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tRNA functional signatures classify plastids as late-branching cyanobacteria.
BMC Ecology and Evolution ( IF 2.3 ) Pub Date : 2019-12-09 , DOI: 10.1186/s12862-019-1552-7
Travis J Lawrence 1, 2 , Katherine Ch Amrine 2, 3 , Wesley D Swingley 4 , David H Ardell 2, 5
Affiliation  

BACKGROUND Eukaryotes acquired the trait of oxygenic photosynthesis through endosymbiosis of the cyanobacterial progenitor of plastid organelles. Despite recent advances in the phylogenomics of Cyanobacteria, the phylogenetic root of plastids remains controversial. Although a single origin of plastids by endosymbiosis is broadly supported, recent phylogenomic studies are contradictory on whether plastids branch early or late within Cyanobacteria. One underlying cause may be poor fit of evolutionary models to complex phylogenomic data. RESULTS Using Posterior Predictive Analysis, we show that recently applied evolutionary models poorly fit three phylogenomic datasets curated from cyanobacteria and plastid genomes because of heterogeneities in both substitution processes across sites and of compositions across lineages. To circumvent these sources of bias, we developed CYANO-MLP, a machine learning algorithm that consistently and accurately phylogenetically classifies ("phyloclassifies") cyanobacterial genomes to their clade of origin based on bioinformatically predicted function-informative features in tRNA gene complements. Classification of cyanobacterial genomes with CYANO-MLP is accurate and robust to deletion of clades, unbalanced sampling, and compositional heterogeneity in input tRNA data. CYANO-MLP consistently classifies plastid genomes into a late-branching cyanobacterial sub-clade containing single-cell, starch-producing, nitrogen-fixing ecotypes, consistent with metabolic and gene transfer data. CONCLUSIONS Phylogenomic data of cyanobacteria and plastids exhibit both site-process heterogeneities and compositional heterogeneities across lineages. These aspects of the data require careful modeling to avoid bias in phylogenomic estimation. Furthermore, we show that amino acid recoding strategies may be insufficient to mitigate bias from compositional heterogeneities. However, the combination of our novel tRNA-specific strategy with machine learning in CYANO-MLP appears robust to these sources of bias with high accuracy in phyloclassification of cyanobacterial genomes. CYANO-MLP consistently classifies plastids as late-branching Cyanobacteria, consistent with independent evidence from signature-based approaches and some previous phylogenetic studies.

中文翻译:

tRNA功能特征将质体归类为后期分支的蓝细菌。

背景技术真核生物通过质体细胞器蓝细菌祖细胞的共生共生获得了光合作用的特征。尽管在蓝细菌的系统发育组学方面有最新进展,但质体的系统发育根源仍存在争议。尽管广泛支持由内共生引起的质体的单一来源,但是最近的系统生物学研究对在蓝细菌中质体是早分支还是晚分支是矛盾的。一个潜在的原因可能是进化模型与复杂的植物统计学数据的拟合度很差。结果使用后验预测分析,我们表明,最近应用的进化模型不能很好地拟合从蓝细菌和质体基因组收集的三个系统基因组数据集,这是因为跨位点和跨谱系的替换过程均存在异质性。为了避免这些偏见,我们开发了CYANO-MLP,这是一种机器学习算法,可根据tRNA基因补体中的生物信息学预测的功能信息特征,对蓝细菌基因组进行一致且准确的系统分类(“系统分类”)蓝细菌基因组。使用CYANO-MLP对蓝细菌基因组进行分类对于输入进化的tRNA数据中的进化枝删除,不平衡采样以及成分异质性而言是准确而稳健的。CYANO-MLP始终将质体基因组分类为后期分支的蓝细菌亚分支,该分支包含单细胞,生产淀粉的固氮生态型,与代谢和基因转移数据一致。结论蓝细菌和质体的系统生物学数据显示了谱系间的位点过程异质性和组成异质性。数据的这些方面都需要仔细建模,以避免系统生物学估计上的偏差。此外,我们表明氨基酸编码策略可能不足以减轻成分异质性的偏见。但是,将我们新颖的tRNA特异性策略与CYANO-MLP中的机器学习相结合,对于这些偏倚来源似乎很健壮,并且在蓝细菌基因组的系统分类上具有很高的准确性。CYANO-MLP始终将质体归类为迟分支蓝细菌,这与基于特征的方法和一些先前的系统发育研究的独立证据一致。数据的这些方面都需要仔细建模,以避免系统生物学估计上的偏差。此外,我们表明氨基酸编码策略可能不足以减轻成分异质性的偏见。但是,将我们新颖的tRNA特异性策略与CYANO-MLP中的机器学习相结合,对于这些偏倚来源似乎很可靠,并且在蓝细菌基因组的系统分类上具有很高的准确性。CYANO-MLP始终将质体归类为迟分支蓝细菌,这与基于特征的方法和一些先前的系统发育研究的独立证据一致。数据的这些方面都需要仔细建模,以避免系统生物学估计上的偏差。此外,我们表明氨基酸编码策略可能不足以减轻成分异质性的偏见。但是,将我们新颖的tRNA特异性策略与CYANO-MLP中的机器学习相结合,对于这些偏倚来源似乎很可靠,并且在蓝细菌基因组的系统分类上具有很高的准确性。CYANO-MLP始终将质体归类为迟分支蓝细菌,这与基于特征的方法和一些先前的系统发育研究的独立证据一致。在CYANO-MLP中,我们新颖的tRNA特异性策略与机器学习的结合对于蓝藻基因组系统分类的这些偏见来源具有很高的准确性。CYANO-MLP始终将质体归类为迟分支蓝细菌,这与基于特征的方法和一些先前的系统发育研究的独立证据一致。在CYANO-MLP中,我们新颖的tRNA特异性策略与机器学习的结合对于蓝藻基因组的系统分类在高精度上对这些偏见来源表现出强大的作用。CYANO-MLP始终将质体归类为迟分支蓝细菌,这与基于特征的方法和一些先前的系统发育研究的独立证据一致。
更新日期:2019-12-09
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