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: 9413742
• 负责人: Maha Farhat
• 金额: $ 18.15万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 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: https://reporter.nih.gov/project-details/9413742
• 关键词: New; Tools; interpretation; Pathogen; Genomic

 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) 马哈·R·法哈特，医学博士，哈佛医学院终身教职讲师，马萨诸塞州综合医院肺部和重症监护医学部的专职医生。她将于2015年5月在哈佛大学公共卫生学院攻读生物统计学硕士学位。在过去的4.5年里，她一直在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。她在全基因组序列数据、耐药性数据和患者临床结局数据的分析方面拥有丰富的经验，重点是识别结核分枝杆菌耐药性的遗传决定因素。她还开发了这一领域的新方法。法哈特博士发表了11篇论文，其中5篇是第一作者，包括在《自然遗传学》、《基因组医学》和《国际结核病和肺部疾病杂志》上发表的高影响力和高被引用的工作。这一K01奖项的短期目标是在数据科学、计算和进化生物学、高级生物统计学和网络科学的关键方面为Farhat博士提供培训。法哈特博士的长期目标是成为传染病大数据分析领域的领导者。拟议的研究以及概述的培训活动 在提案中，法哈特博士将成功地为她的第一个R01和作为一名内科科学家的独立职业生涯定位。环境：法哈特博士将在哈佛大学著名的全球卫生、社会医学、生物统计学、进化生物学和定量社会科学研究所开展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者Megan Murray博士和Gary King博士；以及网络科学和进化生物学领域的两位后起之秀JP Onnela博士和Michael Desai博士。默里博士是这项计划的主要导师，他已经指导了38名学员，其中9人后来从事了独立的研究工作，6人成功竞争了K奖。她还在最近两次获得NIH/NIAID授予的CETRU19和TBRU U19上获得了PI，并拥有350多篇同行评议的出版物。为了补充她的导师的专业知识，法哈特博士将得到克里斯汀尼博士的建议，克里斯汀尼博士是一名执业的肺部和重症护理内科医生，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默斯·克罗萨斯博士和计算生物学家帕迪斯·萨贝蒂博士合作并提供咨询。她将轮流参观Soumya Raychaudhuri博士的生物信息学实验室，以使她对生物医学大数据的接触多样化。此外，她将接受进化生物学、贝叶斯和混合模型生物统计学、计算机科学、领导技能和拨款撰写方面的正式培训。法哈特博士所拥有的合作机会、智力环境和资源都非常出色。研究：传染病仍然是发病率和死亡率的主要原因。尽管有有效的抗菌剂可用，但病原体正在成功地进化出新的疾病表型，使它们能够抵抗这些药物的致死，或者在其他情况下导致更严重的疾病表现或更广泛的传播链。抗药性(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抗药性。与传染病中新出现的病原体表型作斗争的一个关键战略是监测和早期个性化治疗，以防止这些菌株的传播和繁殖。及时开始抗生素治疗 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。依赖于检测微生物基因突变的分子测试在监测和诊断这些病原体表型方面特别有希望，但依赖于对突变如何与这些病原体表型相关的全面了解。目前，关于病原体全基因组序列(WGS)的数据呈爆炸式增长，这些数据越来越多地来自临床实验室。关于疾病表型的数据也可能是可用的，但分析和解释这些大数据的方法是滞后的。在这里，我建议利用来自结核分枝杆菌(MTB)的大数据集和我之前的工作来帮助进行此分析。具体地说，我建议(1)开发一个基于网络的公共界面来访问几种分析工具，包括可以根据其基因组序列预测MTB DR表型的统计学习模型，(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-突变相互作用对DR表型影响的理解，以及(3)研究目前用于病原体基因和表型关联的方法的性能，并开发一个通用性最好的方法的功率计算器。