差分表达式分析用inmoose,集成在Python中的多组开源环境。

IF 3.3 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Maximilien Colange, Guillaume Appé, Léa Meunier, Solène Weill, Akpéli Nordor, Abdelkader Behdenna
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引用次数: 0

摘要

背景:差异基因表达分析是一项重要的技术,用于分析生物分子数据,以确定与表型相关的遗传特征。limma(微阵列数据)和edgeR (RNA-Seq数据)和deseq2 (RNA-Seq数据)是大量转录组学数据中差异基因表达分析最广泛使用的工具。结果:我们展示了InMoose的差分表达式特征,InMoose是R工具limma, edgeR和DESeq2的Python实现。我们通过实验证明,InMoose是这些工具的直接替代品,结果几乎相同。这确保了在生物信息学管道中连接两种语言时的可重复性。InMoose是一个基于GPL3许可发布的开源软件,可在www.github.com/epigenelabs/inmoose和https://inmoose.readthedocs.io上获得。结论:我们提出了一个新的Python实现,最先进的工具limma, edgeR和DESeq2,用于执行大量转录组学数据的差异基因表达分析。这个新实现显示的结果与原始工具几乎相同,提高了Python和R生物信息学管道之间的互操作性和可重复性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Differential expression analysis with inmoose, the integrated multi-omic open-source environment in Python.

Background: Differential gene expression analysis is a prominent technique for the analysis of biomolecular data to identify genetic features associated with phenotypes. Limma-for microarray data -, and edgeR and DESeq2-for RNA-Seq data-, are the most widely used tools for differential gene expression analysis of bulk transcriptomic data.

Results: We present the differential expression features of InMoose, a Python implementation of R tools limma, edgeR, and DESeq2. We experimentally show that InMoose stands as a drop-in replacement for those tools, with nearly identical results. This ensures reproducibility when interfacing both languages in bioinformatic pipelines. InMoose is an open source software released under the GPL3 license, available at www.github.com/epigenelabs/inmoose and https://inmoose.readthedocs.io .

Conclusions: We present a new Python implementation of state-of-the-art tools limma, edgeR, and DESeq2, to perform differential gene expression analysis of bulk transcriptomic data. This new implementation exhibits results nearly identical to the original tools, improving interoperability and reproducibility between Python and R bioinformatics pipelines.

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来源期刊
BMC Bioinformatics
BMC Bioinformatics 生物-生化研究方法
CiteScore
5.70
自引率
3.30%
发文量
506
审稿时长
4.3 months
期刊介绍: BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology. BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.
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