CAREER: New Technologies for Genome-Scale Comparative NcRNA Identification

职业：新技术的基因组规模的比较ncRNA鉴定密歇根州立大学是授予从教师早期职业发展计划（CAREER）的补助金，以开发新技术的基因组规模的比较非编码RNA（ncRNA）鉴定。ncRNA不翻译成蛋白质，而是直接作为RNA发挥作用，在许多生物化学过程中发挥着多种重要作用。因此，注释不同生物体中的ncRNA对现代生物学非常重要。在ncRNA检测的众多努力中，少数程序可以实际应用于整个基因组或大规模数据库。然而，这些基因组规模的工具依赖于传统的比较序列分析，不能有效地解释同源ncRNA的结构保守性，从而危及结构化ncRNA搜索的灵敏度和准确性。为了解决灵敏和快速的大规模ncRNA搜索的需要，本项目的重点是开发新的和改进的ncRNA检测算法，联合收割机结合传统的序列比较工具的效率和ncRNA的结构特征。具体目标是：（1）快速已知的ncRNA鉴定算法和工具，其可以有效地将大量推定的ncRNA分类到相应的ncRNA家族中，（2）用于ncRNA同源性搜索的高效基因组规模的序列比较算法，以及（3）一种新的ncRNA结构建模和比较方法。该项目的研究目标和计算生物学的培训需求与以下教育目标相结合和计划1)提高生物学和工程学生对计算生物学作为重要研究领域的认识。为高年级本科生和研究生设计了一门新的计算生物学课程，每年秋季开设。 2)通过MSU的定量生物学倡议（QBI）双专业学位课程改善研究生和本科生的跨学科研究培训经验。3)鼓励保留和招聘计算生物学相关专业的代表性不足的群体。PI将继续参加K-12女学生外联通过妇女在工程夏季计划每年，并帮助本科女生通过妇女在计算组织在密歇根州立大学。

CAREER: New Technologies for Genome-Scale Comparative NcRNA IdentificationMichigan State University is awarded a grant from the Faculty Early Career Development program (CAREER) to develop new technologies for genome-scale comparative noncoding RNA (ncRNA) identification. ncRNAs, which are not translated into protein but function directly as RNA, play diverse and important roles in many biochemical processes. Annotating ncRNAs in different organisms is thus highly important to modern biology. Among numerous efforts made for ncRNA detection, a few programs can be practically applied to whole genomes or large-scale databases. However, these genome-scale tools rely on conventional comparative sequence analysis, which cannot not effectively account for structural conservation in homologous ncRNAs and thus jeopardizes the sensitivity and accuracy of structured ncRNA search. In order to address the need of sensitive and fast large-scale ncRNA search, this project focuses on the development of novel and improved ncRNA detection algorithms that combine the efficiency of conventional sequence comparison tools and the structural features of ncRNAs. The specific objectives are: (1) fast known ncRNA identification algorithms and tools that can efficiently classify a large number of putative ncRNAs into corresponding ncRNA families, (2) highly efficient genome-scale sequence comparison algorithms for ncRNA homology search, and (3) a novel ncRNA structure modeling and comparison method.The research goals of this project and the training needs of computational biology are integrated with the following educational objectives and plans. 1) Improve the awareness of computational biology as an important research area among students in biology and engineering. A new computational biology course for both senior undergraduates and graduates has been designed and is offered every fall. 2) Improve cross-disciplinary research training experiences for graduate and undergraduate students through MSU's Quantitative Biology Initiative (QBI) dual-major degree program. 3) Encourage retention and recruitment of underrepresented groups in computational biology related majors. The PI will continue to attend the K-12 women students outreach through Women in Engineering Summer Program every year, and help undergraduate women students through Women In Computing organization at MSU.