Department of Computational Biology and Simulation, TU Darmstadt, Schnittspahnstraße 2, Darmstadt, 64287, Germany.
BMC Bioinformatics. 2018 Oct 1;19(1):346. doi: 10.1186/s12859-018-2367-z.
As molecular biology is creating an increasing amount of sequence and structure data, the multitude of software to analyze this data is also rising. Most of the programs are made for a specific task, hence the user often needs to combine multiple programs in order to reach a goal. This can make the data processing unhandy, inflexible and even inefficient due to an overhead of read/write operations. Therefore, it is crucial to have a comprehensive, accessible and efficient computational biology framework in a scripting language to overcome these limitations.
We have developed the Python package Biotite: a general computational biology framework, that represents sequence and structure data based on NumPyndarrays. Furthermore the package contains seamless interfaces to biological databases and external software. The source code is freely accessible at https://github.com/biotite-dev/biotite .
Biotite is unifying in two ways: At first it bundles popular tasks in sequence analysis and structural bioinformatics in a consistently structured package. Secondly it adresses two groups of users: novice programmers get an easy access to Biotite due to its simplicity and the comprehensive documentation. On the other hand, advanced users can profit from its high performance and extensibility. They can implement their algorithms upon Biotite, so they can skip writing code for general functionality (like file parsers) and can focus on what their software makes unique.
随着分子生物学不断产生越来越多的序列和结构数据,用于分析这些数据的软件也在不断增加。大多数程序都是针对特定任务而设计的,因此用户通常需要组合多个程序才能实现目标。由于读写操作的开销,这可能会使数据处理变得麻烦、不灵活甚至效率低下。因此,在脚本语言中拥有一个全面、可访问且高效的计算生物学框架来克服这些限制至关重要。
我们开发了 Python 包 Biotite:一个通用的计算生物学框架,它基于 NumPyndarrays 表示序列和结构数据。此外,该包还包含与生物数据库和外部软件的无缝接口。源代码可在 https://github.com/biotite-dev/biotite 上免费获取。
Biotite 具有两个方面的统一性:首先,它将序列分析和结构生物信息学中的常见任务捆绑在一个结构一致的包中。其次,它针对两类用户:由于其简单性和全面的文档,新手程序员可以轻松访问 Biotite。另一方面,高级用户可以从其高性能和可扩展性中受益。他们可以在 Biotite 上实现自己的算法,从而可以跳过编写通用功能(如文件解析器)的代码,并专注于使他们的软件具有独特性的功能。