CAREER: Expanding the Dimensions of Computational Epigenomic Modeling and Analysis

个体内的每种细胞类型基本上共享相同的DNA序列，但不同的细胞类型可能具有非常不同的功能。在不同的细胞类型中，调节DNA序列解释的是DNA以及包裹DNA的组蛋白尾部的表观遗传标记。大规模并行测序使多种细胞类型的多个表观遗传标记的全基因组图谱成为可能。这项研究将在几个重要的新维度上推进表观基因组数据的建模和分析的计算方法。其中一个维度涉及开发计算方法，以模拟表观基因组随时间的变化。目前正在通过绘制多个时间点的多个表观遗传标记来研究一些重要的生物学过程，这增加了对这些数据进行建模和分析的新的计算方法的需要。另一个维度涉及预测没有标记的细胞类型中的表观遗传标记的全基因组信号的计算方法。这是一个关键的计算问题，因为在实验上绘制每一种感兴趣细胞类型的每一个表观遗传标记是不可行的。该项目的第三个维度涉及计算方法，通过将多个部分信息的数据源整合到这些相互作用中，将表观基因组图谱确定的假定的末端调控元件与他们调控的目标基因相关联。作为职业项目的一部分，Jason Ernst将为在加州大学洛杉矶分校创建计算生物科学协作室做出贡献，旨在同时增加生物研究人员在计算方法方面的培训，并促进计算和实验研究人员之间的合作。将创建一个新的以研究为重点的研究生研讨会，涵盖计算表观基因组学的研究主题。基于这项研究的本科生物信息学课程的课程项目也将被创建。将招募本科生、未被充分代表的少数民族学生和计算机科学专业的女性学生参与研究。所有创建的教学材料都将在互联网上传播。在该项目下开发的计算方法将作为开放源码软件广泛传播，并可能被广泛的生物学研究人员用于一些重要的生物学应用。

Every cell type within an individual essentially shares the same DNA sequence, yet different cell types can have very different functions. Modulating the interpretation of the DNA sequence in different cell types are epigenetic marks on the DNA and on the tails of the histone proteins around which that DNA is wrapped. Massively parallel sequencing has enabled genome-wide mapping of multiple epigenetic marks across a number of cell types. The research will advance computational approaches for modeling and analyzing epigenomic data in several important new dimensions. One dimension involves developing computational methods for modeling epigenomic changes over time. A number of important biological processes are now being studied by mapping multiple epigenetic marks at multiple time points raising the need for new computational approaches to model and analyze this data. Another dimension involves computational approaches to predicting the genome- wide signals of epigenetic marks in cell types in which a mark is not mapped. This is a key computational problem because it is unfeasible to experimentally map every epigenetic mark in every cell type of interest. A third dimension of the project involves computational methods to associate putative distal regulatory elements identified by epigenomic maps with the target genes they regulate by integrating multiple partially informative data sources to these interactions.As part of the CAREER project Jason Ernst will contribute to the creation of a computational biosciences collaboratory at UCLA, aimed at simultaneously increasing the training of biological researchers in computational approaches and facilitating collaborations between computational and experimental researchers. A new research- focused graduate seminar that covers research topics in computational epigenomics will be created. Course projects for an undergraduate bioinformatics course based on the research will also be created. Undergraduate students, under-represented minority students, and women computer science students will be recruited to participate in research. All instructional material created will be disseminated on the internet. Computational methods developed under this project will be broadly disseminated as open source software and will likely be used by a wide range of biological researchers for a number of important biological applications.