System-level analysis of genes mutated in muscular dystrophies reveals a functional pattern associated with muscle weakness distribution.

Muscular dystrophies (MDs) are inherited genetic diseases causing weakness and degeneration of muscles. The distribution of muscle weakness differs between MDs, involving distal muscles or proximal muscles. While the mutations in most of the MD-associated genes lead to either distal or proximal onset, there are also genes whose mutations can cause both types of onsets. We hypothesized that the genes associated with different MD onsets code proteins with distinct cellular functions. To investigate this, we collected the MD-associated genes and assigned them to three onset groups: genes mutated only in distal onset dystrophies, genes mutated only in proximal onset dystrophies, and genes mutated in both types of onsets. We then systematically evaluated the cellular functions of these gene sets with computational strategies based on functional enrichment analysis and biological network analysis. Our analyses demonstrate that genes mutated in either distal or proximal onset MDs code proteins linked with two distinct sets of cellular processes. Interestingly, these two sets of cellular processes are relevant for the genes that are associated with both onsets. Moreover, the genes associated with both onsets display high centrality and connectivity in the network of muscular dystrophy genes. Our findings support the hypothesis that the proteins associated with distal or proximal onsets have distinct functional characteristics, whereas the proteins associated with both onsets are multifunctional.

Random walk with restart on multilayer networks: from node prioritisation to supervised link prediction and beyond.

BACKGROUND: Biological networks have proven invaluable ability for representing biological knowledge. Multilayer networks, which gather different types of nodes and edges in multiplex, heterogeneous and bipartite networks, provide a natural way to integrate diverse and multi-scale data sources into a common framework. Recently, we developed MultiXrank, a Random Walk with Restart algorithm able to explore such multilayer networks. MultiXrank outputs scores reflecting the proximity between an initial set of seed node(s) and all the other nodes in the multilayer network. We illustrate here the versatility of bioinformatics tasks that can be performed using MultiXrank. RESULTS: We first show that MultiXrank can be used to prioritise genes and drugs of interest by exploring multilayer networks containing interactions between genes, drugs, and diseases. In a second study, we illustrate how MultiXrank scores can also be used in a supervised strategy to train a binary classifier to predict gene-disease associations. The classifier performance are validated using outdated and novel gene-disease association for training and evaluation, respectively. Finally, we show that MultiXrank scores can be used to compute diffusion profiles and use them as disease signatures. We computed the diffusion profiles of more than 100 immune diseases using a multilayer network that includes cell-type specific genomic information. The clustering of the immune disease diffusion profiles reveals shared shared phenotypic characteristics. CONCLUSION: Overall, we illustrate here diverse applications of MultiXrank to showcase its versatility. We expect that this can lead to further and broader bioinformatics applications.

In skeletal muscle and neural crest cells, SMCHD1 regulates biological pathways relevant for Bosma syndrome and facioscapulohumeral dystrophy phenotype.

Many genetic syndromes are linked to mutations in genes encoding factors that guide chromatin organization. Among them, several distinct rare genetic diseases are linked to mutations in SMCHD1 that encodes the structural maintenance of chromosomes flexible hinge domain containing 1 chromatin-associated factor. In humans, its function as well as the impact of its mutations remains poorly defined. To fill this gap, we determined the episignature associated with heterozygous SMCHD1 variants in primary cells and cell lineages derived from induced pluripotent stem cells for Bosma arhinia and microphthalmia syndrome (BAMS) and type 2 facioscapulohumeral dystrophy (FSHD2). In human tissues, SMCHD1 regulates the distribution of methylated CpGs, H3K27 trimethylation and CTCF at repressed chromatin but also at euchromatin. Based on the exploration of tissues affected either in FSHD or in BAMS, i.e. skeletal muscle fibers and neural crest stem cells, respectively, our results emphasize multiple functions for SMCHD1, in chromatin compaction, chromatin insulation and gene regulation with variable targets or phenotypical outcomes. We concluded that in rare genetic diseases, SMCHD1 variants impact gene expression in two ways: (i) by changing the chromatin context at a number of euchromatin loci or (ii) by directly regulating some loci encoding master transcription factors required for cell fate determination and tissue differentiation.

A multilevel screening pipeline in zebrafish identifies therapeutic drugs for GAN.

