CSHL Computational and Comparative Genomics Course

冷泉港实验室建议继续开设一门名为“计算和 比较基因组学”，将于2017 - 2019年秋季举行。计算与比较 基因组学课程为实验生物学家提供分子生物学，遗传学和 生物化学的理论背景和实践经验，必须使用和评估 基因组注释和分析的计算方法，包括蛋白质序列数据库 搜索、多序列比对、启动子鉴定和其他基因调控 元件，以及将序列信息整合到更广泛的生物功能模型中。的 课程还为计算机科学家和数学家提供了算法的介绍， 计算方法和生物序列分析中解决的生物学问题， 计算生物学对于博士后研究员和有兴趣的初级和高级研究人员 在改变他们的研究方向向计算生物学，课程提供了一个介绍 计算生物学方法和未来方向的调查。在七天的时间里， 学生在生物和计算机科学基础方面都有很强的基础， 基因组分析和实际的计算机实验室经验的挑战性问题。该课程是 由国际公认的领导人在该领域，谁提供动手示范， 他们开发的程序和生物数据库。在课程结束时，学生不能 只有有效地使用目前可用的生物序列分析工具，他们也可以评估 通过考虑替代方法和解释，采用全新的计算方法， 理解计算方法的优点和局限性，以回答广泛的生物学问题。 问题.

The Cold Spring Harbor Laboratory proposes to continue a course entitled “Computational and Comparative Genomics”, to be held in the Fall of 2017 – 2019. The Computational and Comparative Genomics course provides experimental biologists with backgrounds in molecular biology, genetics, and biochemistry with the theoretical background and practical experience necessary to use and evaluate computational approaches to genome annotation and analysis, including protein sequence database searching, multiple sequence alignment, identification of promoters and other genetic regulatory elements, and the integration of sequence information into broader models of biological function. The course also provides computer scientists and mathematicians with an introduction to the algorithms, computational methods, and biological problems that are addressed in biological sequence analysis and computational biology. For post-doctoral fellows, and junior and senior investigators who are interested in changing their research direction towards computational biology, the course provides an introduction to computational biology methods and a survey of future directions. Over a seven day period, the students receive a strong grounding in the both the biological and computer science foundations for genome analysis and practical computer laboratory experience on challenging problems. The course is taught by internationally recognized leaders in the field, who provide hands-on demonstrations of the programs and biological databases that they have developed. At the end of the course, students can not only use effectively currently available tools in biological sequence analysis, they can also evaluate critically new computational approaches by considering alternative methods and interpretations, and appreciate the strengths and limitations of computational methods for answering broad biological questions.