Animals, plants, and algae rely on symbiotic microorganisms for their development and functioning. Genome sequencing and genomic analyses of these microorganisms provide opportunities to construct metabolic networks and to analyze the metabolism of the symbiotic communities they constitute. Genome-scale metabolic network reconstructions rest on information gained from genome annotation. As there are multiple annotation pipelines available, the question arises to what extent differences in annotation pipelines impact outcomes of these analyses. Here, we compare five commonly used pipelines (Prokka, MaGe, IMG, DFAST, RAST) from predicted annotation features (coding sequences, Enzyme Commission numbers, hypothetical proteins) to the metabolic network-based analysis of symbiotic communities (biochemical reactions, producible compounds, and selection of minimal complementary bacterial communities). While Prokka and IMG produced the most extensive networks, RAST and DFAST networks produced the fewest false positives and the most connected networks with the fewest dead-end metabolites. Our results underline differences between the outputs of the tested pipelines at all examined levels, with small differences in the draft metabolic networks resulting in the selection of different microbial consortia to expand the metabolic capabilities of the algal host. However, the consortia generated yielded similar predicted producible compounds and could therefore be considered functionally interchangeable. This contrast between selected communities and community functions depending on the annotation pipeline needs to be taken into consideration when interpreting the results of metabolic complementarity analyses. In the future, experimental validation of bioinformatic predictions will likely be crucial to both evaluate and refine the pipelines and needs to be coupled with increased efforts to expand and improve annotations in reference databases.

中文翻译：

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基于不同注释管道的藻类微生物群落代谢预测的稳健性分析

动物，植物和藻类依靠共生微生物来发育和发挥功能。这些微生物的基因组测序和基因组分析为构建代谢网络和分析它们构成的共生群落的代谢提供了机会。基因组规模的代谢网络重建基于从基因组注释中获得的信息。由于存在多个注释管线，因此出现了一个问题，即注释管线中的差异在多大程度上影响了这些分析的结果。在这里，我们将五个常用的管道（Prokka，MaGe，IMG，DFAST，RAST）从预测的注释特征（编码序列，酶委员会编号，假设的蛋白质）与基于代谢网络的共生群落分析（生化反应，可生产的化合物）进行了比较，和选择最小的互补细菌群落）。虽然Prokka和IMG产生了最广泛的网络，但是RAST和DFAST网络产生了最少的假阳性，并且连接最多的网络具有最少的代谢产物。我们的结果强调了在所有检查水平下测试管道的输出之间的差异，新陈代谢网络草案中的细微差异导致选择了不同的微生物聚生体以扩大藻类宿主的代谢能力。但是，产生的财团产生了相似的预测可生产化合物，因此可以认为在功能上是可互换的。在解释代谢互补性分析的结果时，需要考虑所选社区和社区功能之间的差异，具体取决于注释流水线。将来，对生物信息学预测进行实验验证对于评估和完善管道至关重要，并且需要结合更多的努力来扩展和改进参考数据库中的注释。