REACTOME: a knowledgebase of biological pathways

Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
Nucleic Acids Research (Impact Factor: 9.11). 01/2005; 33(Database issue):428-432. DOI: 10.1093/nar/gki072
Source: DBLP


Reactome, located at is a curated, peer-reviewed resource of human biological processes. Given the genetic makeup of an organism, the complete
set of possible reactions constitutes its reactome. The basic unit of the Reactome database is a reaction; reactions are then
grouped into causal chains to form pathways. The Reactome data model allows us to represent many diverse processes in the
human system, including the pathways of intermediary metabolism, regulatory pathways, and signal transduction, and high-level
processes, such as the cell cycle. Reactome provides a qualitative framework, on which quantitative data can be superimposed.
Tools have been developed to facilitate custom data entry and annotation by expert biologists, and to allow visualization
and exploration of the finished dataset as an interactive process map. Although our primary curational domain is pathways
from Homo sapiens, we regularly create electronic projections of human pathways onto other organisms via putative orthologs, thus making Reactome
relevant to model organism research communities. The database is publicly available under open source terms, which allows
both its content and its software infrastructure to be freely used and redistributed.

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Available from: Suzanna Elaine Lewis, Oct 04, 2015
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    • "The data flow process, as shown in Fig. 1, starts with the selection of a pathway from online databases. These pathways are manually curated and a large collection is classified and available online from many repositories [5] [6]. A useful tool which is able to download a biological pathway, enhance it, and provide as output a boolean network topology representation is a Cytoscape plugin named ReNE [30]. "
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    ABSTRACT: Gene Regulatory Networks (GRNs) are one of the most investigated biological networks in Systems Biology because their work involves all living activities in the cell. A powerful but simple model of such GRNs are Boolean Networks (BN) that describe interactions among biological compounds in a qualitative manner. One of the most interesting outcomes about GRNs's dynamics are the so called network attractors, since they seem to well represent the stable states of a living cell. Though collecting state space trajectories is a quite simple task when the network topology consists of few nodes, it becomes not so trivial when nodes are of the size of hundreds or thousands. Thus, we exploit the MapReduce algorithm in order to cope this complexity on a cloud architecture built for the purpose. We found that scaling-out the problem is a better solution rather than increasing resources on single machine, thus allowing simulations of large networks.
    BIOICT Workshop @ Embedded Computing (MECO), 2015 4th Mediterranean Conference on, Budva, Montenegro; 06/2015
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    • "Similarly, pathway analysis was used to find out the significant pathway of the differential genes according to KEGG, Biocarta and Reactome [10,16,17]. Still, we turn to the Fisher’s exact test and χ2 test to select the significant pathway, and the threshold of significance was defined by P-value and FDR. "
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    ABSTRACT: Background Cytokine-induced killer (CIK) cells are an emerging approach of cancer treatment. Our previous study have shown that CIK cells stimulated with combination of IL-2 and IL-15 displayed improved proliferation capacity and tumor cytotoxicity. However, the mechanisms of CIK cell proliferation and acquisition of cytolytic function against tumor induced by IL-2 and IL-15 have not been well elucidated yet. Methods CIKIL-2 and CIKIL-15 were generated from peripheral blood mononuclear cells primed with IFN-γ, and stimulated with IL-2 and IL-15 in combination with OKT3 respectively. RNA-seq was performed to identify differentially expressed genes, and gene ontology and pathways based analysis were used to identify the distinct roles of IL-2 and IL-15 in CIK preparation. Results The results indicated that CIKIL-15 showed improved cell proliferation capacity compared to CIKIL-2. However, CIKIL-2 has exhibited greater tumor cytotoxic effect than CIKIL-15. Employing deep sequencing, we sequenced mRNA transcripts in CIKIL-2 and CIKIL-15. A total of 374 differentially expressed genes (DEGs) were identified including 175 up-regulated genes in CIKIL-15 and 199 up-regulated genes in CIKIL-2. Among DEGs in CIKIL-15, Wnt signaling and cell adhesion were significant GO terms and pathways which related with their functions. In CIKIL-2, type I interferon signaling and cytokine-cytokine receptor interaction were significant GO terms and pathways. We found that the up-regulation of Wnt 4 and PDGFD may contribute to enhanced cell proliferation capacity of CIKIL-15, while inhibitory signal from interaction between CTLA4 and CD80 may be responsible for the weak proliferation capacity of CIKIL-2. Moreover, up-regulated expressions of CD40LG and IRF7 may make for improved tumor cytolytic function of CIKIL-2 through type I interferon signaling. Conclusions Through our findings, we have preliminarily elucidated the cells proliferation and acquisition of tumor cytotoxicity mechanism of CIKIL-15 and CIKIL-2. Better understanding of these mechanisms will help to generate novel CIK cells with greater proliferation potential and improved tumor cytolytic function.
    BMC Medical Genomics 08/2014; 7(1):49. DOI:10.1186/1755-8794-7-49 · 2.87 Impact Factor
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    • "A complete list of these genes is shown in Table S3. The pathway analysis was performed with Reactome [18]; lists of up- and down-regulated pathways were obtained and p-values were adjusted with Benjamini-Yekutieli adjustment for multiple testing [17]. "
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    ABSTRACT: The molecular classification of human breast tumors has afforded insights into subtype specific biological processes, patient prognosis and response to therapies. However, using current methods roughly one quarter of breast tumors cannot be classified into one or another molecular subtype. To explore the possibility that the unclassifiable samples might comprise one or more novel subtypes we employed a collection of publically available breast tumor datasets with accompanying clinical information to assemble 1,593 transcript profiles: 25% of these samples could not be assigned to one of the current molecular subtypes of breast cancer. All of the unclassifiable samples could be grouped into a new molecular subtype, which we termed "luminal-like". We also identified the luminal-like subtype in an independent collection of tumor samples (NKI295). We found that patients harboring tumors of the luminal-like subtype have a better prognosis than those with basal-like breast cancer, a similar prognosis to those with ERBB2+, luminal B or claudin-low tumors, but a worse prognosis than patients with luminal A or normal-like breast tumors. Our findings suggest the occurrence of another molecular subtype of breast cancer that accounts for the vast majority of previously unclassifiable breast tumors.
    PLoS ONE 07/2014; 9(7):e103514. DOI:10.1371/journal.pone.0103514 · 3.23 Impact Factor
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