ABI Innovation: Scalable kmer-based algorithms and software for gene expression and regulation

蛋白质是生命有机体的基本构件之一。每个组织都仔细地调节它产生的一组蛋白质，包括所产生的蛋白质的类型和每种蛋白质的数量。例如，构成心脏结构和实现心脏功能的蛋白质与定义肺或大脑的蛋白质不同。深入了解控制蛋白质合成的过程以及这一过程的时间安排具有基础性的科学意义，其影响遍及整个生物学。这个项目专注于蛋白质调控的两个关键点：从基因组DNA中读出基因以产生中间mRNA的步骤(称为转录)，以及移除一些mRNA片段并重新连接末端的步骤(称为剪接)，以便翻译成蛋白质将产生具有不同性质的版本。完全分析组织产生的信使核糖核酸是可能的，但会产生非常大的、复杂的数据集。该项目将创建软件工具，以促进对这些数据集的有效分析，使上述两个过程有意义。研究结果可应用于多种生物学问题的研究。除了科学贡献外，还针对高中生和本科生开展了几项教育和外展活动。特别是，将组织一次暑期讲习班，为当地高中教师和有代表性的学生提供实践生物信息学经验。马里兰大学Terrapin教师计划的学生将接受指导，以制定生物信息学的教学计划；这些课程随后将在当地高中授课。此外，高中生将被邀请在PIS的指导下参加暑期研究体验。细胞的形态和功能是由基因活动的精确调控决定的。生物学研究的一个长期目标是充分破译基因调控的机制。最终基因产物的空间和时间调节主要是在基因转录和产生的RNA被处理时执行的。反过来，转录调控是由调控元件介导的，如增强子和启动子，其特征是扩散的短退化DNA基序簇。转录调控分析中的一项重要任务是比较基因组内和基因组间的调控区域。对于转录后加工事件，关键的第一步是识别、表征和量化基因的可替代加工变体。越来越多的序列数据掌握着这些问题的答案，但为了提取它们的秘密，必须挖掘这些数据。整合大量下一代测序数据的任务规模使得传统解决方案效率低下，因此成为主要的计算瓶颈。这项拟议的研究将开发高效的算法和工具，通过利用最近开发的DNA单词或k-MERS的超快数据结构，在转录和转录后水平上分析基因表达。对两个相关但不同的基本问题--调控区域相似性的量化和基因的替代异构体的量化--提出的解决方案提供了一种传统方法的替代方案，方法是根据k-mer相似性搜索重新表述这些问题，最近为包括转录量化(例如旗鱼)在内的相关问题开发和利用了极其有效的解决方案。拟议的工具将使研究与转录调控有关的某些基本机制和进化问题成为可能，并能够以前所未有的规模分析剪接异构体变体。研究目标将与几项以当地高中生为重点的STEM教育活动捆绑在一起，无论是在早期研究参与方面还是在课堂教育方面。其中一些活动将在生物信息学和计算生物学中心(CBCB)组织，从而鼓励学生和中心教师之间的密切互动。结果的链接将在PI的实验室页面上提供，网址为cbcb.umd.edu/~Sridhar/software.html

Proteins are one of the basic building blocks of living organisms. Each tissue carefully regulates the set of proteins that it produces, both of the types of protein made and the amounts of each. For instance, the proteins that make up the structure and enable the function of the heart are different from those defining the lungs or brain. A deep understanding of the processes that control what proteins are made and how this is timed is of fundamental scientific importance, with implications throughout biology. This project focuses on two critical points in protein regulation: the step in which genes are read off of the genomic DNA to make the intermediate mRNA (called transcription), and the step in which some pieces of the mRNA are removed and the ends reconnected (called splicing), so that translation to protein will result in versions with distinct properties. Complete profiling of mRNA produced by tissues is possible but results in very large, complex data sets. This project will create software tools to facilitate efficient analyses of these data sets, making sense of the two processes described above. The results can be applied to investigate a wide variety of biological questions. Besides the scientific contributions, several educational and outreach activities are aimed at students at the high school and undergraduate levels. In particular, a summer workshop will be organized to provide hands-on bioinformatics experiences to local high school teachers and representative students. Students in the University of Maryland Terrapin Teacher program will be mentored to create lesson plans in Bioinformatics; the lessons will subsequently be delivered at local high schools. In addition, high school students will be invited to participate in summer research experiences under the mentorship of the PIs.Cellular morphology and function is determined by precise regulation of gene activity. A long-term goal of biological research is to fully decipher the mechanisms of gene regulation. Spatial and temporal regulation of final gene products is primarily executed when genes are transcribed and the resulting RNA is processed. In turn, transcriptional regulation is mediated by regulatory elements, such as enhancers and promoters, which are characterized by diffuse clusters of short degenerate DNA motifs. An important task in the analysis of the transcriptional regulation involves comparison of regulatory regions, both within and across genomes. With regards to post-transcriptional processing events, critical first steps are identification, characterization and quantification of alternatively processed variants of a gene. Increasingly, sequence data are available that hold the answers to these questions, but which must be mined in order to extract their secrets. The scale of the task of incorporating the massive amounts of next generation sequencing data render conventional solutions inefficient and thus presents a major computational bottleneck. The proposed research will develop efficient algorithms and tools for the analysis of gene expression at both transcriptional and post-transcriptional levels by exploiting recently developed ultrafast data structures for DNA words or k-mers. The proposed solutions to two related but different fundamental problems - quantification of regulatory region similarity, and quantification of alternative isoforms of a gene, provide an alternative to traditional approaches by reformulating the problems in terms of k-mer similarity searches, for which extremely efficient solutions have been recently developed and exploited for related problems, including transcript quantification (e.g. Sailfish). The proposed tools will enable investigation of certain fundamental mechanistic and evolutionary questions pertaining to transcriptional regulation and analysis of splice isoform variants at an unprecedented scale. The research goals will be tied to several STEM educational activities focused on local high school students, both in terms of early research involvement and classroom education. Some of these activities will be organized at the Center for Bioinformatics and Computational Biology (CBCB), thereby encouraging the close interaction between the students and the center faculty.The link to the results will be provided at PI's lab page at cbcb.umd.edu/~sridhar/software.html