KeyPathwayMineR: De Novo Pathway Enrichment in the R Ecosystem

Mechteridis, Konstantinos; Lauber, Michael; Baumbach, Jan; List, Markus

doi:10.3389/fgene.2021.812853

Cited by 6 publications

(5 citation statements)

References 34 publications

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“…To assess whether any of the SNP‐gene pairs identified yielded contextually relevant associations when mapped against publicly available protein–protein interaction datasets, the R package KeyPathwayMineR was paired with an in‐app BioGRID biological network ( BIOGRID Homo_Sapiens Entrez ; graph_id = 9) to carry out pathway enrichment analysis (Mechteridis et al, 2022; Oughtred et al, 2021). Genomic data pertaining to candidate SNPs identified in the study were extracted from the base genotyping dataset and transformed into an indicator matrix such that each point of convergence reflected a sample's status (depicted in binary fashion) regarding the presence or absence of a specified SNP.…”

Section: Methodsmentioning

confidence: 99%

Identifying genetic loci that are associated with changes in gene expression in PTSD in a South African cohort

Swart

Plessis

Rust

et al. 2023

Journal of Neurochemistry

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The molecular mechanisms underlying posttraumatic stress disorder (PTSD) are yet to be fully elucidated, especially in underrepresented population groups. Expression quantitative trait loci (eQTLs) are DNA sequence variants that influence gene expression, in a local (cis‐) or distal (trans‐) manner, and subsequently impact cellular, tissue, and system physiology. This study aims to identify genetic loci associated with gene expression changes in a South African PTSD cohort. Genome‐wide genotype and RNA‐sequencing data were obtained from 32 trauma‐exposed controls and 35 PTSD cases of mixed‐ancestry, as part of the SHARED ROOTS project. The first approach utilised 108 937 single‐nucleotide polymorphisms (SNPs) (MAF > 10%) and 11 312 genes with Matrix eQTL to map potential eQTLs, while controlling for covariates as appropriate. The second analysis was focused on 5638 SNPs related to a previously calculated PTSD polygenic risk score for this cohort. SNP‐gene pairs were considered eQTLs if they surpassed Bonferroni correction and had a false discovery rate <0.05. We did not identify eQTLs that significantly influenced gene expression in a PTSD‐dependent manner. However, several known cis‐eQTLs, independent of PTSD diagnosis, were observed. rs8521 (C > T) was associated with TAGLN and SIDT2 expression, and rs11085906 (C > T) was associated with ZNF333 expression. This exploratory study provides insight into the molecular mechanisms associated with PTSD in a non‐European, admixed sample population. This study was limited by the cross‐sectional design and insufficient statistical power. Overall, this study should encourage further multi‐omics approaches towards investigating PTSD in diverse populations.image

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Section: Methodsmentioning

confidence: 99%

Identifying genetic loci that are associated with changes in gene expression in PTSD in a South African cohort

Swart

Plessis

Rust

et al. 2023

Journal of Neurochemistry

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“…After a GO enrichment analysis, the only significant GO term for the DEG in aged limbs was collagen fibril organization. To search for relationships among the aged DEGs, de novo pathway enrichment was performed with KeyPathwayMineR (Mechteridis et al, 2022). As input, we used the DEGs in aged limbs and a protein-protein interaction network constructed with STRING using the proteome from the AmexT_v47 annotation file.…”

Section: Key Genes In Regeneration Revealed By the Transcriptomic Pro...mentioning

confidence: 99%

“…Redundant terms were clustered using rrvgo v.1.4.4 for R (Sayols, 2023). The PPI visualizations were built with KeyPathwayMineR (Mechteridis et al, 2022) All network visualizations were created with Cytoscape (Bindea et al, 2009).…”

Section: Gene Annotation and Protein-protein Interaction Networkmentioning

confidence: 99%

“…Modules were considered as significant if they had a p-value < 0.05 and |Module-Trait Correlation| > 0.5. The GO enrichment analysis for the genes contained in each module was performed with gProfiler2 v.0.2.1 for R (Kolberg et al, 2020) and the protein-protein interaction network was built with KeyPathwayMineR (Mechteridis et al, 2022) using only the genes with Module Membership > 0.7. The visualizations were created with Cytoscape and BioRender.com.…”

Section: Co-expression Analysismentioning

confidence: 99%

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Key Proteins for Regeneration inA. mexicanum: Transcriptomic Insights from Aged and Juvenile Limbs

del Moral-Morales,

Sámano,

Ocampo-Cervantes

et al. 2023

Preprint

Self Cite

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The axolotl is an animal with remarkable regenerative abilities, making it an ideal model for studying potential regenerative therapies in mammals, including humans. However, the molecular mechanisms involved in regeneration remain unclear. We conducted a transcriptomic analysis of juvenile axolotls’ limbs and their blastema and compared the results with aged axolotls that failed to regenerate after amputation. We identified a set of genes involved in cell differentiation, transcriptional regulation, cartilage development, bone morphogenesis, and extracellular matrix remodeling. Four highly expressed genes (FSTL1, ADAMTS17, GPX7, andCTHRC1) were identified in regenerating tissue, but underexpressed in aged axolotls. Structural and homology analysis showed that these genes are conserved and have important roles in development, bone morphogenesis, and cartilage formation. Our findings propose a novel set of axolotl genes involved in tissue regeneration that could be a starting point for further studies in other vertebrates.Graphical abstract

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“…In particular, so called active module approaches allow to find the most regulated subnetwork based on the provided omics data. These methods were successfully used to interpret transcriptomics data (jActivemodules ( 14 ), BioNet ( 15 ), KeyPathwayMineR ( 16 )) and genomics data (HotNet2 ( 17 ), NetSig ( 18 )). However, straightforward application of these approaches for studying metabolism is difficult due to unique features of metabolic networks.…”

Section: Introductionmentioning

confidence: 99%

Shiny GATOM: omics-based identification of regulated metabolic modules in atom transition networks

Emelianova

Gainullina

Poperechnyi

et al. 2022

Nucleic Acids Research

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Multiple high-throughput omics techniques provide different angles on systematically quantifying and studying metabolic regulation of cellular processes. However, an unbiased analysis of such data and, in particular, integration of multiple types of data remains a challenge. Previously, for this purpose we developed GAM web-service for integrative metabolic network analysis. Here we describe an updated pipeline GATOM and the corresponding web-service Shiny GATOM, which takes as input transcriptional and/or metabolomic data and finds a metabolic subnetwork most regulated between the two conditions of interest. GATOM features a new metabolic network topology based on atom transition, which significantly improves interpretability of the analysis results. To address computational challenges arising with the new network topology, we introduce a new variant of the maximum weight connected subgraph problem and provide a corresponding exact solver. To make the used networks up-to-date we upgraded the KEGG-based network construction pipeline and developed one based on the Rhea database, which allows analysis of lipidomics data. Finally, we simplified local installation, providing R package mwcsr for solving relevant graph optimization problems and R package gatom, which implements the GATOM pipeline. The web-service is available at https://ctlab.itmo.ru/shiny/gatom and https://artyomovlab.wustl.edu/shiny/gatom.

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KeyPathwayMineR: De Novo Pathway Enrichment in the R Ecosystem

Cited by 6 publications

References 34 publications

Identifying genetic loci that are associated with changes in gene expression in PTSD in a South African cohort

Identifying genetic loci that are associated with changes in gene expression in PTSD in a South African cohort

Key Proteins for Regeneration inA. mexicanum: Transcriptomic Insights from Aged and Juvenile Limbs

Shiny GATOM: omics-based identification of regulated metabolic modules in atom transition networks

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