背景:对于当前的宏基因组分类器来说,跟上基因组测序项目生成的训练数据的步伐是一个计算挑战,例如指数增长的NCBIRefSeq细菌基因组数据库。当将新的参考序列添加到训练数据时,静态训练的分类器必须在所有数据上重新运行,导致一个非常低效的过程。与使用所有数据重新训练分类器相比,“增量学习”的丰富文献解决了更新现有分类器以适应新数据的需求,而不会牺牲太多准确性。
结果:我们通过在渐进式RefSeq快照上递增地训练分类器并在(a)所有已知的当前基因组(作为地面实况集)和(b)真实的实验性宏基因组肠道样本上进行测试,证明了分类如何随着时间的推移而改善。我们证明,随着分类器模型的基因组知识的增长,分类精度提高。概念验证朴素贝叶斯实现,当每年更新时,现在运行在1/4的非增量时间没有精度损失。
结论:很明显,通过掌握最新知识,分类得到了改善。因此,最重要的是使分类器易于计算以跟上数据泛滥。增量学习分类器可以被有效地更新,而无需重新处理的成本,也无需访问现有数据库,因此节省了存储和计算资源。
BACKGROUND: It is a computational challenge for current metagenomic classifiers to keep up with the pace of training data generated from genome sequencing projects, such as the exponentially-growing NCBI
RefSeq bacterial genome database. When new reference sequences are added to training data, statically trained classifiers must be rerun on all data, resulting in a highly inefficient process. The rich literature of \"incremental learning\" addresses the need to update an existing classifier to accommodate new data without sacrificing much accuracy compared to retraining the classifier with all data.
RESULTS: We demonstrate how classification improves over time by incrementally training a classifier on progressive
RefSeq snapshots and testing it on: (a) all known current genomes (as a ground truth set) and (b) a real experimental metagenomic gut sample. We demonstrate that as a classifier model\'s knowledge of genomes grows, classification accuracy increases. The proof-of-concept naïve Bayes implementation, when updated yearly, now runs in 1/4th of the non-incremental time with no accuracy loss.
CONCLUSIONS: It is evident that classification improves by having the most current knowledge at its disposal. Therefore, it is of utmost importance to make classifiers computationally tractable to keep up with the data deluge. The incremental learning classifier can be efficiently updated without the cost of reprocessing nor the access to the existing database and therefore save storage as well as computation resources.