Online <i>in silico</i> validation of disease and gene sets, clusterings or subnetworks with DIGEST

Adamowicz, Klaudia; Maier, Andreas; Baumbach, Jan; Blumenthal, David B.

doi:10.1093/bib/bbac247

Cited by 8 publications

(6 citation statements)

References 33 publications

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“…147 nodes fall into the largest connected component (LCC) and were further used for our analyses; the remaining unconnected nodes were discarded. We used DIGEST [ 1 ] to in silico validate the obtained LCC w.r.t. functional coherence.…”

Section: Methodsmentioning

confidence: 99%

The specific DNA methylation landscape in focal cortical dysplasia ILAE type 3D

Wang

Katoch

Jabari

et al. 2023

acta neuropathol commun

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Focal Cortical Dysplasia (FCD) is a frequent cause of drug-resistant focal epilepsy in children and young adults. The international FCD classifications of 2011 and 2022 have identified several clinico-pathological subtypes, either occurring isolated, i.e., FCD ILAE Type 1 or 2, or in association with a principal cortical lesion, i.e., FCD Type 3. Here, we addressed the DNA methylation signature of a previously described new subtype of FCD 3D occurring in the occipital lobe of very young children and microscopically defined by neuronal cell loss in cortical layer 4. We studied the DNA methylation profile using 850 K BeadChip arrays in a retrospective cohort of 104 patients with FCD 1 A, 2 A, 2B, 3D, TLE without FCD, and 16 postmortem specimens without neurological disorders as controls, operated in China or Germany. DNA was extracted from formalin-fixed paraffin-embedded tissue blocks with microscopically confirmed lesions, and DNA methylation profiles were bioinformatically analyzed with a recently developed deep learning algorithm. Our results revealed a distinct position of FCD 3D in the DNA methylation map of common FCD subtypes, also different from non-FCD epilepsy surgery controls or non-epileptic postmortem controls. Within the FCD 3D cohort, the DNA methylation signature separated three histopathology subtypes, i.e., glial scarring around porencephalic cysts, loss of layer 4, and Rasmussen encephalitis. Differential methylation in FCD 3D with loss of layer 4 mapped explicitly to biological pathways related to neurodegeneration, biogenesis of the extracellular matrix (ECM) components, axon guidance, and regulation of the actin cytoskeleton. Our data suggest that DNA methylation signatures in cortical malformations are not only of diagnostic value but also phenotypically relevant, providing the molecular underpinnings of structural and histopathological features associated with epilepsy. Further studies will be necessary to confirm these results and clarify their functional relevance and epileptogenic potential in these difficult-to-treat children.

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Section: Methodsmentioning

confidence: 99%

The specific DNA methylation landscape in focal cortical dysplasia ILAE type 3D

Wang

Katoch

Jabari

et al. 2023

acta neuropathol commun

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“…Enrichment analysis and functional in silico validation of the computed modules . Supported via queries to the APIs of g: Profiler ( Raudvere et al 2019 ) and DIGEST ( Adamowicz et al 2022 ).…”

Section: Web Applicationmentioning

confidence: 99%

Online bias-aware disease module mining with ROBUST-Web

Sarkar

Lucchetta

Maier³

et al. 2023

Bioinformatics

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Summary We present ROBUST-Web which implements our recently presented ROBUST disease module mining algorithm in a user-friendly web application. ROBUST-Web features seamless downstream disease module exploration via integrated gene set enrichment analysis, tissue expression annotation, and visualization of drug-protein and disease-gene links. Moreover, ROBUST-Web includes bias-aware edge costs for the underlying Steiner tree model as a new algorithmic feature, which allow to correct for study bias in protein-protein interaction networks and further improves the robustness of the computed modules. Availability and implementation Web application: https://robust-web.net. Source code of web application and Python package with new bias-aware edge costs: https://github.com/bionetslab/robust-web, https://github.com/bionetslab/robust_bias_aware. Supplementary information Supplementary data are available at Bioinformatics online.

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“…Cystic fibrosis transmembrane conductance regulator ( CFTR ) mutations affect both osteoblast and osteoclast development 38 . The statistical significance of the resultant network is evaluated using DIGEST 9 , which compares the network to 1000 random networks with the same network attributes regarding functional coherence and calculates an empirical p -value. The resultant network outperforms random networks in terms of gene ontology based on biological process ( P -Value: 0.041), cellular component ( P -Value: 0.001), and KEGG ( P -Value: 0.001) (see Supplementary Note 1 ).…”

Section: Resultsmentioning

confidence: 99%

“…There are many approaches to evaluate the discovered set of biomarkers, including pathway enrichment analysis, which reveals biological pathways enriched in a protein list 8 . In silico validation tools like DIGEST can be used to determine the statistical significance of the obtained enrichment scores in contrast to random background models 9 . Additionally, the biomarkers usually only represent a portion of the disease mechanism.…”

Section: Introductionmentioning

confidence: 99%

Proteomic meta-study harmonization, mechanotyping and drug repurposing candidate prediction with ProHarMeD

Adamowicz,

Arend,

Maier

et al. 2023

npj Syst Biol Appl

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Proteomics technologies, which include a diverse range of approaches such as mass spectrometry-based, array-based, and others, are key technologies for the identification of biomarkers and disease mechanisms, referred to as mechanotyping. Despite over 15,000 published studies in 2022 alone, leveraging publicly available proteomics data for biomarker identification, mechanotyping and drug target identification is not readily possible. Proteomic data addressing similar biological/biomedical questions are made available by multiple research groups in different locations using different model organisms. Furthermore, not only various organisms are employed but different assay systems, such as in vitro and in vivo systems, are used. Finally, even though proteomics data are deposited in public databases, such as ProteomeXchange, they are provided at different levels of detail. Thus, data integration is hampered by non-harmonized usage of identifiers when reviewing the literature or performing meta-analyses to consolidate existing publications into a joint picture. To address this problem, we present ProHarMeD, a tool for harmonizing and comparing proteomics data gathered in multiple studies and for the extraction of disease mechanisms and putative drug repurposing candidates. It is available as a website, Python library and R package. ProHarMeD facilitates ID and name conversions between protein and gene levels, or organisms via ortholog mapping, and provides detailed logs on the loss and gain of IDs after each step. The web tool further determines IDs shared by different studies, proposes potential disease mechanisms as well as drug repurposing candidates automatically, and visualizes these results interactively. We apply ProHarMeD to a set of four studies on bone regeneration. First, we demonstrate the benefit of ID harmonization which increases the number of shared genes between studies by 50%. Second, we identify a potential disease mechanism, with five corresponding drug targets, and the top 20 putative drug repurposing candidates, of which Fondaparinux, the candidate with the highest score, and multiple others are known to have an impact on bone regeneration. Hence, ProHarMeD allows users to harmonize multi-centric proteomics research data in meta-analyses, evaluates the success of the ID conversions and remappings, and finally, it closes the gaps between proteomics, disease mechanism mining and drug repurposing. It is publicly available at https://apps.cosy.bio/proharmed/.

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Online in silico validation of disease and gene sets, clusterings or subnetworks with DIGEST

Cited by 8 publications

References 33 publications

The specific DNA methylation landscape in focal cortical dysplasia ILAE type 3D

The specific DNA methylation landscape in focal cortical dysplasia ILAE type 3D

Online bias-aware disease module mining with ROBUST-Web

Proteomic meta-study harmonization, mechanotyping and drug repurposing candidate prediction with ProHarMeD

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