Comprehensive benchmarking and ensemble approaches for metagenomic classifiers.
Comprehensive benchmarking and ensemble approaches for metagenomic classifiers.
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DOI：
10.1186/s13059-017-1299-7
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发表时间：
2017-09-21
期刊：
影响因子：
12.3
通讯作者：
Mason CE
中科院分区：
文献类型：
--
作者：
McIntyre ABR;Ounit R;Afshinnekoo E;Prill RJ;Hénaff E;Alexander N;Minot SS;Danko D;Foox J;Ahsanuddin S;Tighe S;Hasan NA;Subramanian P;Moffat K;Levy S;Lonardi S;Greenfield N;Colwell RR;Rosen GL;Mason CE
宏基因组学的主要挑战之一是鉴定临床和环境样品中的微生物。虽然有一组广泛且异构的计算工具可用于使用全基因组鸟枪测序数据对微生物进行分类，但对这些方法的全面比较是有限的。在这项研究中，我们使用了迄今为止最大的一组实验室生成的和模拟的控制在846个物种，以评估11个宏基因组分类器的性能。工具的特征在于它们在属、种和菌株水平上鉴定分类群、量化分类群的相对丰度以及将个体读数分类到种水平的能力。引人注目的是，在相同的数据集上，11种工具识别的物种数量可以相差三个数量级以上。各种策略可以改善分类错误，包括丰度过滤，集成方法和工具交叉。然而，这些策略往往不足以完全消除环境样本中的假阳性，这在涉及医学相关物种时尤为重要。总体而言，具有不同分类策略（k-mer、比对、标记）的配对工具可以联合收割机结合它们各自的优点。这项研究提供了阳性和阴性对照，滴定标准，并通过比较精确度，准确度和召回范围来选择宏基因组分析工具的指南。我们表明，适当的实验设计和分析参数可以减少假阳性，在复杂的宏基因组样本中提供更高的物种分辨率，并改善结果的解释。本文的在线版本（doi：10.1186/s13059-017-1299-7）包含补充材料，可供授权用户使用。
One of the main challenges in metagenomics is the identification of microorganisms in clinical and environmental samples. While an extensive and heterogeneous set of computational tools is available to classify microorganisms using whole-genome shotgun sequencing data, comprehensive comparisons of these methods are limited. In this study, we use the largest-to-date set of laboratory-generated and simulated controls across 846 species to evaluate the performance of 11 metagenomic classifiers. Tools were characterized on the basis of their ability to identify taxa at the genus, species, and strain levels, quantify relative abundances of taxa, and classify individual reads to the species level. Strikingly, the number of species identified by the 11 tools can differ by over three orders of magnitude on the same datasets. Various strategies can ameliorate taxonomic misclassification, including abundance filtering, ensemble approaches, and tool intersection. Nevertheless, these strategies were often insufficient to completely eliminate false positives from environmental samples, which are especially important where they concern medically relevant species. Overall, pairing tools with different classification strategies (k-mer, alignment, marker) can combine their respective advantages. This study provides positive and negative controls, titrated standards, and a guide for selecting tools for metagenomic analyses by comparing ranges of precision, accuracy, and recall. We show that proper experimental design and analysis parameters can reduce false positives, provide greater resolution of species in complex metagenomic samples, and improve the interpretation of results. The online version of this article (doi:10.1186/s13059-017-1299-7) contains supplementary material, which is available to authorized users.
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影响因子：
7
作者：
Erlich Y
通讯作者：
Erlich Y
影响因子：
16.6
作者：
Bradley P;Gordon NC;Walker TM;Dunn L;Heys S;Huang B;Earle S;Pankhurst LJ;Anson L;de Cesare M;Piazza P;Votintseva AA;Golubchik T;Wilson DJ;Wyllie DH;Diel R;Niemann S;Feuerriegel S;Kohl TA;Ismail N;Omar SV;Smith EG;Buck D;McVean G;Walker AS;Peto TE;Crook DW;Iqbal Z
通讯作者：
Iqbal Z
影响因子：
12.3
作者：
Koren S;Harhay GP;Smith TP;Bono JL;Harhay DM;Mcvey SD;Radune D;Bergman NH;Phillippy AM
通讯作者：
Phillippy AM
DOI：
10.1093/bioinformatics/btt389
发表时间：
2013-09-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Ames SK;Hysom DA;Gardner SN;Lloyd GS;Gokhale MB;Allen JE
通讯作者：
Allen JE
影响因子：
4.6
作者：
Karlsson E;Lärkeryd A;Sjödin A;Forsman M;Stenberg P
通讯作者：
Stenberg P