546
• 负责人: MOLLY A BOGUE
• 金额: $ 25.23万
• 依托单位: JACKSON LABORATORY
• 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 2020-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9471546
• 关键词: Address; Adopted; Affect; Algorithmic Software; Analysis of Variance; Animal Disease Models; Behavior; Behavioral; Biochemistry; Bioinformatics; Biological Sciences; Biometry; Categories; Code; Collaborations; Color; Communities; Complex; Computational Science; Computer software; Confusion; Coupled; Data; Databases; Diagnostic; Discipline; Doctor of Philosophy; Ensure; Environment; Faculty; Failure; Future; Genes; Genetic; Genetically Engineered Mouse; Genotype; Head; Health; Human; Institution; International; Investments; Israel; Journals; Knowledge; Laboratories; Laboratory mice; Left; Maine; Measures; Medicine; Methods; Modeling; Molecular Genetics; Mollies; Mouse Strains; Mus; Nature; Neurobiology; Neurologic; Neurosciences; Online Systems; Operations Research; Phenotype; Policies; Positioning Attribute; Problem Solving; Process; Proteins; Protocols documentation; Published Comment; Publishing; Reagent; Recommendation; Reproducibility; Research; Research Personnel; Research Proposals; Resources; Schools; Science; Scientist; Standardization; Testing; The Jackson Laboratory; Therapeutic; Time; Training; Training Programs; United States National Institutes of Health; Universities; Validation; Work; Zoology; base; behavior measurement; behavioral study; computational neuroscience; cost; cost effective; data integration; data sharing; experience; experimental study; gaze; gene function; genetic strain; improved; mammalian genome; mathematical sciences; mutant; neurobehavior; neurobehavioral; neurobehavioral disorder; phenome; phenotypic data; pre-clinical research; prevent; programs; relating to nervous system; statistics; tool; user-friendly; web app; web interface

PROJECT DESCRIPTION (as submitted to NSF) CRCNS: US - Israel Data Sharing Proposal: Computational Approaches to Assess Replicability of Neurobehavioral Phenotypes Molly Bogue, Ph.D. (PI) Yoav Benjamini, Ph.D. (PI) The Jackson Laboratory Department of Statistics and Operations Research Bar Harbor, Maine USA School of Mathematical Sciences Sagol School ofNeuroscience Tel Aviv University ISRAEL Ilan Golani, Ph.D. (Co-Inv) Department of Zoology Wise Faculty of Life Sciences Sago! School of Neuroscience Tel Aviv University ISRAEL Iliana Oozes, Ph.D. (Co-Inv) Dept. of Human Molecular Genetics and Biochemistry Saclder Faculty of Medicine Sago! School of Neuroscience Tel Aviv University ISRAEL Neri Kaflcafi, Ph.D. (Co-Inv) Department of Statistics and Operations Research School of Mathematical Sciences Tel Aviv University ISRAEL Prior NSF and/or CRCNS Support: None 35 Introduction We are submitting a revised computational neuroscience proposal. Our original proposal received positive comments from reviewers last year under the Research Proposal mechanism. For example: "All reviewers were very enthusiastic about the transformative potential of solving the problem being addressed in the proposal, and were also enthusiastic about the basic framework underlying the proposed solution." However, previous reviewers thought our proposal was better aligned with the Data Sharing mechanism. Unfortunately, NSF-BSF did not support Data Sharing last year. This year it does and so we are reapplying under the Data Sharing mechanism. We have revised and improved our application to address previous reviewers' concerns. Their concerns can be grouped in four categories: 1) difficulty in acquiring new data, 2) challenge in dealing with sparse data, 3) lack of specific details about our approach, and 4) misconception that The Jackson Laboratory is a for-profit organization and that this would affect data release. JAX is a not-for-profit research institution and is a world leader in providing public bioinformatics resources for the laboratory mouse without any restrictions. Background and Significance Concernsaboutreplicabilityofneurobehavioralphenotypes