CSHL Computational Genomics Course

冷泉港实验室建议继续开设一门名为“计算基因组学”的课程， 将于2020 - 2024年秋季举行。计算基因组学课程提供实验 具有分子生物学、遗传学和生物化学背景的生物学家， 使用和评估计算方法所需的背景和实践经验， 基因组注释和分析，包括蛋白质序列数据库搜索，多序列 启动子和其他遗传调控元件的比对、鉴定，以及 序列信息转化为更广泛的生物功能模型。课程还提供计算机 科学家和数学家介绍了算法，计算方法， 在生物序列分析和计算生物学中解决的生物学问题。为 博士后研究员，以及有兴趣改变他们研究的初级和高级研究人员 方向计算生物学，该课程提供了一个介绍计算生物学 方法和未来方向的调查。在八天的时间里，学生们得到了一个强大的 在基因组分析和实际应用的生物和计算机科学基础的接地 计算机实验室的经验，具有挑战性的问题。该课程由国际上 该领域公认的领导者，他们提供项目和生物学的实际演示 他们开发的数据库。在课程结束时，学生不仅可以有效地使用 目前可用的生物序列分析工具，他们也可以评估关键的新的 计算方法，考虑替代方法和解释，并赞赏 计算方法在回答广泛的生物学问题时的优势和局限性。

The Cold Spring Harbor Laboratory proposes to continue a course entitled “Computational Genomics”, to be held in the Fall of 2020 – 2024. The Computational 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 an eight 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.