Modularity in Biological Networks

Alcalá-Corona, Sergio Antonio; Sandoval-Motta, Santiago; Espinal-Enríquez, Jesús; Hernández‐Lemus, Enrique

doi:10.3389/fgene.2021.701331

Cited by 38 publications

(19 citation statements)

References 228 publications

(321 reference statements)

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“…Only communities from the Louvain algorithm were used for further analysis given that it consistently obtained the highest modularity values (Table 3). Moreover, this algorithm has proved to be successful for biological networks and their association to functional features (92,93) Nominal assortativity. To calculate chromosomal assortativity, we used the chromosome location for each gene.…”

Section: Network Analysismentioning

confidence: 99%

Loss of long-range co-expression is a common trait in cancer

García-Cortés

Hernández‐Lemus

Espinal-Enríquez

2022

Preprint

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Cancer cells display common traits and enabling-characteristics previously described as the Hallmarks of Cancer. These occur alongside alterations in the regulatory mechanisms controlling gene transcription. Gene co-expression networks (GCNs) identify correlated sets of genes that might share these mechanisms. We have previously reported loss of long-range co-expression for breast, lung, and kidney cancer GCNs. Here, we extend the study to fifteen tissues. Unlike in healthy phenotypes, the highest cancer gene-pair interactions are intra-chromosomal and their strength decays with base-pair distance. Communities in tumor GCNs are strongly associated with cancer related processes along with a wide presence of adaptive immune response. Riboproteins are highly co-expressed in cancer and normal GCNs, suggesting their relevance for cell viability. The loss of long-range co-expression is not observed in other chronic diseases such as Type-2 Diabetes or Alzheimer's disease. These results suggest that the loss of long-range co-expression is a common trait in cancer.

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Section: Network Analysismentioning

confidence: 99%

Loss of long-range co-expression is a common trait in cancer

García-Cortés

Hernández‐Lemus

Espinal-Enríquez

2022

Preprint

Self Cite

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“…A second group of tools in netZoo were developed to identify and explore higher-order structure in biological networks [34, 35] by identifying highly connected network “communities” and comparing the structure of these communities between phenotypic states. CONDOR [21] identifies communities in bipartite graphs (including eQTL networks and GRNs), while ALPACA [22] finds differential community structures between two networks, such as in a case versus control setting, by going beyond the simple difference of edge weights and using the complete network structure to find differential communities.…”

Section: Resultsmentioning

confidence: 99%

The Network Zoo: a multilingual package for the inference and analysis of biological networks

Guebila

Wang

Lopes‐Ramos

et al. 2022

Preprint

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Inference and analysis of cellular biological networks requires software tools that integrate multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is an open-source software suite to model biological networks, including context-specific gene regulatory networks and multi-omics partial correlation networks, to conduct differential analyses, estimate community structure, and model the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages (R, Python, MATLAB, and C) and between methods to allow a better integration of these tools into analytical pipelines. To demonstrate the value of this integrated toolkit, we analyzed the multi-omic data from the Cancer Cell Line Encyclopedia (CCLE) by inferring gene regulatory networks for each cancer cell line and associating network features with other phenotypic attributes such as drug sensitivity. This allowed us to identify transcription factors that play a critical role in both drug resistance and cancer development in melanoma. We also used netZoo to build a pan-cancer, multi-tiered CCLE map and used it to identify known metabolic hallmarks of cancer and to estimate novel context-specific elements that mediate post-transcriptional regulation. Because the netZoo tools are open-source and there is a growing community of both users and developers, we built an ecosystem to support community contributions, share use cases, and visualize networks online. As additional data types become available and our suite of methods grows, we will expand 'the zoo' to incorporate an increasingly sophisticated collection of tools for network inference and analysis.

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“…DeRegNet combines prior regulatory networks with omics abundance measurements to identify maximally deregulated subnetworks [101]. Some more of these methods and strategies have been further reviewed by other authors [39,[102][103][104]. Compared to existing methods, SUBATOMIC tries to address open issues and creates a comprehensive analysis framework covering various aspects of module inference (see Additional file 8 that further shows a tabular comparison of the features in-between the here mentioned module inference methods and SUBATOMIC.…”

Section: Discussionmentioning

confidence: 99%

SUBATOMIC: a SUbgraph BAsed mulTi-OMIcs clustering framework to analyze integrated multi-edge networks

Loers

Vermeirssen

2022

BMC Bioinformatics

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Background Representing the complex interplay between different types of biomolecules across different omics layers in multi-omics networks bears great potential to gain a deep mechanistic understanding of gene regulation and disease. However, multi-omics networks easily grow into giant hairball structures that hamper biological interpretation. Module detection methods can decompose these networks into smaller interpretable modules. However, these methods are not adapted to deal with multi-omics data nor consider topological features. When deriving very large modules or ignoring the broader network context, interpretability remains limited. To address these issues, we developed a SUbgraph BAsed mulTi-OMIcs Clustering framework (SUBATOMIC), which infers small and interpretable modules with a specific topology while keeping track of connections to other modules and regulators. Results SUBATOMIC groups specific molecular interactions in composite network subgraphs of two and three nodes and clusters them into topological modules. These are functionally annotated, visualized and overlaid with expression profiles to go from static to dynamic modules. To preserve the larger network context, SUBATOMIC investigates statistically the connections in between modules as well as between modules and regulators such as miRNAs and transcription factors. We applied SUBATOMIC to analyze a composite Homo sapiens network containing transcription factor-target gene, miRNA-target gene, protein–protein, homologous and co-functional interactions from different databases. We derived and annotated 5586 modules with diverse topological, functional and regulatory properties. We created novel functional hypotheses for unannotated genes. Furthermore, we integrated modules with condition specific expression data to study the influence of hypoxia in three cancer cell lines. We developed two prioritization strategies to identify the most relevant modules in specific biological contexts: one considering GO term enrichments and one calculating an activity score reflecting the degree of differential expression. Both strategies yielded modules specifically reacting to low oxygen levels. Conclusions We developed the SUBATOMIC framework that generates interpretable modules from integrated multi-omics networks and applied it to hypoxia in cancer. SUBATOMIC can infer and contextualize modules, explore condition or disease specific modules, identify regulators and functionally related modules, and derive novel gene functions for uncharacterized genes. The software is available at https://github.com/CBIGR/SUBATOMIC.

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Modularity in Biological Networks

Cited by 38 publications

References 228 publications

Loss of long-range co-expression is a common trait in cancer

Loss of long-range co-expression is a common trait in cancer

The Network Zoo: a multilingual package for the inference and analysis of biological networks

SUBATOMIC: a SUbgraph BAsed mulTi-OMIcs clustering framework to analyze integrated multi-edge networks

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