The scientific and lay communities have become increasingly concerned with published "discoveries" that are not replicable [10-12]. Prominent institutions and journals, including NIH [13], Science [14] and Nature [15], have recently announced policy positions on the subject, yet there is still confusion and debate regarding how the problem should be addressed. While documented in all scientific fields, the problem was specifically noted in preclinical research [16] including mouse neurological and behavioral studies [7]. Note: To prevent confusion, we use the term "replicability" for replicating results in other studies and "reproducibility" for reproducing conclusions within the same study [17, 18]. ANOVA Variance Replicable Prnportlon of total variance G* L ns Yes 0% 100% Gxl ns G* L * Yes 0% 100% Gxl ns G* L * Yes 0'1/o 100% Gxl ns G ns L * No 0% 100% Gxl * g1 g2 Figure 1. Illustration of the significance of the main effects of Genotype (G), Laboratory (L) and the Genotype x Laboratory (GxL) interaction. Data for four different behavioral measures are depicted in the left panel across three laboratories (color coded) for two genotypes (g). ANOVA results for each are shown where *=significant; ns=not significant. Proportion of total variance (%) is illustrated by color-coded horizontal bars. Replicability, based on the significance of GxL, is indicated on the right for each measure. Measuring quantitative phenotypes of genetically engineered mouse strains has become a central strategy for discovering mammalian gene function, and for characterizing animal models of disease in which putative cures can be researched (for reviews see [19, 20]). The International Mouse Phenotyping Consortium (IMPC) [21, 22] coordinates an international effort to phenotype thousands of mutant strains, eventually achieving a functional annotation of most of the ~20,000 protein-coding genes in the mammalian genome. An essential aspect of this work is making high- throughput phenotyping data accessible to the scientific community in public databases [23, 24]. The utility of this undertaking, however, critically depends on the ability to replicate phenotyping results in other laboratories. This large project is but one example of the general need for replicability of mouse phenotyping, which has been repeatedly raised and discussed (e.g., [27 25 ,7] with no satisfactory solution yet adopted. Any solution that is likely to be adopted by experimentalists for 36

项目描述 （提交给 NSF） CRCNS：美国-以色列数据共享提案： 计算方法 评估神经行为表型的可重复性 莫莉·博格博士(PI) Yoav Benjamini 博士(PI) 杰克逊实验室统计与运筹学系 美国缅因州巴港数学科学学院 萨戈尔神经科学学院 以色列特拉维夫大学 伊兰·戈拉尼博士（联合投资） 动物学系 怀斯生命科学学院 西米！神经科学学院 以色列特拉维夫大学 伊利亚娜·奥泽斯博士（联合投资） 人类分子遗传学与生物化学系 萨克德医学院 西米！神经科学学院 以色列特拉维夫大学 内里·卡夫卡菲博士（联合投资） 统计与运筹学系 数学科学学院 以色列特拉维夫大学 先前 NSF 和/或 CRCNS 支持：无 35 介绍 我们正在提交修订后的计算神经科学提案。我们最初的提议得到了积极的回应 去年在研究提案机制下审稿人的意见。例如：“所有 审稿人对解决问题的变革潜力非常热衷 该提案中提到了这一点，并且对拟议提案所依据的基本框架也充满热情 解决方案。”然而，之前的审阅者认为我们的提案更符合数据共享 机制。不幸的是，NSF-BSF 去年不支持数据共享。今年确实如此，所以我们 正在数据共享机制下重新申请。我们修改并改进了我们的应用程序 解决以前审稿人的担忧。他们的担忧可分为四类：1）困难 获取新数据，2）处理稀疏数据的挑战，3）缺乏关于我们的具体细节 方法，以及 4) 误解杰克逊实验室是一个营利性组织，并且认为这 会影响数据发布。 JAX 是一家非营利性研究机构，在提供 公共生物信息学资源对实验室小鼠没有任何限制。 背景及意义 对神经行为表型可重复性的担忧 科学界和非专业界越来越关注已发表的“发现” 不可复制[10-12]。著名机构和期刊，包括 NIH [13]、Science [14] 和 《自然》[15]最近宣布了关于该主题的政策立场，但仍然存在混乱和混乱 关于如何解决该问题的争论。虽然在所有科学领域都有记录， 临床前研究[16]特别注意到了这个问题，包括小鼠神经学和行为学研究 研究[7]。注意：为了防止混淆，我们使用术语“可复制性”来复制其他结果 研究和在同一研究中重现结论的“再现性”[17, 18]。 方差分析 方差 可复制 总方差的比例 G* L ns 是 0% 100% Gxlns G* L * 是 0% 100% Gxlns G* L * 是 0'1/o 100% Gxlns 甘斯 L * 否 0% 100% Gxl * g1 g2 图 1. 主效应显着性说明 基因型 (G)、实验室 (L) 和基因型 x 实验室 (GxL) 相互作用。四种不同行为测量的数据 左侧面板中描绘了三个实验室（颜色 编码）两个基因型（g）。每个的方差分析结果是 显示其中*=显着； ns=不显着。比例 总方差 (%) 用颜色编码的水平线表示 酒吧。基于 GxL 重要性的可重复性是 每项措施的右侧都标明了。 测量定量表型 基因工程小鼠品系已 成为发现的核心策略 哺乳动物基因功能，以及 描述疾病动物模型的特征 可以研究哪些假定的治疗方法（例如 评论参见[19, 20]）。国际 小鼠表型联盟 (IMPC) [21, 22] 协调国际努力 对数千种突变菌株进行表型分析， 最终实现功能注释 约 20,000 个蛋白质编码基因中的大部分 在哺乳动物基因组中。必不可少的 这项工作的一个方面是使高 吞吐量表型数据可访问 科学界公众 数据库 [23, 24]。这个的用处 然而，开展这项工作关键取决于 复制表型结果的能力 在其他实验室。这个大工程是 但有一个普遍需要的例子 小鼠表型分析的可重复性， 被多次提出和讨论 （例如，[27 25 ,7] 没有令人满意的 解决方案尚未通过。任何解决方案是 可能会被实验者采用 36