Molecular interaction networks for the analysis of human disease: Utility, limitations, and considerations

Schramm, Sarah-Jane; Jayaswal, Vivek; Goel, Apurv; Li, Simone S.; Yang, Yee Hwa; Mann, Graham J.; Wilkins, Marc R.

doi:10.1002/pmic.201200570

Cited by 14 publications

(9 citation statements)

References 129 publications

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“…Different types of analysis using interaction or even pathway data can be applied to analyse regulatory networks [83,84]. In many of these approaches, building and visualizing an interaction network is a required step, and there are several tools currently available for this task, using both command line or Graphical User Interface (GUI).…”

Section: Network Analysismentioning

confidence: 99%

Non-coding RNA regulatory networks

Panni

Lovering

Porras

et al. 2020

Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanism

334

202

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It is well established that the vast majority of human RNA transcripts do not encode for proteins and that non-coding RNAs regulate cell physiology and shape cellular functions. A subset of them are involved in gene regulation at different levels, from epigenetic gene silencing to post-transcriptional regulation of mRNA stability. Notably, the aberrant expression of many non-coding RNAs has been associated with aggressive pathologies. Rapid advances in network biology indicates that the robustness of cellular processes is the result of specific properties of biological networks such as scale-free degree distribution and hierarchical modularity, suggesting that regulatory network analyses could provide new insights on gene regulation and dysfunction mechanisms. In this study we present an overview of public repositories where non-coding RNA-regulatory interactions are collected and annotated, we discuss unresolved questions for data integration and we recall existing resources to build and analyse networks.

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Section: Network Analysismentioning

confidence: 99%

Non-coding RNA regulatory networks

Panni

Lovering

Porras

et al. 2020

Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanism

334

202

View full text Add to dashboard Cite

show abstract

“…However, there is still much to be improved. The PPI networks still hold a high bias in interactions due to annotation ( Schramm et al, 2013 ; Luecken et al, 2018 ) and will keep getting refined as our understanding of the biological systems increases. Better experimental techniques become more and more available, and data from those will need to be integrated for an even more comprehensive analysis ( Hasin et al, 2017 ; Yan et al, 2017 ; Karczewski and Snyder, 2018 ).…”

Section: Discussionmentioning

confidence: 99%

Network and Pathway Analysis of Toxicogenomics Data

Barel

Herwig

2018

Front. Genet.

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Toxicogenomics is the study of the molecular effects of chemical, biological and physical agents in biological systems, with the aim of elucidating toxicological mechanisms, building predictive models and improving diagnostics. The vast majority of toxicogenomics data has been generated at the transcriptome level, including RNA-seq and microarrays, and large quantities of drug-treatment data have been made publicly available through databases and repositories. Besides the identification of differentially expressed genes (DEGs) from case-control studies or drug treatment time series studies, bioinformatics methods have emerged that infer gene expression data at the molecular network and pathway level in order to reveal mechanistic information. In this work we describe different resources and tools that have been developed by us and others that relate gene expression measurements with known pathway information such as over-representation and gene set enrichment analyses. Furthermore, we highlight approaches that integrate gene expression data with molecular interaction networks in order to derive network modules related to drug toxicity. We describe the two main parts of the approach, i.e., the construction of a suitable molecular interaction network as well as the conduction of network propagation of the experimental data through the interaction network. In all cases we apply methods and tools to publicly available rat in vivo data on anthracyclines, an important class of anti-cancer drugs that are known to induce severe cardiotoxicity in patients. We report the results and functional implications achieved for four anthracyclines (doxorubicin, epirubicin, idarubicin, and daunorubicin) and compare the information content inherent in the different computational approaches.

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“…One of the most significant issues in PPI network analysis is the inaccuracy and the lack of reliability of the available networks. It has been noted that of the huge number of currently available networks, there is very little overlap and consistency between them [ 58 - 61 ]. Currently, the two main approaches for identification of protein-protein interactions are the yeast two-hybrid (Y2H) and the affinity purification of complexes followed by mass spectrometry (AP-MS).…”

Section: Discussionmentioning

confidence: 99%

Network-based biomarkers enhance classical approaches to prognostic gene expression signatures

et al. 2014

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BackgroundClassical approaches to predicting patient clinical outcome via gene expression information are primarily based on differential expression of unrelated genes (single-gene approaches) or genes related by, for example, biologic pathway or function (gene-sets). Recently, network-based approaches utilising interaction information between genes have emerged. An open problem is whether such approaches add value to the more traditional methods of signature modelling. We explored this question via comparison of the most widely employed single-gene, gene-set, and network-based methods, using gene expression microarray data from two different cancers: melanoma and ovarian. We considered two kinds of network approaches. The first of these identifies informative genes using gene expression and network connectivity information combined, the latter drawn from prior knowledge of protein-protein interactions. The second approach focuses on identification of informative sub-networks (small networks of interacting proteins, again from prior knowledge networks). For all methods we performed 100 rounds of 5-fold cross-validation under 3 different classifiers. For network-based approaches, we considered two different protein-protein interaction networks. We quantified resulting patterns of misclassification and discussed the relative value of each relative to ongoing development of prognostic biomarkers.ResultsWe found that single-gene, gene-set and network methods yielded similar error rates in melanoma and ovarian cancer data. Crucially, however, our novel and detailed patient-level analyses revealed that the different methods were correctly classifying alternate subsets of patients in each cohort. We also found that the network-based NetRank feature selection method was the most stable.ConclusionsNext-generation methods of gene expression signature modelling harness data from external networks and are foreshadowed as a standard mode of analysis. But what do they add to traditional approaches? Our findings indicate there is value in the way in which different subspaces of the patient sample are captured differently among the various methods, highlighting the possibility of 'combination' classifiers capable of identifying which patients will be more accurately classified by one particular method over another. We have seen this clearly for the first time because of our in-depth analysis at the level of individual patients.

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Molecular interaction networks for the analysis of human disease: Utility, limitations, and considerations

Cited by 14 publications

References 129 publications

Non-coding RNA regulatory networks

Non-coding RNA regulatory networks

Network and Pathway Analysis of Toxicogenomics Data

Network-based biomarkers enhance classical approaches to prognostic gene expression signatures

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