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Small Genomes and Big Data: Adaptation of Plastid Genomics to the High-Throughput Era

机译:小基因组和大数据:质体基因组学适应高通量时代

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摘要

Plastid genome sequences are becoming more readily available with the increase in high-throughput sequencing, and whole-organelle genetic data is available for algae and plants from across the diversity of photosynthetic eukaryotes. This has provided incredible opportunities for studying species which may not be amenable to in vivo study or genetic manipulation or may not yet have been cultured. Research into plastid genomes has pushed the limits of what can be deduced from genomic information, and in particular genomic information obtained from public databases. In this Review, we discuss how research into plastid genomes has benefitted enormously from the explosion of publicly available genome sequence. We describe two case studies in how using publicly available gene data has supported previously held hypotheses about plastid traits from lineage-restricted experiments across algal and plant diversity. We propose how this approach could be used across disciplines for inferring functional and biological characteristics from genomic approaches, including integration of new computational and bioinformatic approaches such as machine learning. We argue that the techniques developed to gain the maximum possible insight from plastid genomes can be applied across the eukaryotic tree of life.
机译:随着高通量测序的增加,质体基因组序列变得越来越容易获得,并且藻类和植物的全细胞器遗传数据可用于多种光合真核生物。这为研究可能不适合进行体内研究或基因操作或尚未培养的物种提供了令人难以置信的机会。对质体基因组的研究已经突破了可以从基因组信息,特别是从公共数据库获得的基因组信息推论的极限。在这篇综述中,我们讨论了质体基因组的研究如何从公开可用的基因组序列的爆炸中受益匪浅。我们描述了两个案例研究,说明如何使用可公开获得的基因数据来支持先前关于藻类和植物多样性的谱系限制实验中关于质体性状的假说。我们提出如何将此方法跨学科用于从基因组方法中推断功能和生物学特征,包括集成新的计算方法和生物信息学方法,例如机器学习。我们认为,为从质体基因组中获得最大可能的见识而开发的技术可以应用于整个真核生物树。

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