Poretsky Elly, Huffaker Alisa
Division of Biology, University of California, San Diego, La Jolla, CA, USA.
PeerJ. 2020 Nov 9;8:e10264. doi: 10.7717/peerj.10264. eCollection 2020.
The rapid assignment of genotypes to phenotypes has been a historically challenging process. The discovery of genes encoding biosynthetic pathway enzymes for defined plant specialized metabolites has been informed and accelerated by the detection of gene clusters. Unfortunately, biosynthetic pathway genes are commonly dispersed across chromosomes or reside in genes clusters that provide little predictive value. More reliably, transcript abundance of genes underlying biochemical pathways for plant specialized metabolites display significant coregulation. By rapidly identifying highly coexpressed transcripts, it is possible to efficiently narrow candidate genes encoding pathway enzymes and more easily predict both functions and functional associations. Mutual Rank (MR)-based coexpression analyses in plants accurately demonstrate functional associations for many specialized metabolic pathways; however, despite the clear predictive value of MR analyses, the application is uncommonly used to drive new pathway discoveries. Moreover, many coexpression databases aid in the prediction of both functional associations and gene functions, but lack customizability for refined hypothesis testing. To facilitate and speed flexible MR-based hypothesis testing, we developed MutRank, an R Shiny web-application for coexpression analyses. MutRank provides an intuitive graphical user interface with multiple customizable features that integrates user-provided data and supporting information suitable for personal computers. Tabular and graphical outputs facilitate the rapid analyses of both unbiased and user-defined coexpression results that accelerate gene function predictions. We highlight the recent utility of MR analyses for functional predictions and discoveries in defining two maize terpenoid antibiotic pathways. Beyond applications in biosynthetic pathway discovery, MutRank provides a simple, customizable and user-friendly interface to enable coexpression analyses relating to a breadth of plant biology inquiries. Data and code are available at GitHub: https://github.com/eporetsky/MutRank.
将基因型快速对应到表型一直是一个具有历史挑战性的过程。基因簇的检测为确定植物特殊代谢产物生物合成途径酶的编码基因的发现提供了信息并加速了这一过程。不幸的是,生物合成途径基因通常分散在染色体上,或者存在于预测价值不大的基因簇中。更可靠的是,植物特殊代谢产物生化途径相关基因的转录丰度显示出显著的共调控。通过快速识别高度共表达的转录本,有可能有效地缩小编码途径酶的候选基因范围,并更容易预测功能和功能关联。基于互秩(MR)的植物共表达分析准确地证明了许多特殊代谢途径的功能关联;然而,尽管MR分析具有明确的预测价值,但该应用很少用于推动新途径的发现。此外,许多共表达数据库有助于预测功能关联和基因功能,但缺乏用于精细假设检验的可定制性。为了促进和加速基于MR的灵活假设检验,我们开发了MutRank,一个用于共表达分析的R Shiny网络应用程序。MutRank提供了一个直观的图形用户界面,具有多个可定制功能,集成了用户提供的数据和适合个人计算机的支持信息。表格和图形输出便于对无偏和用户定义的共表达结果进行快速分析,从而加速基因功能预测。我们强调了MR分析在定义两条玉米萜类抗生素途径中的功能预测和发现方面的最新效用。除了在生物合成途径发现中的应用外,MutRank还提供了一个简单、可定制且用户友好的界面,以进行与广泛的植物生物学研究相关的共表达分析。数据和代码可在GitHub上获取:https://github.com/eporetsky/MutRank。