python外文文献

1、 a python Environment for Tree ExplorationReviewed by Jaime Huerta-Cepas,corresponding author1 Joaqun Dopazo,2 and Toni Gabaldncorresponding author1AbstractMany bioinformatics analyses, ranging from gene clustering to phylogenetics, produce hierarchical trees as their main result. These are used to represent the relationships among different biological entities, thus facilitating their analysis and interpretation. A number of standalone programs are available that focus on tree visualization or

2、that perform specific analyses on them. However, such applications are rarely suitable for large-scale surveys, in which a higher level of automation is required. Currently, many genome-wide analyses rely on tree-like data representation and hence there is a growing need for scalable tools to handle tree structures at large scale.Keywords: Python, spiking neurons, simulation, integrate and fire, teaching, neural networks, computational neuroscience, softwareBackgroundHere we present the Environm

3、ent for Tree Exploration (ETE), a python programming toolkit that assists in the automated manipulation, analysis and visualization of hierarchical trees. ETE libraries provide a broad set of tree handling options as well as specific methods to analyze phylogenetic and clustering trees. Among other features, ETE allows for the independent analysis of tree partitions, has support for the extended newick format, provides an integrated node annotation system and permits to link trees to external da

4、ta such as multiple sequence alignments or numerical arrays. In addition, ETE implements a number of built-in analytical tools, including phylogeny-based orthology prediction and cluster validation techniques. Finally, ETEs programmable tree drawing engine can be used to automate the graphical rendering of trees with customized node-specific visualizations.ConclusionsETE provides a complete set of methods to manipulate tree data structures that extends current functionality in other bioinformati

5、c toolkits of a more general purpose. ETE is free software and can be downloaded from http:/ete.cgenomics.org.Trees are commonly used to represent the results of many bioinformatics analyses. In particular, such type of binary graphs are ideal to describe the hierarchical relationships among a variety of biological entities. Some common examples are the evolutionary analysis of molecular sequences or the clusterization of genes and proteins according to their properties. Besides the information

6、encoded in the topology of trees, branch lengths can also be scaled to provide information on the distances between the different partitions. In phylogenetics, for instance, trees are used to illustrate the evolutionary relationships among species or molecular sequences, considering terminal nodes as extant Operational Taxonomic Units (OTU) and internal nodes as their corresponding ancestors. In such phylogenetic trees, branch lengths are usually proportional to the evolutionary distance among s

7、equences. Other applications, such as the analysis of gene expression, use hierarchical clustering analysis to group genes or experimental conditions according to the similarity of their expression patterns. Likewise, trees are used by many protein classification methods and for the analysis of phylogenetic profiles. Thus, the analysis of tree data structures is a common task in many areas of bioinformatics and there is a need for analytical and visualization tools. In this respect, a number of

8、bioinformatic programs do exist that assist in the exploration of hierarchical trees. Most of them, however, consist of standalone applications that are focused on visualization and, occasionally, on performing specific tests. Some well known examples are TreeView 1, a widely used program for inspecting phylogenetic trees; Cluster Treeview 2, an application for visualizing microarray clustering results; ATV 3, a java program used to explore phylogenies which provides also some editing options; M

9、EGA 4, an evolutionary genetics analysis suite that includes a built-in tree viewer; and many other recent applications 5-8. While all these programs are very useful to manage single trees, they can hardly be automatized or adapted to specific needs. Thus, when the analysis of hundreds or thousands of trees is required, the use of standalone programs becomes restrictive, because a much higher level of automation is required. In such cases, programming toolkits represent a more adequate framework, since they provide tools and methods to handle data at a lower level. Using toolkits, bioinformaticians can easily create their own analysis pipelines and program custom tasks over large collections of data 9. Several generic bioinformatic toolkits do exist that cover a wide range of programming languages and scopes, with BioPerl 10 and BioPython 11 being the most extensively developed. Together with a broad range of other features, these toolkits allow ce

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