Reactome graph database: Efficient access to complex pathway data

Fabregat, Antonio; Korninger, Florian; Viteri, Guilherme; Sidiropoulos, Konstantinos; Marín-Garcia, Pablo; Ping, Peipei; Wu, Guanming; Stein, Lincoln; D'Eustachio, P; Hermjakob, Henning

doi:10.1371/journal.pcbi.1005968

Cited by 233 publications

(207 citation statements)

References 13 publications

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“…S5). Finally, the algorithm updates the dist dictionary for all nodes that are reached in the k-th iteration and increments k (lines [14][15][16][17]. This implementation keeps track of B 0 , B 1 , .…”

Section: Parameterized Hypergraph Connectivitymentioning

confidence: 99%

“…Many pathway databases [13][14][15][16][17][18][19][20] have adopted reaction-centric signaling pathway formats such as the Biological Pathway Exchange (BioPAX) [21], which provides more mechanistic information about the interactions. As reactioncentric information has become available, many modeling frameworks have been proposed to overcome the limitations of graphs for analyzing signaling pathway structure [8,9,22,23].…”

Section: Introductionmentioning

confidence: 99%

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Connectivity Measures for Signaling Pathway Topologies

Franzese

Groce

Murali

et al. 2019

Preprint

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Characterizing cellular responses to different extrinsic signals is an active area of research, and curated pathway databases describe these complex signaling reactions. Here, we revisit a fundamental question in signaling pathway analysis: are two molecules "connected" in a network? This question is the first step towards understanding the potential influence of molecules in a pathway, and the answer depends on the choice of modeling framework. We examined the connectivity of Reactome signaling pathways using four different pathway representations. We find that Reactome is very well connected as a graph, moderately well connected as a compound graph or bipartite graph, and poorly connected as a hypergraph (which captures many-to-many relationships in reaction networks). We present a novel relaxation of hypergraph connectivity that iteratively increases connectivity from a node while preserving the hypergraph topology. This measure, B-relaxation distance, provides a parameterized transition between hypergraph connectivity and graph connectivity. B-relaxation distance is sensitive to the presence of small molecules that participate in many functionally unrelated reactions in the network. We also define a score that quantifies one pathway's downstream influence on another, which can be calculated as B-relaxation distance gradually relaxes the connectivity constraint in hypergraphs. Computing this score across all pairs of 34 Reactome pathways reveals two case studies of pathway influence, and we describe the specific reactions that contribute to the large influence score. Our method lays the groundwork for other generalizations of graph-theoretic concepts to hypergraphs in order to facilitate signaling pathway analysis.

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Section: Parameterized Hypergraph Connectivitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Connectivity Measures for Signaling Pathway Topologies

Franzese

Groce

Murali

et al. 2019

Preprint

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“…Enriched cellular processes and molecular functions from Gene Ontology with FDR<0.05 calculated from the Fisher's Exact test were reported. Network interactions were clustered using Cytoscape based on gene functional annotations in the reactome pathways (Fabregat et al 2018).…”

Section: Functional Enrichment For Candidate Dnmt1 Dependent Genes Inmentioning

confidence: 99%

The conserved DNMT1 dependent methylation regions in human cells are vulnerable to environmental rotenone

Freeman

Lou

et al. 2019

Preprint

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Allele-specific DNA methylation (ASM) describes genomic loci that maintain CpG methylation at only one inherited allele rather than having coordinated methylation across both alleles. The most prominent of these regions are germline ASMs (gASMs) that control the expression of imprinted genes in a parent of origindependent manner and are associated with disease. However, our recent report reveals numerous ASMs at non-imprinted genes. These non-germline ASMs are dependent on DNA methyltransferase 1 (DNMT1) and strikingly show the feature of random, switchable monoallelic methylation patterns in the mouse genome. The significance of these ASMs to human health has not been explored. Due to their shared allelicity with gASMs, herein, we propose that non-traditional ASMs are sensitive to exposures in association with human disease. We first explore their conservancy in the human genome. Our data show that our putative non-germline ASMs were in conserved regions of the human genome and located adjacent to genes vital for neuronal development and maturation. We next tested the hypothesized vulnerability of these regions by exposing human embryonic kidney cell HEK293 with the neurotoxicant rotenone for 24h. Indeed,14 genes adjacent to our identified regions were differentially expressed from RNA-sequencing. We analyzed the base-resolution methylation patterns of the predicted non-germline ASMs at two neurological genes, HCN2 and NEFM, with potential to increase the risk of neurodegeneration. Both regions were significantly hypomethylated in response to rotenone. Our data indicate that non-germline ASMs seem conserved between mouse and human genomes, overlap important regulatory factor binding motifs, and regulate the expression of genes vital to neuronal function. These results support the notion that ASMs are sensitive to environmental factors and may alter the risk of neurological disease later in life by disrupting neuronal development.

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“…The metrics presented in this manuscript were obtained by querying the Reactome graph database directly [22]. The queries used can be found in the online documentation at:…”

Section: Metrics and Figuresmentioning

confidence: 99%

PathwayMatcher: proteoform-centric network construction enables fine-granularity multi-omics pathway mapping

Sánchez

Burger

Horro

et al. 2018

Preprint

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BackgroundMapping biomedical data to functional knowledge is an essential task in bioinformatics and can be achieved by querying identifiers, e.g. gene sets, in pathway knowledgebases. However, the isoform and post-translational modification states of proteins are lost when converting input and pathways into gene-centric lists.FindingsBased on the Reactome knowledgebase, we built a network of protein-protein interactions accounting for the documented isoform and modification statuses of proteins. We then implemented a command line application called PathwayMatcher (github.com/PathwayAnalysisPlatform/PathwayMatcher) to query this network. PathwayMatcher supports multiple types of omics data as input, and outputs the possibly affected biochemical reactions, subnetworks, and pathways.ConclusionsPathwayMatcher enables refining the network-representation of pathways by including isoform and post-translational modifications. The specificity of pathway analyses is hence adapted to different levels of granularity and it becomes possible to distinguish interactions between different forms of the same protein.

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Reactome graph database: Efficient access to complex pathway data

Cited by 233 publications

References 13 publications

Connectivity Measures for Signaling Pathway Topologies

Connectivity Measures for Signaling Pathway Topologies

The conserved DNMT1 dependent methylation regions in human cells are vulnerable to environmental rotenone

PathwayMatcher: proteoform-centric network construction enables fine-granularity multi-omics pathway mapping

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