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） 医学博士Maha R Farhat是哈佛医学院的医学教练，并在马萨诸塞州总医院的肺和重症监护医学系的身体人员和一名员工。她将于5/2015在哈佛公共卫生学院完成生物统计学硕士学位。在过去的4。5年中，她在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获得了技能。她在整个基因组序列数据，耐药性数据和患者临床结果数据的分析方面具有经验，重点是鉴定结核分枝杆菌的耐药性遗传决定剂。她还在该领域开发了新方法。 Farhat博士有11个出版物5是第一作者，其中包括高影响力和期刊上的高度引用的作品自然遗传学，基因组医学和国际结核病与肺部疾病杂志。该K01奖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。 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。拟议的研究以及概述的培训活动 在该提案中，将成功地将Farhat博士定位为她的第一个R01和独立的物理科学家职业。环境：Farhat博士将在全球健康社会医学，生物统计学，进化生物学和定量社会科学研究所的哈佛大学杰出的哈佛部门中概述的跨学科工作。 Farhat博士的Mentalship团队将包括传染病和大数据领域的两个世界知名领导人，Megan Murray博士和Gary King博士；以及网络科学与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（Murray），该提案的主要心态被称为38多名学员，其中9名继续从事独立的研究职业，并成功获得了K奖。她也是两名最近被授予NIH/NIAID的PI，授予了CetR U19和TBRU U19，并拥有350多个同行评审出版物。为了补充她的导师的专业知识，Farhat博士将由Christiani博士提供肺和重症监护疗法，并在肺和环境遗传学领域的身体和世界知名研究员。她还将与数据科学家Merce Crosas博士和计算生物学家Parris Sabeti博士进行合作和咨询。她将通过Soumya Raychaudhuri博士的生物信息学实验室进行旋转，以使她对生物医学大数据的接触多样化。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，领导能力和赠款写作的正式培训。 Farhat博士提供的协作机会，智力环境和资源非常出色。研究：传染病仍然是发病率和死亡率的主要原因。尽管有效的抗菌药物可用，但病原体仍在成功地发展出新的疾病表型，这些表型使它们能够抵抗这些药物的杀戮，或者在其他情况下会导致更严重的疾病表现或广泛的传播链。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具有抗药性6。反感染疾病中反对新兴病原体表型斗争的关键策略是监视，以及早期的个性化疗法，以防止这些菌株传播和传播。及时开始的抗生素疗法开始 病原体敏感已被证明是潜水员感染阵列的关键因素抑制作用。依赖于检测微生物遗传突变的分子测试对于这些病原体表型的监测和诊断尤其有希望，但依赖于对突变如何与这些病原体表型相关联的全面理解。当前，关于病原体全基因组序列（WGS）的数据爆炸量越来越多地从临床实验室产生。关于疾病表型的数据也可能可用，但是分析和解释这些大数据的方法滞后。在这里，我提出的工具有助于进行此分析，利用结核分枝杆菌（MTB）和我的先前工作的大数据集。 Specifically I proposal 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病原体，并为最佳性能方法开发可推广的功率计算器。