CoCoCoNet: conserved and comparative co-expression across a diverse set of species

Lee, John; Shah, Manthan; Ballouz, Sara; Crow, Megan; Gillis, Jesse

doi:10.1093/nar/gkaa348

Cited by 41 publications

(56 citation statements)

References 45 publications

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“…Reliable estimates of species-specific gene coexpression patterns are a necessary backbone for comparative analysis of regulatory divergence. We recently published a set of networks with our coexpression webserver, CoCoCoNet 21 , which contain RNA-seq data from 14 species across 895 datasets, and more than thirty-nine thousand individual samples ( Figure 1A ). Here, we establish the power and robustness of these networks by measuring the connectivity of genes with the same Gene Ontology (GO) annotations 31 , and by evaluating the stability of results after bootstrapping the network building process.…”

Section: Resultsmentioning

confidence: 99%

“…We breathe new life into this area by taking advantage of high-powered coexpression networks from animals, plants and yeast RNA-sequencing (RNA-seq) data 21 , as well as modern orthology prediction algorithms [22][23][24] , and measuring the conservation of coexpression relationships. As expected, we find that coexpression conservation tracks with evolutionary distances 25 , and is significantly higher for genes expressed in all cell types.…”

Section: Introductionmentioning

confidence: 99%

“…Aggregate coexpression networks were downloaded from CoCoCoNet21 . Networks for individual datasets are stored internally and are available on request.…”

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confidence: 99%

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Coexpression reveals conserved mechanisms of transcriptional cell identity

Crow

Suresh

et al. 2020

Preprint

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What makes a mouse a mouse, and not a hamster? The answer lies in the genome, and more specifically, in differences in gene regulation between the two organisms: where and when each gene is expressed. To quantify differences, a typical study will either compare functional genomics data from homologous tissues, limiting the approach to closely related species; or compare gene repertoires, limiting the resolution of the analysis to gross correlations between phenotypes and gene family size. As an alternative, gene coexpression networks provide a basis for studying the evolution of gene regulation without these constraints. By incorporating data from hundreds of independent experiments, meta-analytic coexpression networks reflect the convergent output of species-specific transcriptional regulation.In this work, we develop a measure of regulatory evolution based on gene coexpression. Comparing data from 14 species, we quantify the conservation of coexpression patterns 1) as a function of evolutionary time, 2) across orthology prediction algorithms, and 3) with reference to cell- and tissue-specificity. Strikingly, we uncover deeply conserved patterns of gradient-like expression across cell types from both the animal and plant kingdoms. These results suggest that ancient genes contribute to transcriptional cell identity through mechanisms that are independent of duplication and divergence.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Coexpression reveals conserved mechanisms of transcriptional cell identity

Crow

Suresh

et al. 2020

Preprint

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“…To estimate this coexpression, several methods have been proposed, including the Pearson correlation coefficient, the Spearman correlation coefficient, or a partial correlation coefficient ( 2 ). Although coexpression approaches to identify gene function have been used extensively in the model yeast Saccharomyces cerevisiae and humans ( 1 , 3 , 4 ), it has only recently been applied to full effect in other fungi, as in the recent work from Meyer and colleagues ( 5 , 6 ). Not only does coexpression have high predictive accuracy for gene function annotations, it also captures evolutionary-scale changes in cell identity ( 7 ).…”

Section: Introductionmentioning

confidence: 99%

DeORFanizing Candida albicans Genes using Coexpression

O’Meara

2021

mSphere

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Functional characterization of open reading frames in nonmodel organisms, such as the common opportunistic fungal pathogen Candida albicans, can be labor-intensive. To meet this challenge, we built a comprehensive and unbiased coexpression network for C. albicans, 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 Saccharomyces cerevisiae through orthology for additional accuracy in gene function prediction. To prospectively demonstrate the utility of the coexpression network in C. albicans, we predicted the function of underannotated open reading frames (ORFs) and identified CCJ1 as a novel cell cycle regulator in C. albicans. This study provides a tool for future systems biology analyses of gene function in C. albicans. We provide a computational pipeline for building and analyzing the coexpression network and CalCEN itself at http://github.com/momeara/CalCEN. IMPORTANCE Candida albicans 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 C. albicans 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/CCJ1 in cell cycle. This tool will allow for a better characterization of underannotated genes in pathogenic yeasts.

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“…To estimate this co-expression, several methods have been proposed, including the Pearson correlation coefficient, the Spearman correlation coefficient, or a partial correlation coefficient (2). Although co-expression approaches to identify gene function have been used extensively in the model yeast S. cerevisiae and humans (1,3,4), it has only recently been applied to full effect in other fungi, as in the recent work from Meyer and colleagues (5,6). Not only does co-expression have high predictive accuracy for gene function annotations, it also captures evolutionary-scale changes in cell identity (7).…”

Section: Introductionmentioning

confidence: 99%

DeORFanizingCandida albicansGenes using Co-Expression

O’Meara

2020

Preprint

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Functional characterization of open reading frames in non-model organisms, such as the common opportunistic fungal pathogen Candida albicans, can be labor intensive. To meet this challenge, we built a comprehensive and unbiased co-expression network for C. albicans, 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 Saccharomyces cerevisiae through orthology for additional accuracy in gene function prediction. To prospectively demonstrate the utility of the co-expression network in C. albicans, we predicted the function of under-annotated open reading frames (ORF)s and identified CCJ1 as a novel cell cycle regulator in C. albicans. This study provides a tool for future systems biology analyses of gene function in C. albicans. We provide a computational pipeline for building and analyzing the co-expression network and CalCEN itself at (http://github.com/momeara/CalCEN).ImportanceCandida albicans 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 RNAseq studies and generate a C. albicans co-expression 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/CCJ1 in cell cycle. This tool will allow for a better characterization of under-annotated genes in pathogenic yeasts.

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CoCoCoNet: conserved and comparative co-expression across a diverse set of species

Cited by 41 publications

References 45 publications

Coexpression reveals conserved mechanisms of transcriptional cell identity

Coexpression reveals conserved mechanisms of transcriptional cell identity

DeORFanizing Candida albicans Genes using Coexpression

DeORFanizingCandida albicansGenes using Co-Expression

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