CAREER: New Technologies for Genome-Scale Comparative NcRNA Identification

职业：基因组尺度比较NcRNA鉴定的新技术密歇根州立大学获得了教师早期职业发展计划（CAREER）的资助，用于开发基因组尺度比较非编码RNA （NcRNA）鉴定的新技术。ncRNAs不被翻译成蛋白质，而是直接作为RNA发挥作用，在许多生化过程中发挥着多种重要作用。因此，在不同生物体中注释ncrna对现代生物学非常重要。在对ncRNA检测的众多努力中，少数程序可以实际应用于全基因组或大规模数据库。然而，这些基因组尺度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搜索的敏感性和准确性。为了满足灵敏和快速的大规模ncRNA搜索的需求，本项目专注于开发新的和改进的ncRNA检测算法，将传统序列比较工具的效率与ncRNA的结构特征结合起来。具体目标是：(1)快速已知的ncRNA识别算法和工具，可以有效地将大量假定的ncRNA分类到相应的ncRNA家族中；(2)高效的基因组尺度序列比较算法，用于ncRNA同源性搜索；(3)一种新的ncRNA结构建模和比较方法。本项目的研究目标和计算生物学的培养需求，与以下教育目标和计划相结合。1)提高学生对计算生物学作为一个重要研究领域的认识。一门新的计算生物学课程为高年级本科生和研究生设计，每年秋季开课。2)通过密歇根州立大学的定量生物学计划（QBI）双学位项目，提高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中代表性不足的群体。PI将继续参加每年通过女性工程暑期项目开展的K-12女学生拓展活动，并通过密歇根州立大学的女性计算机组织帮助本科女学生。

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.