Pymportx:促进Python中的下一代转录组学分析。

IF 4 Q1 GENETICS & HEREDITY NAR Genomics and Bioinformatics Pub Date : 2024-11-15 eCollection Date: 2024-12-01 DOI:10.1093/nargab/lqae160
Paula Pena González, Dafne Lozano-Paredes, José Luis Rojo-Álvarez, Luis Bote-Curiel, Víctor Javier Sánchez-Arévalo Lobo
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引用次数: 0

摘要

在转录组学中,有效输入量化基因表达数据是关键。从历史上看,R包timport通过支持来自各种量化工具的无缝数据集成来解决这一需求。然而,Python社区缺乏相应的工具,限制了跨平台的生物信息学互操作性。我们介绍Pymportx,它是对ximport的Python改编版,它复制并扩展了原始包的功能。Pymportx保持基因表达数据的完整性和准确性,同时提高处理速度和Python生态系统内的集成。它支持新的数据格式,并包括用于增强数据探索和分析的工具。在MIT许可下,Pymportx与Python的生物信息学工具顺利集成,促进了R和Python生态系统之间统一高效的工作流程。这一进步不仅拓宽了Python广泛的工具集,还促进了跨学科合作和尖端生物信息学分析的发展。
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Pymportx: facilitating next-generation transcriptomics analysis in Python.

The efficient importation of quantified gene expression data is pivotal in transcriptomics. Historically, the R package Tximport addressed this need by enabling seamless data integration from various quantification tools. However, the Python community lacked a corresponding tool, restricting cross-platform bioinformatics interoperability. We introduce Pymportx, a Python adaptation of Tximport, which replicates and extends the original package's functionalities. Pymportx maintains the integrity and accuracy of gene expression data while improving processing speed and integration within the Python ecosystem. It supports new data formats and includes tools for enhanced data exploration and analysis. Available under the MIT license, Pymportx integrates smoothly with Python's bioinformatics tools, facilitating a unified and efficient workflow across the R and Python ecosystems. This advancement not only broadens access to Python's extensive toolset but also fosters interdisciplinary collaboration and the development of cutting-edge bioinformatics analyses.

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来源期刊
CiteScore
8.00
自引率
2.20%
发文量
95
审稿时长
15 weeks
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