metaSPARSim: a 16S rRNA gene sequencing count data simulator
metaSPARSim: a 16S rRNA gene sequencing count data simulator
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DOI：
10.1186/s12859-019-2882-6
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发表时间：
2019-11-22
影响因子：
3
通讯作者：
Di Camillo, Barbara
中科院分区：
文献类型：
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作者：
Patuzzi, Ilaria;Baruzzo, Giacomo;Di Camillo, Barbara
背景资料：在过去的几年中，16 S rRNA基因测序（16 S rDNA-seq）作为一种进行微生物群落研究的方法，其选择率惊人地快速增长。尽管该技术相当流行，但目前有大量特定工具可用于适当的16 S rDNA-seq计数数据预处理和模拟。事实上，绝大多数工具都是采用以前用于批量RNA-seq数据的方法开发的，对它们在宏基因组学领域的适用性评估不佳。对于这些工具和少数专门为16 S rDNA-seq数据开发的工具，性能评估具有挑战性，主要是由于数据的复杂性和缺乏现实的模拟模型。事实上，据我们所知，没有软件认为数据模拟可直接获得合成的16 S rDNA-seq计数表，正确的模型沉重的稀疏性和组合性典型的这些data.Results：在本文中，我们提出了MetaSPARSim，稀疏计数矩阵模拟器，旨在用于开发16 S rDNA-seq宏基因组数据处理管道。MetaSPARSim实现了一种新的生成过程，该过程使用多变量超几何分布对测序过程进行建模，以逼真地模拟16 S rDNA-seq计数表，类似于真实的实验数据的组成性和稀疏性。它提供了随时可用的计数矩阵，并有可能重现不同的预编码的情况下，估计模拟参数从真实的实验数据。该工具可在http://sysbiobig.dei.unipd.it/?上获得q=软件# metaSPARSim和https://gitlab.com/sysbiobig/metasparsim.Conclusion：metaSPARSim能够生成类似于真实的16 S rDNA-seq数据的计数矩阵。计数数据模拟器的可用性对于方法开发人员（需要工具验证的基本事实）和想要评估最先进的分析工具以选择最准确的分析工具的用户都非常有价值。因此，我们相信，对于在16 S rRNA基因测序背景下开发、测试和使用稳健可靠的数据分析方法的研究人员来说，MetaSPARSim是一个有价值的工具。
Background: In the last few years, 16S rRNA gene sequencing (16S rDNA-seq) has seen a surprisingly rapid increase in election rate as a methodology to perform microbial community studies. Despite the considerable popularity of this technique, an exiguous number of specific tools are currently available for proper 16S rDNA-seq count data preprocessing and simulation. Indeed, the great majority of tools have been developed adapting methodologies previously used for bulk RNA-seq data, with poor assessment of their applicability in the metagenomics field. For such tools and the few ones specifically developed for 16S rDNA-seq data, performance assessment is challenging, mainly due to the complex nature of the data and the lack of realistic simulation models. In fact, to the best of our knowledge, no software thought for data simulation are available to directly obtain synthetic 16S rDNA-seq count tables that properly model heavy sparsity and compositionality typical of these data.Results: In this paper we present metaSPARSim, a sparse count matrix simulator intended for usage in development of 16S rDNA-seq metagenomic data processing pipelines. metaSPARSim implements a new generative process that models the sequencing process with a Multivariate Hypergeometric distribution in order to realistically simulate 16S rDNA-seq count table, resembling real experimental data compositionality and sparsity. It provides ready-to-use count matrices and comes with the possibility to reproduce different pre-coded scenarios and to estimate simulation parameters from real experimental data. The tool is made available at http://sysbiobig.dei.unipd.it/?q=Software# metaSPARSim and https://gitlab.com/sysbiobig/metasparsim.Conclusion: metaSPARSim is able to generate count matrices resembling real 16S rDNA-seq data. The availability of count data simulators is extremely valuable both for methods developers, for which a ground truth for tools validation is needed, and for users who want to assess state of the art analysis tools for choosing the most accurate one. Thus, we believe that metaSPARSim is a valuable tool for researchers involved in developing, testing and using robust and reliable data analysis methods in the context of 16S rRNA gene sequencing.