<i>corto</i>: a lightweight R package for Gene Network Inference and Master Regulator Analysis

Mercatelli, Daniele; Lopez, Gonzalo; Giorgi, Federico M.

doi:10.1101/2020.02.10.942623

Cited by 8 publications

(12 citation statements)

References 15 publications

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“…Coexpression-based networks were generated using the corto algorithm with default parameters, freely available on the CRAN repository of R packages [27], using the SARS-CoV-2/human interactome proteins derived from [26] and the largest human Lung RNA-Seq dataset available from the GTEX consortium, generated from 427 patients gene expression profiles [28]. In brief, corto calculates a coexpression network for each protein and then removes indirect interactions using Data Processing Inequality [29], which has been shown to provide a more robust readout of single protein abundance alone [30].…”

Section: Coexpression-based Lung Networkmentioning

confidence: 99%

“…Master Regulator Analyses were performed by comparing infected and mock samples in both MERS and SARS datasets separately with the corto algorithm [27] using default parameters and the coexpression network derived from human Lung samples. In brief, a gene-by-gene signature of viral-induced differential expression is generated, and a combined value for each coexpression network is generated by weighting every gene's likelihood in the network, providing a final Normalized Enrichment Score for each human/SARS-nCoV-2 interactome member, which is positive if the network is upregulated by the infection, and negative if it's downregulated, as in [36].…”

Section: Master Regulator Analysismentioning

confidence: 99%

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Master Regulator Analysis of the SARS-CoV-2/Human interactome

Guzzi

Mercatelli

Ceraolo

et al. 2020

Preprint

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the recent epidemic outbreak of a novel human coronavirus called SARS-CoV-2 and causing the respiratory tract disease COVID-19 has reached worldwide resonance and a global effort is being undertaken to characterize the molecular features and evolutionary origins of this virus. In this paper, we set out to shed light on the SARS-CoV-2/host receptor recognition, a crucial factor for successful virus infection. Based on the current knowledge of the interactome between SARS-CoV-2 and host cell proteins, we performed Master Regulator Analysis to detect which parts of the human interactome are most affected by the infection. We detected, amongst others, affected apoptotic and mitochondrial mechanisms, and a downregulation of the ACE2 protein receptor, notions that can be used to develop specific therapies against this new virus.

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Section: Coexpression-based Lung Networkmentioning

confidence: 99%

Section: Master Regulator Analysismentioning

confidence: 99%

Master Regulator Analysis of the SARS-CoV-2/Human interactome

Guzzi

Mercatelli

Ceraolo

et al. 2020

Preprint

Self Cite

103

119

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“…The PR AUC values are low, as is generally the case in eukaryotic GRN inference (Chen and Mar, 2018). Our comparison with several established gene expression-based GRN approaches, such as PANDA (Glass et al, 2013), ARACNE (Margolin et al, 2006), and corto (Mercatelli et al, 2020), shows that these methods also result in low PR AUC values. In addition to the improved performance, ANANSE has another clear benefit.…”

Section: Discussionmentioning

confidence: 93%

“…As the previously described reference datasets contain regulatory interactions for all genes, regardless of cell type, we created high confidence cell type-specific reference data by filtering for TFs and genes that are expressed, using a stringent cutoff of TPM < 10. As comparison, we included three GRN inference methods that were used to create tissue-specific networks using GTEx expression data: PANDA (Glass et al, 2013), ARACNE (Margolin et al, 2006), and corto (Mercatelli et al, 2020).…”

Section: Resultsmentioning

confidence: 99%

ANANSE: An enhancer network-based computational approach for predicting key transcription factors in cell fate determination

Γεωργίου

Veenstra

et al. 2020

Preprint

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Proper cell fate determination is largely orchestrated by complex gene regulatory networks centered around transcription factors. However, experimental elucidation of key transcription factors that drive cellular identity is often intractable. Here, we present ANANSE (ANalysis Algorithm for Networks Specified by Enhancers), a network-based method that exploits enhancer-encoded regulatory information to identify the key transcription factors in cell fate determination. As cell type-specific transcription factors predominantly bind to enhancers, we use regulatory networks based on enhancer properties to prioritize transcription factors. First, we predict genome-wide binding profiles of transcription factors in various cell types using enhancer activity and transcription factor binding motifs. Subsequently, applying these inferred binding profiles, we construct cell type-specific gene regulatory networks, and then predict key transcription factors controlling cell fate conversions using differential gene networks between cell types. Compared to other existing methods, ANANSE correctly predicts the highest number of transcription factors experimentally demonstrated to be sufficient for trans-differentiation. Finally, we apply ANANSE to define an atlas of key transcription factors in 18 normal human tissues. In conclusion, we present a ready-to-implement computational tool for efficient prediction of transcription factors in cell fate determination and to study transcription factor-mediated regulatory mechanisms. ANANSE is freely available at https://github.com/vanheeringen-lab/ANANSE.

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“…We considered only missense variants affecting specific AAs in the protein sequence, for a total of 155 entries ( Supplementary File 2 ). Graph generation was performed with the R statistical software and the corto package v1.1.2 (Mercatelli, Lopez-Garcia et al., 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Structural genetics of circulating variants affecting the SARS-CoV-2 spike/human ACE2 complex

Ortuso

Mercatelli

Guzzi

et al. 2021

Journal of Biomolecular Structure and Dynamics

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SARS-CoV-2 entry in human cells is mediated by the interaction between the viral Spike protein and the human ACE2 receptor. This mechanism evolved from the ancestor bat coronavirus and is currently one of the main targets for antiviral strategies. However, there currently exist several Spike protein variants in the SARS-CoV-2 population as the result of mutations, and it is unclear if these variants may exert a specific effect on the affinity with ACE2 which, in turn, is also characterized by multiple alleles in the human population. In the current study, the GBPM analysis, originally developed for highlighting host-guest interaction features, has been applied to define the key amino acids responsible for the Spike/ACE2 molecular recognition, using four different crystallographic structures. Then, we intersected these structural results with the current mutational status, based on more than 295,000 sequenced cases, in the SARS-CoV-2 population. We identified several Spike mutations interacting with ACE2 and mutated in at least 20 distinct patients: S477N, N439K, N501Y, Y453F, E484K, K417N, S477I and G476S. Among these, mutation N501Y in particular is one of the events characterizing SARS-CoV-2 lineage B.1.1.7, which has recently risen in frequency in Europe. We also identified five ACE2 rare variants that may affect interaction with Spike and susceptibility to infection: S19P, E37K, M82I, E329G and G352V.

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corto: a lightweight R package for Gene Network Inference and Master Regulator Analysis

Cited by 8 publications

References 15 publications

Master Regulator Analysis of the SARS-CoV-2/Human interactome

Master Regulator Analysis of the SARS-CoV-2/Human interactome

ANANSE: An enhancer network-based computational approach for predicting key transcription factors in cell fate determination

Structural genetics of circulating variants affecting the SARS-CoV-2 spike/human ACE2 complex

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