近年来基因组技术的进展较大的加速了基础植物科学和应用育种研究的进程。同时，高通量植物表型鉴定正越来越多的被应用于植物群落，有望缓解表型研究的瓶颈。当这些技术突破显著加快QTL和目的基因鉴定时，实施较为复杂的试验分析仍然值得挑战。尤其是，当依赖于植物生理学鉴定时，需要特别地关注标准化、描述和实施试验。本文主要综述了目前的基因组组装技术水平以及对未来植物泛基因组研究的展望。作者还讨论了利用植物表型试验的较低限度信息MIAPPE来标准化、描述表型研究的重要性，这些标准有利于以后的研究人员对于表型数据的再利用和整合。另外，本文还表明深度表型数据挖掘可能会产生新的性状-性状关联，同时回顾了如何将表型数据与基因组数据进行关联。较终，作者对未来的机器学习以及其在关联表型和基因组信息方面的作用发表了一些看法。

Computational aspects underlying genome to phenome analysis in plants

First Author: Anthony M. Bolger; Affiliation: RWTH Aachen University ,Aachen, Germany

Corresponding author: Recent advances in genomics technologies have greatly accelerated progress in both fundamental plant science and applied breeding research. Concurrently, high throughput plant phenotyping is becoming widely adopted in the plant community, promising to alleviate (缓解) the phenotypic bottleneck. Whilst these technological breakthroughs are significantly accelerating QTL and causal gene identification, challenges to enable even more sophisticated analyses remain. In particular, care needs to be taken to standardize, describe and conduct experiments robustly while relying on plant physiology expertise. Here we review the state of the art regarding genome assembly and the future potential of pangenomics in plant research. We also describe the necessity of standardizing and describing phenotypic studies using the Minimum Information About a Plant Phenotyping Experiment (MIAPPE) standard in order to enable the reuse and integration of phenotypic data. In addition, we show how deep phenotypic data might yield novel trait‐ rait correlations and review how to link phenotypic data to genomic data. Finally, we provide perspectives on the golden future of machine learning and their potential in linking phenotypes to genomic features.