New Tools for the interpretation of Pathogen Genomic Data with a focus on Mycobacterium tuberculosis
解读病原体基因组数据的新工具,重点关注结核分枝杆菌
基本信息
- 批准号:9413742
- 负责人:
- 金额:$ 18.15万
- 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
- 起止时间:2015-09-30 至 2020-07-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
DESCRIPTION (provided by applicant)
Maha R Farhat, MD is an Instructor of Medicine at Harvard Medical School on the tenure track and a staff physician in the Department of Pulmonary and Critical Care Medicine at Massachusetts General Hospital. She is completing a masters of biostatistics at the Harvard School of Public Health in 5/2015. She has spent the last 4.5 years acquiring skills in Mycobacterium tuberculosis biology, epidemiology, bioinformatics and biostatistics. She has experience in the analysis of whole genome sequence data, drug resistance data and patient clinical outcome data with the focus of identifying Mycobacterium tuberculosis genetic determinants of drug resistance. She has also developed new methods in this area. Dr. Farhat has 11 publications 5 of which are first author including high impact and highly cited work in the journals Nature Genetics, Genome Medicine and the International Journal of Tuberculosis and Lung Disease. The short term goals of this K01 award are to provide training for Dr. Farhat in critical aspects of data science, computational and evolutionary biology, advanced biostatistics and network science. Dr. Farhat's long term goal is to become a leader in the field of Big Data analysis for infectious diseases. The proposed research as well as the training activities outlined
in the proposal will successfully position Dr. Farhat for her first R01 and an independent career as a physician scientist. Environment: Dr. Farhat will perform the interdisciplinary work outlined in this proposal at the distinguished Harvard Departments of Global Health Social Medicine, Biostatistics, Evolutionary biology and the Institute for Quantitative Social Sciences. Dr. Farhat' mentorship team will include two world renowned leaders in the fields of infectious diseases and Big Data, Dr. Megan Murray and Dr. Gary King; and two rising stars in the fields of network Science and evolutionary Biology, Dr. JP Onnela and Dr. Michael Desai. Dr. Murray, the principal mentor on this proposal has mentored over 38 trainees, 9 of which have went on to have independent research careers, and 6 competed successfully for K awards. She is also PI on two recently awarded NIH/NIAID grants a CETR U19 and a TBRU U19 and has over 350 peer reviewed publications. To complement the expertise of her mentors Dr. Farhat will be advised by Dr. Christiani a practicing pulmonary and critical care physician and world renowned researcher in the field of lung and environmental genetics. She will also collaborate and consult with Dr. Merce Crosas, a data scientist, and Dr. Pardis Sabeti, a computational biologist. She will rotate through Dr. Soumya Raychaudhuri's bioinformatics laboratory to diversify her exposure to biomedical Big Data. In addition, she will receive formal training in evolutionary biology, Bayesian and mixed-model biostatistics, computer science, leadership skills and grant writing. The collaborative opportunities, intellectual environment and resources available to Dr. Farhat are outstanding. Research: Infectious diseases continue to be a major cause of morbidity and mortality. Despite the availability of effective antimicrobials, pathogens are successfully evolving new disease phenotypes that allow them to resist killing by these drugs or in other instances cause more severe disease manifestations or wider chains of transmission. Drug resistance (DR) is now common and some bacteria have even become resistant to multiple types or classes of antibiotics6. A key strategy in the fight against emerging pathogen phenotypes in infectious diseases is surveillance, and early personalized therapy to prevent transmission and propagation of these strains. The timely initiation of antibiotic therapy to which
the pathogen is sensitive has been shown to be the key factor influencing treatment outcome for a diverse array of infections. Molecular tests that rely on the detection of microbial genetic mutations are particularly promising for surveillance and diagnosis of these pathogen phenotypes but rely on a comprehensive understanding of how mutations associate with these pathogen phenotypes. Currently there is an explosion of data on pathogen whole genome sequences (WGS) that is increasingly generated from clinical laboratories. Data on disease phenotype may also be available, but methods for the analysis and interpretation of these Big Data are lagging. Here I propose tools to aid in this analysis leveraging Big Data sets from Mycobacterium tuberculosis (MTB) and my prior work. Specifically I propose to (1) develop a web-based public interface to several analysis tools, including a statistical learning model that can predict the MTB DR phenotype from its genomic sequence, (2) to develop and study an MTB gene-gene network, based on WGS data, to improve our understanding of the effect of mutation-mutation interactions on the DR phenotype, and (3) study the performance of methods in current use for the association of genotype and phenotype in pathogens, and develop a generalizable power calculator for the best performing method.
