Data Analysis Tools for Emerging High-Throughput Technologies

适用于新兴高通量技术的数据分析工具

• 批准号: 10612937
• 负责人: Rafael Angel Irizarry
• 金额: $ 59.68万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10612937
• 关键词: Adoption; Affect; Algorithms; Basic Science; Biological; Biomedical Research; Computer software; DNA Methylation; DNA Sequence; Data; Data Analyses; Data Analytics; Data Set; Database Management Systems; Development; Disease; FarGo; Gene Expression; Goals; Investigation; Laboratories; Measurement; Measures; Methodology; Molecular; National Research Council; Nucleic Acids; Outcome; Phenotype; Protocols documentation; Province; Publications; Research Personnel; Sampling; Scanning; Signal Transduction; Source; Statistical Methods; Systematic Bias; Techniques; Technology; Translational Research; Variant; Work; clinical application; complex data; flexibility; frontier; high throughput technology; improved; indexing; interest; precision medicine; success; tool

Project Summary Biomedical research and the basic sciences are increasingly dependent on high-throughput technologies that have the ability to simultaneously measure thousands of nucleic acid molecules in a sample. In combination with ingenious laboratory protocols, these technologies have permitted unprecedented ways of studying the molecular basis of disease and phenotypic variation. As a result of the increasing adoption of these technologies, more investigations rely on complex datasets and require the development of new statistical techniques to adequately interpret data. Today, high-throughput technologies applications go far beyond their original task of studying DNA sequence itself and also include the measurement of quantitative and dynamic outcomes such as gene expression levels and DNA methylation (DNAm) status. These quantitative and dynamic outcomes introduce levels of variability that give rise to further data analytic challenges related to distinguishing unwanted sources of variability from bio- logically relevant signals. Furthermore, when measuring these quantitative outcomes, data are subject to severe technological and biological biases that can substantially impact downstream analyses. Our group has previously demonstrated that statistical methodology can provide great improvements over ad-hoc algorithms offered as de- faults by technology developers. Our highly cited statistical methodology and our widely used software demonstrate the success of our work. The National Research Council's Frontiers in Massive Data Analysis publication states that, “the challenges for massive data go beyond the storage, indexing, and querying that have been the province of classical database systems and instead hinge on the ambitious goal of inference”. Inference is particularly relevant in biomedical applications since we often look to draw conclusions based on observed differences between groups in the presence of within group variability. Two particularly challenging tasks relate to performing valid inference when 1) we perform scans over large spaces to identify small regions of interests and 2) the data is affected by unexpected systematic bias or batch effects. We will focus on these two general challenges. Our specific proposal is to work on the most urgent needs of researchers facing new challenges as they increasingly rely on high-throughput techniques. We will leverage the expertise of our collaborators to prioritize projects. We greatly appreciate the flexibility permitted by the R35 mechanism as it will help us maximize the impact of our work.

项目摘要 生物医学研究和基础科学越来越依赖高通量技术，这些技术具有 能够同时测量样本中的数千个核酸分子。与独具匠心的 实验室协议，这些技术已经允许前所未有的方法来研究分子基础 疾病和表型变异。由于越来越多地采用这些技术，更多的调查 依赖复杂的数据集，需要开发新的统计技术来充分解释数据。 今天，高通量技术的应用远远超出了研究dna序列的原始任务。 还包括对定量和动态结果的测量，如基因表达水平和 DNA甲基化(DNaM)状态。这些定量的和动态的结果引入了可变性的水平 引发了与区分不需要的可变性来源和生物来源相关的进一步数据分析挑战 逻辑上相关的信号。此外，在衡量这些量化结果时，数据受到严重影响 可能对下游分析产生重大影响的技术和生物偏差。我们小组之前已经 证明了统计方法可以提供比ff的自组织算法更大的改进 技术开发人员的失误。我们被广泛引用的统计方法和我们广泛使用的软件表明 为我们工作的成功干杯。 国家研究委员会的《海量数据分析中的前沿》出版物指出， 对于海量数据，已经超越了传统数据库的存储、索引和查询 而是依赖于雄心勃勃的推理目标“。推论在生物医学中尤其相关 应用程序，因为我们经常希望根据观察到的存在的组之间的差异来得出结论 集团内部的可变性。两项特别具有挑战性的任务与执行有效推理有关，当1)我们 对大片空间执行扫描，以确定感兴趣的小区域；2)数据是意外检测到的ff 系统性偏差或批量ff等。我们将重点关注这两个普遍的挑战。我们的特殊fic建议是致力于 最迫切的需求是研究人员面临着新的挑战，因为他们越来越依赖高通量技术。 我们将利用我们的合作者的专业知识来确定项目的优先顺序。我们非常欣赏fl的灵活性 这是R35机制允许的，因为它将帮助我们最大限度地发挥我们工作的影响。

项目成果

Discovery of antibodies and cognate surface targets for ovarian cancer by surface profiling.

• DOI: 10.1073/pnas.2206751120
• 发表时间: 2023-01-03
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1

Detection of renal cell carcinoma using plasma and urine cell-free DNA methylomes.

• DOI: 10.1038/s41591-020-0933-1
• 发表时间: 2020-07
• 期刊: Nature medicine
• 影响因子: 82.9
• 作者: Nuzzo PV;Berchuck JE;Korthauer K;Spisak S;Nassar AH;Abou Alaiwi S;Chakravarthy A;Shen SY;Bakouny Z;Boccardo F;Steinharter J;Bouchard G;Curran CR;Pan W;Baca SC;Seo JH;Lee GM;Michaelson MD;Chang SL;Waikar SS;Sonpavde G;Irizarry RA;Pomerantz M;De Carvalho DD;Choueiri TK;Freedman ML
• 通讯作者: Freedman ML

Effectiveness estimates of three COVID-19 vaccines based on observational data from Puerto Rico.

• DOI: 10.1016/j.lana.2022.100212
• 发表时间: 2022-05
• 期刊: Lancet regional health. Americas
• 作者: Robles-Fontán MM;Nieves EG;Cardona-Gerena I;Irizarry RA
• 通讯作者: Irizarry RA

Differential richness inference for 16S rRNA marker gene surveys.

• DOI: 10.1186/s13059-022-02722-x
• 发表时间: 2022-08-01
• 期刊: Genome biology
• 影响因子: 12.3

All-cause excess mortality across 90 municipalities in Gujarat, India, during the COVID-19 pandemic (March 2020-April 2021).

• DOI: 10.1371/journal.pgph.0000824
• 发表时间: 2022
• 期刊: PLOS global public health