Reactome pathway analysis: a high-performance in-memory approach

Fabregat, Antonio; Sidiropoulos, Konstantinos; Viteri, Guilherme; Forner, Oscar; Marín-Garcia, Pablo; Arnau, Vicente; D'Eustachio, P; Stein, Lincoln; Hermjakob, Henning

doi:10.1186/s12859-017-1559-2

Cited by 703 publications

(591 citation statements)

References 19 publications

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“…The detected pathways and interactions were mostly linked to miRNA production and processing, further highlighting the role of gene expression in asthma inflammatory pathways and suggesting possible roles for non‐coding RNA in the process. Analysis in the Reactome database revealed involvement of signalling pathways (eg, IL13 signalling pathway, cytokine signalling and IL4 signalling) which were the most commonly detected in the studied gene set resulting from blood cell studies (13 genes out of 43) . In comparison, the nasal epithelium network shows that the detected genes were mostly involved in extracellular matrix organization, collagen degradation and cellular responses to external stimuli.…”

Section: Discussionmentioning

confidence: 99%

“…Analysis in the Reactome database revealed involvement of signalling pathways (eg, IL13 signalling pathway, cytokine signalling and IL4 signalling) which were the most commonly detected in the studied gene set resulting from blood cell studies (13 genes out of 43). 74 In comparison, the nasal epithelium network shows that the detected genes were mostly involved in extracellular matrix organization, collagen degradation and cellular responses to external stimuli. This analysis clearly shows that the detected inflammatory pathways differ between blood cells and nasal epithelial cells.…”

Section: Discussionmentioning

confidence: 99%

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Epigenome‐wide association studies in asthma: A systematic review

Edris

Dekker

Melén

et al. 2019

Clin Experimental Allergy

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Objective Asthma is a common chronic respiratory airway disease influenced by environmental factors and possibly their interaction with the human genome causing epigenetic changes. Epigenome‐wide association studies (EWAS) have mainly investigated DNA methylation and its association with disease or traits, exposure factors or gene expression. This systematic review aimed to identify all EWAS assessing differentially methylated sites associated with asthma in humans. Design Structured systematic literature search following PRISMA guidelines, Newcastle‐Ottawa Scale (NOS) for cohort studies was used for bias assessment. Data Sources We searched PubMed and Embase databases from 2005 to 2019. Eligibility Criteria Epigenome‐wide association studies testing association between differential methylation and asthma in humans. Results Overall, we identified 16 EWAS studies complying with our search criteria. Twelve studies were conducted on children, and 10 were conducted on sample sizes <150 subjects. Four hundred and nineteen CpGs were reported in children studies after correction for multiple testing. In the adult studies, thousands of differentially methylated sites were identified. Differential methylation in inflammatory‐related genes correlated with higher levels of gene expressions of inflammatory modulators in asthma. Differentially methylated genes associated with asthma included SMAD3, SERPINC1, PROK1, IL13, RUNX3 and TIGIT. Forty‐one CpGs were replicated at least once in blood samples, and 28 CpGs were replicated in nasal samples. Conclusion Although many differentially methylated CpGs in genes known to be involved in asthma have been identified in EWAS to date, we conclude that further studies of larger sample sizes and analyses of differential methylation between different phenotypes are needed in order to comprehensively evaluate the role of epigenetic factors in the pathophysiology and heterogeneity of asthma, and the potential clinical utility to predict or classify patients with asthma.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Epigenome‐wide association studies in asthma: A systematic review

Edris

Dekker

Melén

et al. 2019

Clin Experimental Allergy

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“…If they use the ranking system described here, these resources could display the 'best' representative structure and additional coordinate-based information. The PDBe REST API allows such ranking, and multiple data resources, such as Reactome (Fabregat et al, 2017), OpenTargets (Koscielny et al, 2017) and Complex Portal (Meldal et al, 2015), use this approach to display PDB information on their web interface.…”

Section: Resultsmentioning

confidence: 99%

Worldwide Protein Data Bank validation information: usage and trends

Smart

Horský

Gore

et al. 2018

Acta Crystallogr D Struct Biol

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Realising the importance of assessing the quality of the biomolecular structures deposited in the Protein Data Bank (PDB), the Worldwide Protein Data Bank (wwPDB) partners established Validation Task Forces to obtain advice on the methods and standards to be used to validate structures determined by X-ray crystallography, nuclear magnetic resonance spectroscopy and three-dimensional electron cryo-microscopy. The resulting wwPDB validation pipeline is an integral part of the wwPDB OneDep deposition, biocuration and validation system. The wwPDB Validation Service webserver (https://validate.wwpdb.org) can be used to perform checks prior to deposition. Here, it is shown how validation metrics can be combined to produce an overall score that allows the ranking of macromolecular structures and domains in search results. The ValTrends DB database provides users with a convenient way to access and analyse validation information and other properties of X-ray crystal structures in the PDB, including investigating trends in and correlations between different structure properties and validation metrics.

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“…In this approach, we included features related to genes that belonged to the same signaling pathway as the set of target genes. Pathways information was derived from Reactome (33,34) database (version 66 accessed on October 2018). For each compound, first its target set was derived, followed by finding all pathways which included at least one of the given targets.…”

Section: Features Based Only On Drug Targets and Tissue Type Shortlymentioning

confidence: 99%

Feature selection strategies for drug sensitivity prediction

Koras

Juraeva

Kreis

et al. 2019

Preprint

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Drug sensitivity prediction constitutes one of the main challenges in personalized medicine. The major difficulty of this problem stems from the fact that the sensitivity of cancer cells to treatment depends on an unknown subset of a large number of biological features. Although feature selection is the key to interpretable results and identification of potential biomarkers, a comprehensive assessment of feature selection methods for drug sensitivity prediction has so far not been performed. We propose feature selection approaches driven by prior knowledge of drug targets, target pathways, and gene expression signatures. We asses these methodologies on Genomics of Drug Sensitivity in Cancer (GDSC) dataset, a panel of around 1000 cell lines screened against multiple anticancer compounds. We compare our results with a baseline model utilizing genome-wide gene expression features and common data-driven feature selection techniques. Together, 2484 unique models were evaluated, providing a comprehensive study of feature selection strategies for the drug response prediction problem. For 23 drugs, the models achieve better predictive performance when the features are selected according to prior knowledge of drug targets and pathways. The best correlation of observed and predicted response using the test set is achieved for Linifanib (r=0.75). Extending the drug-dependent features with gene expression signatures yields models that are most predictive of drug response for 60 drugs, with the best performing example of Dabrafenib. Examples of how pre-selection of features benefits the model interpretability are given for Dabrafenib, Linifanib and Quizartinib. Based on GDSC drug data, we find that feature selection driven by prior knowledge tends to yield better results for drugs targeting specific genes and pathways, while models with the genome-wide features perform better for drugs affecting general mechanisms such as metabolism and DNA replication. For a significant group of the compounds, even a very small number of features based on simple drug properties is often highly predictive of drug sensitivity, can explain the mechanism of drug action and be used as guidelines for their prescription. In general, choosing appropriate feature selection strategies has the potential to develop interpretable models that are indicative for therapy design. Pharmacogenomics | Machine learning | Personalized medicine

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Reactome pathway analysis: a high-performance in-memory approach

Cited by 703 publications

References 19 publications

Epigenome‐wide association studies in asthma: A systematic review

Epigenome‐wide association studies in asthma: A systematic review

Worldwide Protein Data Bank validation information: usage and trends

Feature selection strategies for drug sensitivity prediction

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