描述(由申请人提供)
Maha R Farhat 医学博士是哈佛医学院的终身教授,也是马萨诸塞州总医院肺科和重症监护医学科的主治医师,她正在哈佛大学公共卫生学院攻读生物统计学硕士学位。 2015 年 5 月,她花了 4.5 年时间学习结核分枝杆菌生物学、流行病学、生物信息学和生物统计学方面的技能。 Farhat 博士还开发了该领域的新方法,其中 5 篇是高影响力和高影响力的出版物。在《自然遗传学》、《基因组医学》和《国际结核病与肺病杂志》杂志上引用的工作 该 K01 奖项的短期目标是为 Farhat 博士提供数据科学、计算和肺病关键方面的培训。 Farhat 博士的长期目标是成为传染病大数据分析领域的领导者。
该提案中的内容将成功地为 Farhat 博士奠定了她的第一个 R01 和作为一名医师科学家的独立职业环境:Farhat 博士将在著名的哈佛大学全球健康社会医学、生物统计学、进化系进行本提案中概述的跨学科工作。 Farhat博士的导师团队将包括传染病和大数据领域的两位世界知名领导人Megan Murray博士和Gary King博士以及网络领域的两位后起之秀。科学与进化论生物学方面,该提案的首席导师 JP Onnela 博士和 Michael Desai 博士指导了超过 38 名学员,其中 9 人已经从事独立研究工作,6 人成功角逐 K 奖。此外,她还是最近获得的两项 NIH/NIAID 资助(CETR U19 和 TBRU U19)的 PI,并拥有 350 多篇同行评审出版物。 Christiani 博士是一位执业肺部和重症监护医师,也是肺部和环境遗传学领域的世界知名研究员。她还将与数据科学家 Merce Crosas 博士和计算生物学家 Pardis Sabeti 博士合作和咨询。将在 Soumya Raychaudhuri 博士的生物信息学实验室轮流工作,以丰富她对生物医学大数据的接触。此外,她还将接受进化生物学、贝叶斯和混合模型生物统计学、计算机科学、领导力方面的正式培训。法哈特博士拥有出色的合作机会、智力环境和资源。研究:尽管有有效的抗菌药物,但传染病仍然是发病和死亡的主要原因。耐药性(DR)现在很常见,一些细菌甚至对多种类型或类别的抗生素产生了耐药性。在战斗中针对传染病中新出现的病原体表型的方法是监测和早期个性化治疗,以防止这些菌株的传播和繁殖,并及时启动抗生素治疗。
病原体的敏感性已被证明是影响多种感染治疗结果的关键因素依赖于微生物基因突变检测的分子测试对于这些病原体表型的监测和诊断特别有希望,但依赖于全面的了解。目前,临床实验室生成的病原体全基因组序列 (WGS) 数据激增,但也可能提供有关疾病表型的数据,但也有分析和解释这些数据的方法。大数据在这方面我落后了。提出利用结核分枝杆菌 (MTB) 的大数据集和我之前的工作来帮助进行此分析的工具,具体而言,我建议 (1) 开发一个基于网络的多个分析工具的公共界面,包括可以预测的统计学习模型。根据其基因组序列确定 MTB DR 表型,(2) 基于 WGS 数据开发和研究 MTB 基因-基因网络,以提高我们对突变与突变相互作用对 DR 表型影响的理解,以及 (3) 研究的表现目前用于关联病原体基因型和表型的方法,并开发一个通用的功率计算器以获得最佳执行方法。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Maha Farhat其他文献
Maha Farhat的其他文献
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{{ truncateString('Maha Farhat', 18)}}的其他基金
An RNA Nanosensor for the Diagnosis of Antibiotic Resistance in M. Tuberculosis
用于诊断结核分枝杆菌抗生素耐药性的 RNA 纳米传感器
- 批准号:
10670613 - 财政年份:2023
- 资助金额:
$ 18.15万 - 项目类别:
Human adaptation and transmissibility of Mycobacterium tuberculosis genetic lineages. A genomic epidemiology study to guide TB control
结核分枝杆菌遗传谱系的人类适应和传播性。
- 批准号:
10382446 - 财政年份:2021
- 资助金额:
$ 18.15万 - 项目类别:
Human adaptation and transmissibility of Mycobacterium tuberculosis genetic lineages. A genomic epidemiology study to guide TB control
结核分枝杆菌遗传谱系的人类适应和传播性。
- 批准号:
10218961 - 财政年份:2021
- 资助金额:
$ 18.15万 - 项目类别:
Investigating bacterial contributions to TB treatment response: a focus on in-host pathogen dynamics
研究细菌对结核病治疗反应的贡献:关注宿主内病原体动态
- 批准号:
10468975 - 财政年份:2020
- 资助金额:
$ 18.15万 - 项目类别:
Investigating bacterial contributions to TB treatment response: a focus on in-host pathogen dynamics
研究细菌对结核病治疗反应的贡献:关注宿主内病原体动态
- 批准号:
10100014 - 财政年份:2020
- 资助金额:
$ 18.15万 - 项目类别:
Investigating bacterial contributions to TB treatment response: a focus on in-host pathogen dynamics
研究细菌对结核病治疗反应的贡献:关注宿主内病原体动态
- 批准号:
10267702 - 财政年份:2020
- 资助金额:
$ 18.15万 - 项目类别:
Investigating bacterial contributions to TB treatment response: a focus on in-host pathogen dynamics
研究细菌对结核病治疗反应的贡献:关注宿主内病原体动态
- 批准号:
10772431 - 财政年份:2020
- 资助金额:
$ 18.15万 - 项目类别:
Investigating bacterial contributions to TB treatment response: a focus on in-host pathogen dynamics
研究细菌对结核病治疗反应的贡献:关注宿主内病原体动态
- 批准号:
10701691 - 财政年份:2020
- 资助金额:
$ 18.15万 - 项目类别:
Investigating bacterial contributions to TB treatment response: a focus on in-host pathogen dynamics
研究细菌对结核病治疗反应的贡献:关注宿主内病原体动态
- 批准号:
10751670 - 财政年份:2020
- 资助金额:
$ 18.15万 - 项目类别:
New Tools for the interpretation of Pathogen Genomic Data with a focus on Mycobacterium tuberculosis
解读病原体基因组数据的新工具,重点关注结核分枝杆菌
- 批准号:
9044227 - 财政年份:2015
- 资助金额:
$ 18.15万 - 项目类别:
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