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树分析和可视化参考指南。

A reference guide for tree analysis and visualization.

机构信息

Structural and Computational Biology Unit, EMBL, Meyerhofstrasse 1, Heidelberg, Germany.

Computational Biology and Data Mining Group, Max-Delbrück Center for Molecular Medicine, Robert-Rössle-Strasse, 10, D-13125, Berlin, Germany.

出版信息

BioData Min. 2010 Feb 22;3(1):1. doi: 10.1186/1756-0381-3-1.

Abstract

The quantities of data obtained by the new high-throughput technologies, such as microarrays or ChIP-Chip arrays, and the large-scale OMICS-approaches, such as genomics, proteomics and transcriptomics, are becoming vast. Sequencing technologies become cheaper and easier to use and, thus, large-scale evolutionary studies towards the origins of life for all species and their evolution becomes more and more challenging. Databases holding information about how data are related and how they are hierarchically organized expand rapidly. Clustering analysis is becoming more and more difficult to be applied on very large amounts of data since the results of these algorithms cannot be efficiently visualized. Most of the available visualization tools that are able to represent such hierarchies, project data in 2D and are lacking often the necessary user friendliness and interactivity. For example, the current phylogenetic tree visualization tools are not able to display easy to understand large scale trees with more than a few thousand nodes. In this study, we review tools that are currently available for the visualization of biological trees and analysis, mainly developed during the last decade. We describe the uniform and standard computer readable formats to represent tree hierarchies and we comment on the functionality and the limitations of these tools. We also discuss on how these tools can be developed further and should become integrated with various data sources. Here we focus on freely available software that offers to the users various tree-representation methodologies for biological data analysis.

摘要

新的高通量技术(如微阵列或 ChIP-Chip 阵列)和大规模 OMICS 方法(如基因组学、蛋白质组学和转录组学)所获得的数据量变得非常庞大。测序技术变得越来越便宜和易用,因此,针对所有物种的生命起源和进化的大规模进化研究变得越来越具有挑战性。关于数据之间的关系以及它们如何分层组织的数据库迅速扩展。由于这些算法的结果无法有效地可视化,因此聚类分析越来越难以应用于大量数据。大多数可用的可视化工具能够表示这种层次结构,将数据投影到 2D 中,但通常缺乏必要的用户友好性和交互性。例如,当前的系统发育树可视化工具无法显示易于理解的具有几千个以上节点的大规模树。在这项研究中,我们回顾了目前可用于生物树可视化和分析的工具,这些工具主要是在过去十年中开发的。我们描述了用于表示树层次结构的统一和标准的计算机可读格式,并评论了这些工具的功能和局限性。我们还讨论了如何进一步开发这些工具,并应将其与各种数据源集成。在这里,我们重点介绍了免费提供的软件,这些软件为用户提供了各种用于生物数据分析的树表示方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cf0/2844399/f9420704415e/1756-0381-3-1-1.jpg

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