史上最权威宏基因组软件评估—人工重组宏基因组基准数据集

写在前面

近年来，宏基因组学得到了快速发展，但由于研究对象包含成百上千物种混合体，仍面临三大挑战———高度复杂混合物种基因组拼接、混合序列分箱(bin)重构单菌基因组、基因组的物种分类鉴定与注释。
虽然在这三个方向，已经出现了大量软件，但由于缺少标准样品的评估体系，各软件的优缺点、适用范围至今没有系统的评估，用户使用中也极难选择。
今天介绍的这篇来自德国不伦瑞克市赫尔姆霍茨传染病研究中心(Helmholtz Centre for Infection Research)Alice C McHardy教授团队领导的研究成功建立了含有1300多种己知微生物基因组的标准品及数据集，成为目前本领域软件评估的金标准，对现在软件的系统评估，不仅对用户选择与使用有重要的指导意义，同时可以帮助本领域软件和算法的进一步优化和发展。本研究共有45家研究单位参与，本课题组也参与了标准品建立的部分工作。

目前文章已经被Nature Method接收，还末在线发表。预印本于2017年6月12日发表在bioRxiv上，截止9月10日，文章已经被摘要阅读8641次，PDF下载3784，google scholar统计引用10次。

Figure 1: Boxplots representing the fraction of reference genomes assembled by each assembler for the high complexity data set. (a): all genomes, (b): genomes with ANI >=95%, (c): genomes with ANI < 95%. Coloring indicates the results from the same assembler incorporated in different pipelines or with other parameter settings. (d): genome recovery fraction versus genome sequencing depth (coverage) for the high complexity data set. Data were classified as unique genomes (ANI < 95%, brown color), genomes with related strains present (ANI >= 95%, blue color) and high copy circular elements (green color). The gold standard includes all genomic regions covered by at least one read in the metagenome dataset, therefore the genome fraction for low abundance genomes can be less than 100%.

图2. 基因组分箱重建的纯度和完整度

(a) 不同软件在不同分类级别和不同错误矩阵下的相对表现。(b) 不同矩阵表现最好的前三个软件和得分。(c) 不同软件在不同分类级别的Recall和准确度下的绝对表现。

Figure 3: (a) Relative performance of profilers for different ranks and with different error metrics (weighted Unifrac, L1 norm, recall, precision, and false positives), shown here exemplarily for the microbial portion of the first high complexity sample. Each error metric was divided by its maximal value to facilitate viewing on the same scale and relative performance comparisons. A method’s name is given in red (with two asterisks) if it returned no predictions at the corresponding taxonomic rank. (b) Best scoring profilers using different performance metrics summed over all samples and taxonomic ranks to the genus level. A lower score indicates that a method was more frequently ranked highly for a particular metric. The maximum (worst) score for the Unifrac metric is 38 = (18 + 11 + 9) profiling submissions for the low, medium and high complexity datasets respectively), while the maximum score is 190 for all other metrics (= 5 taxonomic ranks * (18 + 11 + 9) profiling submissions for the low, medium and high complexity datasets respectively). (c) Absolute recall and precision for each profiler on the microbial (filtered) portion of the low complexity data set across six taxonomic ranks. Abbreviations are FS (FOCUS), T-P (Taxy-Pro), MP2.0(MetaPhlAn 2.0), MPr (Metaphyler), CK (Common Kmers) and D (DUDes).

表1. 序列组装软件评估结果