Computational Biology Research Core

生殖科学领域正在迅速产生复杂的数据集，但数据的整合性仍然很差。计算生物学研究核心的主要目标是集中处理和整合将由项目调查人员产生的不同种类的数据集，并应用强大的分析方法来发现人类发育和植入的新的遗传和表观遗传调节因素。该核心将开发和应用严格、有效的分析方法，以确保一致和可重复的分析结果。该核心将与拟议的研究项目以及其他核心协同工作，并将有助于推动计算发育生物学领域的发展。它将负责管理数据传输和检索的流程，执行严格的质量控制，建立分析管道，整合数据，并向项目调查人员和公众传播分析结果。具体地说，拟议的Closed Core将能够(1)执行严格的质量控制测试，(2)分析从微阵列和下一代测序(NGS)获得的mRNA表达谱，(3)推断转录因子(TF)结合位点和表观遗传修饰 通过测序和微阵列实验，(4)研究小的非编码RNA的生物发生和功能，以及(5)整合这些不同的数据集，形成调控网络的概率模型，该网络控制人类胚胎发育和植入的早期阶段。

Complex datasets are being rapidly generated in reproductive sciences, but the data remain poorly integrated. The main objective of the Computational Biology Research Core is to centralize the processing and integration of the heterogeneous datasets to be generated by the project Investigators and to apply powerful analysis methods to discover novel genetic and epigenetic regulators of human development and implantation. This Core will develop and apply rigorous, effective analysis methods that will ensure consistent and reproducible analysis results. The Core will synergize with the proposed research projects as well as other Cores and will also contribute towards advancing the field of computational developmental biology. It will be responsible for managing the flow of data transfer and retrieval, performing rigorous quality control, establishing analysis pipelines, integrating the data, and disseminating the analysis results to the project Investigators as well as the public. Specifically, the proposed closed Core will be able to (1) perform rigorous quality control tests, (2) analyze mRNA expression profiles obtained from microarrays and Next-Generation Sequencing (NGS), (3) infer transcription factor (TF) binding sites and epigenetic modifications from sequencing and microarray experiments, (4) study the biogenesis and functions of small non-coding RNA, and (5) integrate these heterogeneous datasets to formulate probabilistic models of regulatory networks that govern the early stages of human embryo development and implantation.