ABI Innovation: Annotation of cis-regulatory sequences using a large number of ChIP-seq datasets

动植物中的顺式调控序列与编码序列一样重要，但由于难以对其进行表征，我们对大多数已测序生物中的顺式调控序列的了解有限。最近强大的功能基因组技术的发展，特别是染色质免疫沉淀结合测序（ChIP-seq）技术，为在基因组规模上破译顺式调控序列提供了前所未有的机会。然而，从大量非常大的ChIP-seq数据集中获得顺式调控序列仍然是一项极具挑战性的任务。为了应对这一挑战，该项目将开发一套新颖的算法和工具，并利用大量的ChIP-seq数据集在动物和植物中应用它们来注释顺式调控序列。这些工具和预测的顺式调控序列将从根本上改变生物学家在人类和模式生物中研究调控基因组和转录调控的方式。该项目从一开始就涉及高中生，本科生，研究生和博士后，从而使他们接触算法和工具开发，并帮助他们培养解决复杂生物问题所需的批判性思维技能。PI将特别鼓励传统上代表性不足的少数民族和/或女学生参与该项目。在适当的时候，产生的算法和结果将被纳入PI在北卡罗来纳大学夏洛特分校教授的相关课程：本科生和研究生分子序列分析，计算比较基因组学和数学系统生物学。因此，该项目也将提供一个理想的教育平台，以培养下一代计算生物学家，他们可以使用非常大的数据集来解决重要的生物学问题。这些工具和资源将在http://bioinfo.uncc.edu/mniu/pcrms/www/上公开提供。

Cis-regulatory sequences in animals and plants are as important as coding sequences, but our understanding of them in most sequenced organisms is limited due to the difficulty in characterizing them. Recent developments of powerful functional genomic technologies, in particular, chromatin immunoprecipitation coupled with sequencing (ChIP-seq) techniques, have provided an unprecedented opportunity to decipher cis-regulatory sequences in a genome scale. However, it remains a highly challenging task to derive cis-regulatory sequences from a large number of very big ChIP-seq datasets. To tackle this challenge, this project will develop a set of novel algorithms and tools and apply them to annotate cis-regulatory sequences using a large number of ChIP-seq datasets in animals and plants. These tools and predicted cis-regulatory sequences will fundamentally change the ways that biologists study regulatory genomes and transcriptional regulation in humans and model organisms. The project involves high school students, undergraduates, graduates and a postdoctoral fellow from the very beginning, thereby exposing them to algorithm and tool development, and helping them develop critical thinking skills needed to solve complex biological problems. The PI will particularly encourage traditionally underrepresented minority and/or female students to participate in the project. Whenever appropriate, the resulting algorithms and results will be incorporated into the PI's relevant courses taught at the University of North Carolina at Charlotte: undergraduate and graduate Molecular Sequence Analysis, Computational Comparative Genomics, and Mathematical Systems Biology. Thus, the project will also provide an ideal educational platform to train the next generation of computational biologists who can use very big datasets solving important biological problems. The tools and resources will be publicly available at http://bioinfo.uncc.edu/mniu/pcrms/www/.