Department of Informatics, University of Oslo, Oslo, Norway.
Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.
Gigascience. 2017 Jul 1;6(7):1-12. doi: 10.1093/gigascience/gix032.
Recent large-scale undertakings such as ENCODE and Roadmap Epigenomics have generated experimental data mapped to the human reference genome (as genomic tracks) representing a variety of functional elements across a large number of cell types. Despite the high potential value of these publicly available data for a broad variety of investigations, little attention has been given to the analytical methodology necessary for their widespread utilisation.
We here present a first principled treatment of the analysis of collections of genomic tracks. We have developed novel computational and statistical methodology to permit comparative and confirmatory analyses across multiple and disparate data sources. We delineate a set of generic questions that are useful across a broad range of investigations and discuss the implications of choosing different statistical measures and null models. Examples include contrasting analyses across different tissues or diseases. The methodology has been implemented in a comprehensive open-source software system, the GSuite HyperBrowser. To make the functionality accessible to biologists, and to facilitate reproducible analysis, we have also developed a web-based interface providing an expertly guided and customizable way of utilizing the methodology. With this system, many novel biological questions can flexibly be posed and rapidly answered.
Through a combination of streamlined data acquisition, interoperable representation of dataset collections, and customizable statistical analysis with guided setup and interpretation, the GSuite HyperBrowser represents a first comprehensive solution for integrative analysis of track collections across the genome and epigenome. The software is available at: https://hyperbrowser.uio.no.
最近的大规模研究，如 ENCODE 和 Roadmap Epigenomics，已经生成了映射到人类参考基因组（作为基因组轨迹）的实验数据，这些数据代表了大量细胞类型中的各种功能元素。尽管这些公开可用的数据对于广泛的研究具有很高的潜在价值，但很少关注广泛利用这些数据所需的分析方法。
我们在这里提出了一种用于分析基因组轨迹集合的基本原则处理方法。我们已经开发了新的计算和统计方法，以允许在多个和不同的数据源之间进行比较和验证分析。我们定义了一组通用问题，这些问题在广泛的研究中都很有用，并讨论了选择不同的统计度量和零模型的影响。例如，对比不同组织或疾病之间的分析。该方法已在一个全面的开源软件系统 GSuite HyperBrowser 中实现。为了使生物学家能够访问该功能，并促进可重复的分析，我们还开发了一个基于网络的界面，提供了一种专业指导和可定制的方法来利用该方法。通过这个系统，许多新的生物学问题可以灵活地提出并迅速得到回答。
通过简化数据采集、数据集集合的互操作表示以及具有引导设置和解释的可定制统计分析的组合，GSuite HyperBrowser 代表了一种全面的综合解决方案，用于在整个基因组和表观基因组中对轨迹集合进行综合分析。该软件可在以下网址获得：https://hyperbrowser.uio.no。
