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How difficult is inference of mammalian causal gene regulatory networks?

Djordjevic D, Yang A, Zadoorian A, Rungrugeecharoen K, Ho JW - PLoS ONE (2014)

Bottom Line: Our data have thorough annotation of tissue types and embryonic stages, as well as the type of regulation (activation, inhibition and no effect), which uniquely allows us to estimate both sensitivity and specificity of the inference of tissue specific causal GRN edges.Using these unprecedented datasets, we found that gene co-expression does not reliably distinguish true positive from false positive interactions, making inference of GRN in mammalian development very difficult.Our result supports the importance of using perturbation experimental data in causal network reconstruction.

View Article: PubMed Central - PubMed

Affiliation: Victor Chang Cardiac Research Institute, Sydney, New South Wales, Australia; The University of New South Wales, Sydney, New South Wales, Australia.

ABSTRACT
Gene regulatory networks (GRNs) play a central role in systems biology, especially in the study of mammalian organ development. One key question remains largely unanswered: Is it possible to infer mammalian causal GRNs using observable gene co-expression patterns alone? We assembled two mouse GRN datasets (embryonic tooth and heart) and matching microarray gene expression profiles to systematically investigate the difficulties of mammalian causal GRN inference. The GRNs were assembled based on > 2,000 pieces of experimental genetic perturbation evidence from manually reading > 150 primary research articles. Each piece of perturbation evidence records the qualitative change of the expression of one gene following knock-down or over-expression of another gene. Our data have thorough annotation of tissue types and embryonic stages, as well as the type of regulation (activation, inhibition and no effect), which uniquely allows us to estimate both sensitivity and specificity of the inference of tissue specific causal GRN edges. Using these unprecedented datasets, we found that gene co-expression does not reliably distinguish true positive from false positive interactions, making inference of GRN in mammalian development very difficult. Nonetheless, if we have expression profiling data from genetic or molecular perturbation experiments, such as gene knock-out or signalling stimulation, it is possible to use the set of differentially expressed genes to recover causal regulatory relationships with good sensitivity and specificity. Our result supports the importance of using perturbation experimental data in causal network reconstruction. Furthermore, we showed that causal gene regulatory relationship can be highly cell type or developmental stage specific, suggesting the importance of employing expression profiles from homogeneous cell populations. This study provides essential datasets and empirical evidence to guide the development of new GRN inference methods for mammalian organ development.

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Related in: MedlinePlus

CardiacCode is a public online resource allowing interactive visualisation of the heart GRN, and download of the heart data collected and used in this study.
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pone-0111661-g001: CardiacCode is a public online resource allowing interactive visualisation of the heart GRN, and download of the heart data collected and used in this study.

Mentions: In this study, we assembled two manually-curated mouse GRN datasets (embryonic development of tooth and heart), summarising experimental evidence for causal regulation (or lack of causal regulation) between 1,177 pairs of regulator-target gene pairs, and a compendium of matching microarray expression profiles, to systematically investigate the difficulties of GRN inference in mammalian cells, especially in the context of organ development. The tooth GRN and microarray dataset was downloaded from ToothCODE, and the data were generated to study epithelial-mesenchymal interactions during early tooth organogenesis [23]. It contains over 1,500 pieces of genetic perturbation evidence from 120 primary research papers, and 105 matching microarray profiles (Table 1). Using a similar curation approach, we specifically assembled the heart dataset for this study. We manually collected over 700 pieces of genetic perturbation evidence from 43 published primary research papers on in vivo mouse cardiac development. We complemented this with 86 microarray expression profiles from the GEO database. The curated perturbation dataset, the assembled microarray data, and the inferred cardiac development network (see below for more details on inference of mode of regulation) can be accessed through our newly developed interactive web resource, CardiacCode (Figure 1). It was built on an SQL database and interfacing with javascript and HTML5 through PHP. The network visualisation was supported by the cytoscape.js plugin (Figure 1). The tooth and heart GRN and microarray gene expression datasets are available via ToothCODE (http://compbio.med.harvard.edu/ToothCODE/) and CardiacCode (http://CardiacCode.victorchang.edu.au/).


