Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, Michigan, USA
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
mSphere. 2021 Jan 20;6(1):e01245-20. doi: 10.1128/mSphere.01245-20.
Functional characterization of open reading frames in nonmodel organisms, such as the common opportunistic fungal pathogen , can be labor-intensive. To meet this challenge, we built a comprehensive and unbiased coexpression network for , which we call CalCEN, from data collected from 853 RNA sequencing runs from 18 large-scale studies deposited in the NCBI Sequence Read Archive. Retrospectively, CalCEN is highly predictive of known gene function annotations and can be synergistically combined with sequence similarity and interaction networks in through orthology for additional accuracy in gene function prediction. To prospectively demonstrate the utility of the coexpression network in , we predicted the function of underannotated open reading frames (ORFs) and identified as a novel cell cycle regulator in This study provides a tool for future systems biology analyses of gene function in We provide a computational pipeline for building and analyzing the coexpression network and CalCEN itself at http://github.com/momeara/CalCEN is a common and deadly fungal pathogen of humans, yet the genome of this organism contains many genes of unknown function. By determining gene function, we can help identify essential genes, new virulence factors, or new regulators of drug resistance, and thereby give new targets for antifungal development. Here, we use information from large-scale RNA sequencing (RNAseq) studies and generate a coexpression network (CalCEN) that is robust and able to predict gene function. We demonstrate the utility of this network in both retrospective and prospective testing and use CalCEN to predict a role for C4_06590W/ in cell cycle. This tool will allow for a better characterization of underannotated genes in pathogenic yeasts.
在非模式生物（如常见的机会性真菌病原体）中，功能表征的工作量很大。为了应对这一挑战，我们从 NCBI Sequence Read Archive 中 18 个大型研究中收集的 853 个 RNA 测序运行数据中构建了一个全面而无偏的共生网络，我们称之为 CalCEN。回顾性地，CalCEN 高度预测已知的基因功能注释，并可以通过序列相似性和相互作用网络与 CalCEN 协同结合，以提高基因功能预测的准确性。为了前瞻性地展示共生网络在 CalCEN 中的应用，我们预测了未注释的开放阅读框（ORFs）的功能，并鉴定了 C4_06590W/ 作为 CalCEN 中的新型细胞周期调节剂。这项研究为未来的 CalCEN 基因功能系统生物学分析提供了工具。我们提供了一个构建和分析共生网络和 CalCEN 本身的计算管道，网址为 http://github.com/momeara/。
CalCEN 是一种常见且致命的人类真菌病原体，但该生物体的基因组包含许多未知功能的基因。通过确定基因功能，我们可以帮助鉴定必需基因、新的毒力因子或新的药物耐药性调节剂，并为抗真菌药物的开发提供新的靶点。在这里，我们使用来自大规模 RNA 测序（RNAseq）研究的信息，并生成了一个稳健且能够预测基因功能的共生网络（CalCEN）。我们在回顾性和前瞻性测试中展示了该网络的实用性，并使用 CalCEN 预测 C4_06590W/ 在细胞周期中的作用。该工具将允许更好地描述致病性酵母中未注释的基因。
