WikiPathways: capturing the full diversity of pathway knowledge

Kutmon, Martina; Riutta, Anders; Nunes, Nuno; Hanspers, Kristina; Willighagen, Egon; Bohler, Anwesha; Mélius, Jonathan; Waagmeester, Andra; Sinha, Sravanthi R.; Miller, Ryan; Coort, Susan L.; Cirillo, Elisa; Smeets, Bart; Evelo, Chris T.; Pico, Alexander R.

doi:10.1093/nar/gkv1024

Cited by 391 publications

(316 citation statements)

References 23 publications

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“…S7). These DEGs were enriched in the WikiPathways (Kutmon et al 2016) such as PluriNetWork, Focal Adhesion, MAPK signaling pathway, and Wnt signaling pathway (adjusted P < 0.01) (Fig. 5B).…”

Section: Transcriptional Signature Underlying the Delayed Progressionmentioning

confidence: 99%

Single-cell analyses of X Chromosome inactivation dynamics and pluripotency during differentiation

et al. 2016

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Pluripotency, differentiation, and X Chromosome inactivation (XCI) are key aspects of embryonic development. However, the underlying relationship and mechanisms among these processes remain unclear. Here, we systematically dissected these features along developmental progression using mouse embryonic stem cells (mESCs) and single-cell RNA sequencing with allelic resolution. We found that mESCs grown in a ground state 2i condition displayed transcriptomic profiles diffused from preimplantation mouse embryonic cells, whereas EpiStem cells closely resembled the post-implantation epiblast. Sex-related gene expression varied greatly across distinct developmental states. We also identified novel markers that were highly enriched in each developmental state. Moreover, we revealed that several novel pathways, including PluriNetWork and Focal Adhesion, were responsible for the delayed progression of female EpiStem cells. Importantly, we "digitalized" XCI progression using allelic expression of active and inactive X Chromosomes and surprisingly found that XCI states exhibited profound variability in each developmental state, including the 2i condition. XCI progression was not tightly synchronized with loss of pluripotency and increase of differentiation at the single-cell level, although these processes were globally correlated. In addition, highly expressed genes, including core pluripotency factors, were in general biallelically expressed. Taken together, our study sheds light on the dynamics of XCI progression and the asynchronicity between pluripotency, differentiation, and XCI.

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Section: Transcriptional Signature Underlying the Delayed Progressionmentioning

confidence: 99%

Single-cell analyses of X Chromosome inactivation dynamics and pluripotency during differentiation

et al. 2016

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“…Integrates different -omics levels by relying on existing biological knowledge gathered from metabolic pathways such as Kegg and wikipathways (Kutmon et al 2015) InCroMAP and IMPALA for integrated pathway-based analysis Eichner et al (2014), Kamburov et al (2011) SAMNetWeb to generate biological networks with transcriptomics and proteomics data Gosline et al (2015) MetScape cytoscape plugin to produce metabolic networks from transcriptomics and metabolite data Karnovsky et al (2012) …”

Section: Pathway-based Integrationmentioning

confidence: 99%

Systems biology approaches to study the molecular effects of caloric restriction and polyphenols on aging processes

et al. 2015

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Worldwide population is aging, and a large part of the growing burden associated with age-related conditions can be prevented or delayed by promoting healthy lifestyle and normalizing metabolic risk factors. However, a better understanding of the pleiotropic effects of available nutritional interventions and their influence on the multiple processes affected by aging is needed to select and implement the most promising actions. New methods of analysis are required to tackle the complexity of the interplay between nutritional interventions and aging, and to make sense of a growing amount of -omics data being produced for this purpose. In this paper, we review how various systems biology-inspired methods of analysis can be applied to the study of the molecular basis of nutritional interventions promoting healthy aging, notably caloric restriction and polyphenol supplementation. We specifically focus on the role that different versions of network analysis, molecular signature identification and multiomics data integration are playing in elucidating the complex mechanisms underlying nutrition, and provide some examples on how to extend the application of these methods using available microarray data.

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“…The philosophy of this task group is to leverage upon the critical mass of knowledge and activity that already exists for model organisms, linking to these other efforts and exploiting resources such as sequenced genomes to predict metabolism (as targets for experimental investigation) using genome-wide metabolic reconstructions or metabolic pathway databases KEGG 12 , BioCyc 13 , and WikiPathways 21 . The task group has set a grand challenge for the community, to identify and map all metabolites onto metabolic pathways, to develop quantitative metabolic models for model organisms, and to relate organism metabolic pathways within the context of evolutionary metabolomics 22 .…”

Section: Identification Of Challengesmentioning

confidence: 99%

The future of metabolomics in ELIXIR

Rijswijk

Beirnaert

Caron³

et al. 2017

F1000Res

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Metabolomics, the youngest of the major omics technologies, is supported by an active community of researchers and infrastructure developers across Europe. To coordinate and focus efforts around infrastructure building for metabolomics within Europe, a workshop on the “Future of metabolomics in ELIXIR” was organised at Frankfurt Airport in Germany. This one-day strategic workshop involved representatives of ELIXIR Nodes, members of the PhenoMeNal consortium developing an e-infrastructure that supports workflow-based metabolomics analysis pipelines, and experts from the international metabolomics community. The workshop established metabolite identification as the critical area, where a maximal impact of computational metabolomics and data management on other fields could be achieved. In particular, the existing four ELIXIR Use Cases, where the metabolomics community - both industry and academia - would benefit most, and which could be exhaustively mapped onto the current five ELIXIR Platforms were discussed. This opinion article is a call for support for a new ELIXIR metabolomics Use Case, which aligns with and complements the existing and planned ELIXIR Platforms and Use Cases.

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WikiPathways: capturing the full diversity of pathway knowledge

Cited by 391 publications

References 23 publications

Single-cell analyses of X Chromosome inactivation dynamics and pluripotency during differentiation

Single-cell analyses of X Chromosome inactivation dynamics and pluripotency during differentiation

Systems biology approaches to study the molecular effects of caloric restriction and polyphenols on aging processes

The future of metabolomics in ELIXIR

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