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Biotite: a unifying open source computational biology framework in Python
BACKGROUND: 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 c...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6167853/ https://www.ncbi.nlm.nih.gov/pubmed/30285630 http://dx.doi.org/10.1186/s12859-018-2367-z |
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author | Kunzmann, Patrick Hamacher, Kay |
author_facet | Kunzmann, Patrick Hamacher, Kay |
author_sort | Kunzmann, Patrick |
collection | PubMed |
description | BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2367-z) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6167853 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-61678532018-10-09 Biotite: a unifying open source computational biology framework in Python Kunzmann, Patrick Hamacher, Kay BMC Bioinformatics Software BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2367-z) contains supplementary material, which is available to authorized users. BioMed Central 2018-10-01 /pmc/articles/PMC6167853/ /pubmed/30285630 http://dx.doi.org/10.1186/s12859-018-2367-z Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Software Kunzmann, Patrick Hamacher, Kay Biotite: a unifying open source computational biology framework in Python |
title | Biotite: a unifying open source computational biology framework in Python |
title_full | Biotite: a unifying open source computational biology framework in Python |
title_fullStr | Biotite: a unifying open source computational biology framework in Python |
title_full_unstemmed | Biotite: a unifying open source computational biology framework in Python |
title_short | Biotite: a unifying open source computational biology framework in Python |
title_sort | biotite: a unifying open source computational biology framework in python |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6167853/ https://www.ncbi.nlm.nih.gov/pubmed/30285630 http://dx.doi.org/10.1186/s12859-018-2367-z |
work_keys_str_mv | AT kunzmannpatrick biotiteaunifyingopensourcecomputationalbiologyframeworkinpython AT hamacherkay biotiteaunifyingopensourcecomputationalbiologyframeworkinpython |