Using the Reactome Database

Rothfels, Karen; Milacic, Marija; Matthews, Lisa; Haw, Robin; Sevilla, Cristoffer; Gillespie, Marc; Stephan, Ralf; Gong, Chuqiao; Ragueneau, Eliot; May, Bruce; Shamovsky, Veronica; Wright, Adam; Weiser, Joel; Beavers, Deidre; Conley, Patrick; Tiwari, Krishna Kumar; Jassal, Bijay; Griss, Johannes; Senff‐Ribeiro, Andrea; Brunson, Timothy; Petryszak, Robert; Hermjakob, Henning; D'Eustachio, P; Wu, Guanming; Stein, Lincoln

doi:10.1002/cpz1.722

Cited by 16 publications

(8 citation statements)

References 30 publications

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“…Of 90 SLE-associated genes discovered by ldVAE models, 37 are among 418 HD versus SLE DEGs identified in all cell-types (hypergeometric enrichment p-value 7.8E-10, see Supplementary Table 1A and Methods for details). Thirteen of these 37 genes are in the Reactome 31 ‘R-HSA-913531’ Interferon Signaling pathway (FDR-adjusted hypergeometric enrichment p-value 7.0E-12), and 11 of the 37 genes are in the Gene Ontology 32 biological process ‘GO:0012501’ Programmed Cell Death (FDR-adjusted hypergeometric enrichment p-vale 0.0024).…”

Section: Resultsmentioning

confidence: 99%

Unsupervised generative AI discovers pan-leukocyte dysregulated pathways in single-cell lupus data

Bolouri,

Cerosaletti,

Lacy-Hulbert

2024

Preprint

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Comparisons of single-cell RNA-sequencing (scRNA-seq) data from healthy and diseased tissues are widely used to identify cell-type-specific dysregulated genes, pathways, and processes. Accordingly, many sophisticated methods have been developed for this purpose. However, such tools generally require considerable user expertise for optimal performance. Here, we show that unsupervised application of a linearly-decoded Variational Auto Encoder (a generative AI model) to scRNA-seq data recapitulates and extends findings from a seminal recent lupus study and leads to new insights. Thanks to existing software libraries, our approach is straightforward to implement, computationally efficient, methodologically robust, and produces consistent and reproducible results.

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Section: Resultsmentioning

confidence: 99%

Unsupervised generative AI discovers pan-leukocyte dysregulated pathways in single-cell lupus data

Bolouri,

Cerosaletti,

Lacy-Hulbert

2024

Preprint

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“…Reactome disease variants can be accessed on the Reactome website (www.reactome.org) as previously published [30] and described in the User Guide (https://reactome.org/userguide). Detailed descriptions of annotated disease processes and variants are also available in Reactome’s electronic textbook (https://reactome.org/download/current/TheReactomeBook.pdf.tgz).…”

Section: Methodsmentioning

confidence: 99%

Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypes

Orlic-Milacic,

Rothfels,

Matthews

et al. 2023

Preprint

Self Cite

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Disease variant annotation in the context of biological reactions and pathways can provide a standardized overview of molecular phenotypes of pathogenic mutations that is amenable to computational mining and mathematical modeling. Reactome, an open source, manually curated, peer-reviewed database of human biological pathways, provides annotations for over 4000 disease variants of close to 400 genes in the context of ~800 disease reactions constituting ~400 disease pathways. Functional annotation of disease variants proceeds from normal gene functions, through disease variants whose divergence from normal molecular behaviors has been experimentally verified, to extrapolation from molecular phenotypes of characterized variants to variants of unknown significance using criteria of the American College of Medical Genetics and Genomics (ACMG). The Reactome pathway-based, reaction-specific disease variant dataset and data model provide a platform to infer pathway output impacts of numerous human disease variants and model organism orthologs, complementing computational predictions of variant pathogenicity.

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“…Moreover, bioinformatics tools enable the functional annotation and pathway analysis of identified proteins, shedding light on the biological processes perturbed in OP. Gene Ontology (GO) [42,43] analysis categorizes proteins based on their biological process (BP), molecular function (MF), and cellular component (CC), while pathway analysis tools such as Kyoto Encyclopedia of Genes and Genomes (KEGG) [44,45] and Reactome [46,47] elucidate the interconnected signaling pathways dysregulated in OP. Ingenuity Pathway Analysis (IPA) [48,49], STRING [50,51], and the Database for Annotation, Visualization and Integrated Discovery (DAVID) [52] provide functional annotation and enrichment analysis, revealing biological pathways and protein-protein interaction (PPI) networks associated with OP pathogenesis.…”

Section: Bioinformatics Tools For Proteomic Data Analysis and Interpr...mentioning

confidence: 99%

Proteomic Insights into Osteoporosis: Unraveling Diagnostic Markers of and Therapeutic Targets for the Metabolic Bone Disease

Wang,

Xue,

et al. 2024

Biomolecules

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Osteoporosis (OP), a prevalent skeletal disorder characterized by compromised bone strength and increased susceptibility to fractures, poses a significant public health concern. This review aims to provide a comprehensive analysis of the current state of research in the field, focusing on the application of proteomic techniques to elucidate diagnostic markers and therapeutic targets for OP. The integration of cutting-edge proteomic technologies has enabled the identification and quantification of proteins associated with bone metabolism, leading to a deeper understanding of the molecular mechanisms underlying OP. In this review, we systematically examine recent advancements in proteomic studies related to OP, emphasizing the identification of potential biomarkers for OP diagnosis and the discovery of novel therapeutic targets. Additionally, we discuss the challenges and future directions in the field, highlighting the potential impact of proteomic research in transforming the landscape of OP diagnosis and treatment.

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Using the Reactome Database

Cited by 16 publications

References 30 publications

Unsupervised generative AI discovers pan-leukocyte dysregulated pathways in single-cell lupus data

Unsupervised generative AI discovers pan-leukocyte dysregulated pathways in single-cell lupus data

Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypes

Proteomic Insights into Osteoporosis: Unraveling Diagnostic Markers of and Therapeutic Targets for the Metabolic Bone Disease

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