Predoctoral Training in Bioinformatics and Computational Biology

生物信息学和计算生物学博士前培训

• 批准号: 10413932
• 负责人: THOMAS D TULLIUS
• 金额: $ 46.83万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10413932
• 关键词: Bioinformatics; Computational Biology; Training; pre-doctoral

Project Summary/Abstract This renewal application seeks continued support for the Predoctoral Training Program in Bioinformatics and Computational Biology at Boston University. Five predoctoral training slots per year are requested, to support trainees for the first year of residence. The Bioinformatics Program aims to prepare top researchers for careers in the molecular life sciences who will support the advances now underway in modern medicine. Based largely on the framework of the human genome sequence, these advances depend upon a battery of high-throughput techniques that generate enormous quantities of data that in turn reflect the complexities of biological networks. Full exploitation of this information requires computation-based, interdisciplinary efforts. The Program will inculcate the skills required in this new environment, including (a) an understanding of biological systems, (b) facility with computational methods and statistics, especially for network analysis, and (c) the ability to interact and communicate with colleagues from diverse disciplines. The curriculum includes a strong didactic foundation in computational methods, statistics, biological networks, and database development, along with in-depth examination of biological systems. Experiential learning is emphasized throughout the training (1) the annual International Workshop in Bioinformatics and Systems Biology, a joint undertaking of computational biology graduate training programs in Boston, Kyoto/Tokyo, and Berlin; (2) the Challenge Project, which offers teams of first-year trainees the opportunity to do original research on large-scale problems; (3) the Wet-Lab Experience, which introduces new trainees to state-of-the- curriculum. Program features include art experimental methods in the summer before they officially enter the program; (4) the annual Student- Organized Symposium; (5) three rotations; and (6) an annual Student Seminar at which trainees present their research. Emphasis is placed throughout in developing skills in reproducible and rigorous research, and in communicating science. Numerous mechanisms are employed to assist trainees in preparing for diverse career paths. A distinguished training faculty of 28 members is drawn from 16 departments from the Charles River and Medical campuses of Boston University. Areas of mentor research include genomics, proteomics, epigenetics, neurobiology, biological networks, synthetic biology, systems biology, statistical methods in bioinformatics, metabolomics, virology, immunology, the microbiome, and structural biology. Several mentors are engaged in translational bioinformatics . Co-advising of trainees, by computational and experimental mentors, is strongly encouraged. Students play a large role in defining their interdisciplinary research projects. 16 trainees who have been supported by this T32 grant currently are working towards the Ph.D.; they are a subset of the 54 trainees who currently are members of the Boston University Bioinformatics Ph.D. program.

项目总结/摘要 此更新申请寻求继续支持生物信息学博士前培训计划， 波士顿大学的计算生物学。每年要求五个博士前培训名额，以支持 实习生第一年居住。生物信息学计划旨在为顶级研究人员的职业生涯做好准备 在分子生命科学中，他们将支持现代医学的进步。主要依据 在人类基因组序列的框架上，这些进展依赖于一组高通量的 这些技术产生了大量的数据，这些数据反过来反映了生物学的复杂性。 网络.充分利用这些信息需要基于计算的跨学科努力。的 该计划将灌输在这个新环境中所需的技能，包括（a）对生物学的理解 系统，（B）计算方法和统计学的设施，特别是用于网络分析，以及（c） 能够与来自不同学科的同事进行互动和沟通。课程包括一个强大的 在计算方法，统计学，生物网络和数据库开发的教学基础，沿着 深入研究生物系统。在整个培训过程中强调体验式学习 (1)一年一度的生物信息学和系统国际研讨会 生物学，在波士顿、京都/东京联合开展计算生物学研究生培训项目， 柏林;（2）挑战项目，为一年级学员提供机会， 大规模问题的研究;（3）湿实验室的经验，介绍新的学员的状态- 课程。方案特点包括 艺术实验方法在夏季正式进入程序之前;（4）每年的学生- 有组织的研讨会;（5）三个轮换;和（6）一个年度学生研讨会，学员提出他们的 research.重点放在整个发展技能，可重复和严格的研究，并在 传播科学采用了许多机制来帮助受训者为各种不同的学习做好准备。 职业道路。由28名成员组成的杰出培训教师来自查尔斯学院的16个部门 波士顿大学的河流和医学校区。 导师的研究领域包括基因组学，蛋白质组学， 表观遗传学，神经生物学，生物网络，合成生物学，系统生物学，统计方法， 生物信息学、代谢组学、病毒学、免疫学、微生物组学和结构生物学。几位导师 从事翻译生物信息学 .通过计算和实验，共同指导学员 导师，强烈鼓励。学生在定义他们的跨学科研究项目中发挥着重要作用。 16名获得T32赠款支持的受训人员目前正在攻读博士学位;他们是一个 目前是波士顿大学生物信息学博士的54名学员的子集。程序.

项目成果

Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data.

• DOI: 10.1038/s41467-022-29212-9
• 发表时间: 2022-03-30
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Hong R;Koga Y;Bandyadka S;Leshchyk A;Wang Y;Akavoor V;Cao X;Sarfraz I;Wang Z;Alabdullatif S;Jansen F;Yajima M;Johnson WE;Campbell JD
• 通讯作者: Campbell JD

The Mutational Signature Comprehensive Analysis Toolkit (musicatk) for the Discovery, Prediction, and Exploration of Mutational Signatures.

• DOI: 10.1158/0008-5472.can-21-0899
• 发表时间: 2021-12-01
• 期刊: Cancer research
• 影响因子: 11.2
• 作者: Chevalier A;Yang S;Khurshid Z;Sahelijo N;Tong T;Huggins JH;Yajima M;Campbell JD
• 通讯作者: Campbell JD

Environmental boundary conditions for the origin of life converge to an organo-sulfur metabolism

• DOI: 10.1038/s41559-019-1018-8
• 发表时间: 2019-12-01
• 期刊: NATURE ECOLOGY & EVOLUTION
• 影响因子: 16.8
• 作者: Goldford, Joshua E.;Hartman, Hyman;Segre, Daniel
• 通讯作者: Segre, Daniel

Metabolic complexity drives divergence in microbial communities.

代谢的复杂性导致微生物群落的分化。

• DOI: 10.1101/2023.08.03.551516
• 发表时间: 2023
• 期刊: bioRxiv : the preprint server for biology
• 作者: Silverstein,Michael;Bhatnagar,JenniferM;Segrè,Daniel
• 通讯作者: Segrè,Daniel

Stoichiometric Modeling of Artificial String Chemistries Reveals Constraints on Metabolic Network Structure.

• DOI: 10.1007/s00239-021-10018-0
• 发表时间: 2021-08
• 期刊: Journal of molecular evolution
• 影响因子: 3.9
• 作者: Moyer D;Pacheco AR;Bernstein DB;Segrè D
• 通讯作者: Segrè D