2005 MARC INTERNS

这个子项目是许多利用资源的研究子项目之一 由NIH/NCRR资助的中心拨款提供。子项目的主要支持 而子项目的主要调查员可能是由其他来源提供的， 包括其它NIH来源。 列出的子项目总成本可能 代表子项目使用的中心基础设施的估计数量， 而不是由NCRR赠款提供给子项目或子项目工作人员的直接资金。 MARC实习补助金，MARC讲习班的后续活动。 这个为期两周的课程，针对教师，学生和工作人员在MARC计划机构，有准备的参与者在当地校园教生物信息学课程的目标。鼓励与MARC计划机构相关的感兴趣的个人申请，包括来自不同部门的教师，包括生物，化学，物理，数学，统计，计算机科学和工程。 讲习班将向与会者介绍围绕生物信息学的计算、数学和生物学问题。生物信息学中的一些基本主题将被讨论，包括推断结构和功能;数据库搜索和成对序列比对;多序列比对;模式识别。特别强调将放在参数的选择，和数学，统计和生物基础的算法和方法。信息也将提供隐马尔可夫模型，先进的位置特定的权重矩阵，baidom估计，同源建模，结构生物学，分子动力学模拟，和基因树创建。互联网资源和远程计算设施的使用也将被讨论，将介绍PERL编程语言。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Grant for MARC internships, follow-up to MARC workshop. This two week course, directed towards faculty, students and staff at MARC program institutions, has the goal of preparing the participants to teach bioinformatics courses at their local campuses. Interested individuals associated with a MARC program institution are encouraged to apply, including faculty from diverse departments, including biology, chemistry, physics, mathematics, statistics, computer science, and engineering. The workshop will introduce participants to the computational, mathematical and biological issues surrounding bioinformatics. A number of fundamental topics in bioinformatics will be discussed including inferring structures and functions; database searching and pairwise sequence alignment; multiple sequence alignments; pattern identification. Special emphasis will be placed on parameter selection, and the mathematical, statistical, and biological foundations of the algorithms and methods presented. Information will also be provided on hidden Markov models, advanced position-specific weight matrices, bayesian estimation, homology modeling, structural biology, molecular dynamics simulations, and phylogenic tree creation. The use of internet resources and remote computing facilities will also be discussed as will an introduction to the PERL programming language.