How difficult is inference of mammalian causal gene regulatory networks?

Djordjevic D, Yang A, Zadoorian A, Rungrugeecharoen K, Ho JW - PLoS ONE (2014)

CardiacCode is a public online resource allowing interactive visualisation of the heart GRN, and download of the heart data collected and used in this study.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4219746&req=5

pone-0111661-g001: CardiacCode is a public online resource allowing interactive visualisation of the heart GRN, and download of the heart data collected and used in this study.
Mentions: In this study, we assembled two manually-curated mouse GRN datasets (embryonic development of tooth and heart), summarising experimental evidence for causal regulation (or lack of causal regulation) between 1,177 pairs of regulator-target gene pairs, and a compendium of matching microarray expression profiles, to systematically investigate the difficulties of GRN inference in mammalian cells, especially in the context of organ development. The tooth GRN and microarray dataset was downloaded from ToothCODE, and the data were generated to study epithelial-mesenchymal interactions during early tooth organogenesis [23]. It contains over 1,500 pieces of genetic perturbation evidence from 120 primary research papers, and 105 matching microarray profiles (Table 1). Using a similar curation approach, we specifically assembled the heart dataset for this study. We manually collected over 700 pieces of genetic perturbation evidence from 43 published primary research papers on in vivo mouse cardiac development. We complemented this with 86 microarray expression profiles from the GEO database. The curated perturbation dataset, the assembled microarray data, and the inferred cardiac development network (see below for more details on inference of mode of regulation) can be accessed through our newly developed interactive web resource, CardiacCode (Figure 1). It was built on an SQL database and interfacing with javascript and HTML5 through PHP. The network visualisation was supported by the cytoscape.js plugin (Figure 1). The tooth and heart GRN and microarray gene expression datasets are available via ToothCODE (http://compbio.med.harvard.edu/ToothCODE/) and CardiacCode (http://CardiacCode.victorchang.edu.au/).

Bottom Line: Our data have thorough annotation of tissue types and embryonic stages, as well as the type of regulation (activation, inhibition and no effect), which uniquely allows us to estimate both sensitivity and specificity of the inference of tissue specific causal GRN edges.Using these unprecedented datasets, we found that gene co-expression does not reliably distinguish true positive from false positive interactions, making inference of GRN in mammalian development very difficult.Our result supports the importance of using perturbation experimental data in causal network reconstruction.

View Article: PubMed Central - PubMed

Affiliation: Victor Chang Cardiac Research Institute, Sydney, New South Wales, Australia; The University of New South Wales, Sydney, New South Wales, Australia.

ABSTRACT
Gene regulatory networks (GRNs) play a central role in systems biology, especially in the study of mammalian organ development. One key question remains largely unanswered: Is it possible to infer mammalian causal GRNs using observable gene co-expression patterns alone? We assembled two mouse GRN datasets (embryonic tooth and heart) and matching microarray gene expression profiles to systematically investigate the difficulties of mammalian causal GRN inference. The GRNs were assembled based on > 2,000 pieces of experimental genetic perturbation evidence from manually reading > 150 primary research articles. Each piece of perturbation evidence records the qualitative change of the expression of one gene following knock-down or over-expression of another gene. Our data have thorough annotation of tissue types and embryonic stages, as well as the type of regulation (activation, inhibition and no effect), which uniquely allows us to estimate both sensitivity and specificity of the inference of tissue specific causal GRN edges. Using these unprecedented datasets, we found that gene co-expression does not reliably distinguish true positive from false positive interactions, making inference of GRN in mammalian development very difficult. Nonetheless, if we have expression profiling data from genetic or molecular perturbation experiments, such as gene knock-out or signalling stimulation, it is possible to use the set of differentially expressed genes to recover causal regulatory relationships with good sensitivity and specificity. Our result supports the importance of using perturbation experimental data in causal network reconstruction. Furthermore, we showed that causal gene regulatory relationship can be highly cell type or developmental stage specific, suggesting the importance of employing expression profiles from homogeneous cell populations. This study provides essential datasets and empirical evidence to guide the development of new GRN inference methods for mammalian organ development.

Show MeSH
Related in: MedlinePlus