Evaluation and comparison of bioinformatic tools for the enrichment analysis of metabolomics data.
Evaluation and comparison of bioinformatic tools for the enrichment analysis of metabolomics data.
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DOI：
10.1186/s12859-017-2006-0
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发表时间：
2018-01-02
影响因子：
3
通讯作者：
Andres-Lacueva C
中科院分区：
文献类型：
--
作者：
Marco-Ramell A;Palau-Rodriguez M;Alay A;Tulipani S;Urpi-Sarda M;Sanchez-Pla A;Andres-Lacueva C
丰富“组学”数据集的生物信息学工具有助于对数据的解释和理解。到目前为止，很少有数据适用于代谢组学数据集。这项工作的主要目标是首次对这些工具的性能进行批判性概述。为此，选择了来自代谢库的数据集，并创建了丰富的数据。使用这些工具分析了这两类数据，并彻底检查了产出。基于Jaccard距离的非度量多维标度(NMDS)，对最常用的代谢物集浓缩工具进行了探索性多变量分析，并反映了它们的多样性。在不同的代谢物数据库(HMDB、KEGG、PubChem、Chebi、BioCyc/HumanCyc、LipidMAPS、ChemSpider、Metlin和Rest2)中搜索数据集的代谢物的编码(识别符)。提供数据集代谢物更多标识符的数据库是PubChem，紧随其后的是Metlin和Chebi。然而，这些数据库有重复的条目，可能会出现假阳性。考察了BioCyc/HumanCyc、ConensusPathDB、Impala、MBRole、MetbraAnalyst、Metabox、MetExplore、MPEA、PathVisio和Reactome等ORA工具以及作图工具KEGGREST的性能。不同工具之间以及真实数据和丰富数据之间的结果基本一致，尽管这些工具各有不同。然而，也发现了一些有争议的结果，如代谢物总数的差异。还对基于疾病的浓缩分析进行了评估，但发现它们并不准确，可能是因为代谢物疾病集不是最新的，而且很难从代谢物清单中预测疾病。我们广泛地回顾了可用于代谢组数据集的各种工具、代谢物数据库的完整性、ORA方法的性能和基于疾病的分析的最新进展。尽管这些工具千变万化，但他们提供了一致的结果，而不依赖于他们的分析方法。然而，需要在代谢物和途径数据库的完整性方面做更多的工作，这严重影响了浓缩分析的准确性。改进将转化为对代谢组更准确和更全面的洞察。本文的在线版本(10.1186/s12859-0172006-0)包含补充材料，授权用户可以使用。
Bioinformatic tools for the enrichment of ‘omics’ datasets facilitate interpretation and understanding of data. To date few are suitable for metabolomics datasets. The main objective of this work is to give a critical overview, for the first time, of the performance of these tools. To that aim, datasets from metabolomic repositories were selected and enriched data were created. Both types of data were analysed with these tools and outputs were thoroughly examined. An exploratory multivariate analysis of the most used tools for the enrichment of metabolite sets, based on a non-metric multidimensional scaling (NMDS) of Jaccard’s distances, was performed and mirrored their diversity. Codes (identifiers) of the metabolites of the datasets were searched in different metabolite databases (HMDB, KEGG, PubChem, ChEBI, BioCyc/HumanCyc, LipidMAPS, ChemSpider, METLIN and Recon2). The databases that presented more identifiers of the metabolites of the dataset were PubChem, followed by METLIN and ChEBI. However, these databases had duplicated entries and might present false positives. The performance of over-representation analysis (ORA) tools, including BioCyc/HumanCyc, ConsensusPathDB, IMPaLA, MBRole, MetaboAnalyst, Metabox, MetExplore, MPEA, PathVisio and Reactome and the mapping tool KEGGREST, was examined. Results were mostly consistent among tools and between real and enriched data despite the variability of the tools. Nevertheless, a few controversial results such as differences in the total number of metabolites were also found. Disease-based enrichment analyses were also assessed, but they were not found to be accurate probably due to the fact that metabolite disease sets are not up-to-date and the difficulty of predicting diseases from a list of metabolites. We have extensively reviewed the state-of-the-art of the available range of tools for metabolomic datasets, the completeness of metabolite databases, the performance of ORA methods and disease-based analyses. Despite the variability of the tools, they provided consistent results independent of their analytic approach. However, more work on the completeness of metabolite and pathway databases is required, which strongly affects the accuracy of enrichment analyses. Improvements will be translated into more accurate and global insights of the metabolome. The online version of this article (10.1186/s12859-017-2006-0) contains supplementary material, which is available to authorized users.
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