From expression footprints to causal pathways: contextualizing large signaling networks with CARNIVAL

Liu, Anika; Trairatphisan, Panuwat; Gjerga, Enio; Didangelos, Athanasios; Barratt, Jonathan; Sáez-Rodríguez, Julio

doi:10.1038/s41540-019-0118-z

Cited by 129 publications

(176 citation statements)

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“…As we demonstrated here, OmniPath is able to provide the input knowledge for different data analysis tools, such as CellPhoneDB 6 , NicheNet 8 and CARNIVAL 43 to infer communication between and within cell types. OmniPath is not limited to literature curated interactions and it includes activity flow, kinase-substrate and ligand-receptor interactions without references as separate datasets, so that the users can decide which ones to use according to their purposes.…”

Section: Comprehensive Knowledge For Multicellular Omics Analysismentioning

confidence: 99%

Integrated intra- and intercellular signaling knowledge for multicellular omics analysis

Türei¹,

Valdeolivas²,

Gul

et al. 2020

Preprint

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Molecular knowledge of biological processes is a cornerstone in the analysis of omics data. Applied to single-cell data, such analyses can provide mechanistic insights into individual cells and their interactions. However, knowledge of intercellular communication is scarce, scattered across different resources, and not linked to intracellular processes. To address this gap, we combined over 100 resources in a single database. It covers the interactions and roles of proteins in inter- and intracellular signal transduction, as well as transcriptional and post-transcriptional regulation. We also provide a comprehensive collection of protein complexes and rich annotations on the properties of proteins, including function, localization, and role in diseases. The resource is available for human, and via homology translation for mouse and rat. The data is accessible via the web service of OmniPath, a Cytoscape plugin, and packages in R/Bioconductor and Python, providing convenient access options for both computational and experimental scientists. Our resource provides a single access point to knowledge spanning intra- and intercellular processes for data analysis, as we demonstrate in applications to study SARS-CoV-2 infection and ulcerative colitis.

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Section: Comprehensive Knowledge For Multicellular Omics Analysismentioning

confidence: 99%

Integrated intra- and intercellular signaling knowledge for multicellular omics analysis

Türei¹,

Valdeolivas²,

Gul

et al. 2020

Preprint

Self Cite

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“…If desired, a diffusion tool which does not need signed a priori interactions can be implemented to increase the input dataset size. Alternatively, a different method, such as an integer linear programming approach which identifies paths based on an optimisation problem (as implemented in CARNIVAL), could be used for network reconstruction (Liu et al 2019). In addition, integration of CARNIVAL could extend the workflow to permit network reconstruction without supplying upstream perturbations (in this case the viral-host protein interactions).…”

Section: Discussionmentioning

confidence: 99%

ViralLink: An integrated workflow to investigate the effect of SARS-CoV-2 on intracellular signalling and regulatory pathways

Treveil

Bohár

Sudhakar

et al. 2020

Preprint

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AbstractThe SARS-CoV-2 pandemic of 2020 has mobilised scientists around the globe to research all aspects of the coronavirus virus and its infection. For fruitful and rapid investigation of viral pathomechanisms, a collaborative and interdisciplinary approach is required. Therefore, we have developed ViralLink: a systems biology workflow which reconstructs and analyses networks representing the effect of viruses on intracellular signalling. These networks trace the flow of signal from intracellular viral proteins through their human binding proteins and downstream signalling pathways, ending with transcription factors regulating genes differentially expressed upon viral exposure. In this way, the workflow provides a mechanistic insight from previously identified knowledge of virally infected cells. By default, the workflow is set up to analyse the intracellular effects of SARS-CoV-2, requiring only transcriptomics counts data as input from the user: thus, encouraging and enabling rapid multidisciplinary research. However, the wide-ranging applicability and modularity of the workflow facilitates customisation of viral context, a priori interactions and analysis methods. Through a case study of SARS-CoV-2 infected bronchial/tracheal epithelial cells, we evidence the functionality of the workflow and its ability to identify key pathways and proteins in the cellular response to infection. The application of ViralLink to different viral infections in a cell-type specific manner using different available transcriptomics datasets will uncover key mechanisms in viral pathogenesis. The workflow is available on GitHub (https://github.com/korcsmarosgroup/ViralLink) in an easily accessible Python wrapper script, or as customisable modular R and Python scripts.Author summaryCollaborative and multidisciplinary science provides increased value for experimental datasets and speeds the process of discovery. Such ways of working are especially important at present due to the urgency of the SARS-CoV-2 pandemic. Here, we present a systems biology workflow which models the effect of viral proteins on the infected host cell, to aid collaborative and multidisciplinary research. Through integration of gene expression datasets with context-specific and context-agnostic molecular interaction datasets, the workflow can be easily applied to different datasets as they are made available. Application to diverse SARS-CoV-2 datasets will increase our understanding of the mechanistic details of the infection at a cell type specific level, aid drug target discovery and help explain the variety of clinical manifestations of the infection.

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“…We believe that combining gene expression, data driven inference of protein activity and network information (A. Liu et al 2019;Paull et al 2013) allows us to extract more accurate insights into signal transduction.…”

Section: Discussionmentioning

confidence: 99%

Why do pathway methods work better than they should?

Szalai

Sáez-Rodríguez

2020

Preprint

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Different pathway analysis methods are frequently applied to cancer gene expression data to identify dysregulated pathways. In most cases these methods infer pathway activity changes based on the gene expression of pathway members. However, pathways are constituted by signaling proteins, and their activity - not their abundance - defines the activity of the pathway; the association between gene expression and protein activity is in turn limited and not well characterised. Other methods infer pathway activity from the expression of the genes whose transcription is regulated by the pathway of interest, which seems a more adequate proxy of activity. Despite these potential limitations, membership based pathway methods are frequently used and often provide statistically significant results.Here, we submit that pathway based methods are not effective because of the correlation between the gene expression of pathway members and the activity of the pathway, but because pathway member gene sets overlap with the genes regulated by transcription factors (regulons). This implies that pathway methods do not inform about the activity of the pathway of interest, but instead the downstream effects of changes in the activities of transcription factors.To support our hypothesis, we show that the higher the overlap to transcription factor regulons, the higher the information value of pathway gene sets. Furthermore, removing these overlapping genes reduces the information content of pathway gene sets, but not vice versa. Our results suggest that results of classical pathway analysis methods should be interpreted with caution, and instead methods using pathway regulated genes for activity inference should be prioritised.Graphical abstract

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From expression footprints to causal pathways: contextualizing large signaling networks with CARNIVAL

Cited by 129 publications

References 50 publications

Integrated intra- and intercellular signaling knowledge for multicellular omics analysis

Integrated intra- and intercellular signaling knowledge for multicellular omics analysis

ViralLink: An integrated workflow to investigate the effect of SARS-CoV-2 on intracellular signalling and regulatory pathways

Why do pathway methods work better than they should?

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