CSHL Computational Genomics Course

冷泉港实验室建议继续一门名为“计算基因组学”的课程，以 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际经验 基因组注释和分析，包括蛋白质序列数据库搜索，多个序列 配对，启动子和其他基因调控元件的鉴定，以及整合 将序列信息转化为更广泛的生物功能模型。该课程还提供计算机 科学家和数学家，介绍算法、计算方法和 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，以及对改变研究感兴趣的初级和高级调查人员 关于计算生物学的方向，本课程提供计算生物学的入门。 方法和对未来发展方向的展望。在八天的时间里，学生们收到了强烈的 扎根于基因组分析的生物学和计算机科学基础和实用 具有计算机实验室处理挑战性问题的经验。这门课由国际教师授课。 该领域公认的领导者，他们提供项目和生物学的实践演示 他们开发的数据库。在课程结束后，学生不仅可以有效地使用 目前在生物序列分析中可用的工具，他们还可以评估关键的新 通过考虑替代方法和解释来计算方法，并理解 回答广泛生物学问题的计算方法的优点和局限性。

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.