Giant axonal neuropathy (GAN) is a fatal neurodegenerative disorder for which there is currently no treatment. Affecting the nervous system, GAN starts in infancy with motor deficits that rapidly evolve toward total loss of ambulation. Using the gan zebrafish model that reproduces the loss of motility as seen in patients, we conducted the first pharmacological screening for the GAN pathology. Here, we established a multilevel pipeline to identify small molecules restoring both the physiological and the cellular deficits in GAN. We combined behavioral, in silico, and high-content imaging analyses to refine our Hits to five drugs restoring locomotion, axonal outgrowth, and stabilizing neuromuscular junctions in the gan zebrafish. The postsynaptic nature of the drug's cellular targets provides direct evidence for the pivotal role the neuromuscular junction holds in the restoration of motility. Our results identify the first drug candidates that can now be integrated in a repositioning approach to fasten therapy for the GAN disease. Moreover, we anticipate both our methodological development and the identified hits to be of benefit to other neuromuscular diseases.

Tracking clusters of patients over time enables extracting information from medico-administrative databases.

CONTEXT: Identifying clusters (i.e., subgroups) of patients from the analysis of medico-administrative databases is particularly important to better understand disease heterogeneity. However, these databases contain different types of longitudinal variables which are measured over different follow-up periods, generating truncated data. It is therefore fundamental to develop clustering approaches that can handle this type of data. OBJECTIVE: We propose here cluster-tracking approaches to identify clusters of patients from truncated longitudinal data contained in medico-administrative databases. MATERIAL AND METHODS: We first cluster patients at each age. We then track the identified clusters over ages to construct cluster-trajectories. We compared our novel approaches with three classical longitudinal clustering approaches by calculating the silhouette score. As a use-case, we analyzed antithrombotic drugs used from 2008 to 2018 contained in the Échantillon Généraliste des Bénéficiaires (EGB), a French national cohort. RESULTS: Our cluster-tracking approaches allow us to identify several cluster-trajectories with clinical significance without any imputation of data. The comparison of the silhouette scores obtained with the different approaches highlights the better performances of the cluster-tracking approaches. CONCLUSION: The cluster-tracking approaches are a novel and efficient alternative to identify patient clusters from medico-administrative databases by taking into account their specificities.

orsum: a Python package for filtering and comparing enrichment analyses using a simple principle.

BACKGROUND: Enrichment analyses are widely applied to investigate lists of genes of interest. However, such analyses often result in long lists of annotation terms with high redundancy, making the interpretation and reporting difficult. Long annotation lists and redundancy also complicate the comparison of results obtained from different enrichment analyses. An approach to overcome these issues is using down-sized annotation collections composed of non-redundant terms. However, down-sized collections are generic and the level of detail may not fit the user's study. Other available approaches include clustering and filtering tools, which are based on similarity measures and thresholds that can be complicated to comprehend and set. RESULT: We propose orsum, a Python package to filter enrichment results. orsum can filter multiple enrichment results collectively and highlight common and specific annotation terms. Filtering in orsum is based on a simple principle: a term is discarded if there is a more significant term that annotates at least the same genes; the remaining more significant term becomes the representative term for the discarded term. This principle ensures that the main biological information is preserved in the filtered results while reducing redundancy. In addition, as the representative terms are selected from the original enrichment results, orsum outputs filtered terms tailored to the study. As a use case, we applied orsum to the enrichment analyses of four lists of genes, each associated with a neurodegenerative disease. CONCLUSION: orsum provides a comprehensible and effective way of filtering and comparing enrichment results. It is available at https://anaconda.org/bioconda/orsum .

Tracking Temporal Clusters from Patient Networks.

Creating homogeneous groups (clusters) of patients from medico-administrative databases provides a better understanding of health determinants. But in these databases, patients have truncated care pathways. We developed an approach based on patient networks to construct care trajectories from such truncated data. We tested this approach on antithrombotic treatments prescribed from 2008 to 2018 contained in the échantillon généraliste des bénéficiaires (EGB). We constructed a patient network for each patients' age (years from birth). We then applied the Markov clustering algorithm in each network. The care trajectories were finally constructed by matching clusters identified in two consecutive networks. We calculated the silhouette score to assess the performance of this network approach compared to three existing approaches. We identified 12 care trajectories that we were able to associate with pathologies. The best silhouette score was obtained for the network approach. Our approach allowed to highlight care trajectories taking into account the longitudinal, multidimensional and truncated nature of data from medico-administrative databases.

miR-376a-3p and miR-376b-3p overexpression in Hutchinson-Gilford progeria fibroblasts inhibits cell proliferation and induces premature senescence.

Hutchinson-Gilford progeria syndrome (HGPS) is a rare genetic disorder, in which an abnormal and toxic protein called progerin, accumulates in cell nuclei, leading to major cellular defects. Among them, chromatin remodeling drives gene expression changes, including miRNA dysregulation. In our study, we evaluated miRNA expression profiles in HGPS and control fibroblasts. We identified an enrichment of overexpressed miRNAs belonging to the 14q32.2-14q32.3 miRNA cluster. Using 3D FISH, we demonstrated that overexpression of these miRNAs is associated with chromatin remodeling at this specific locus in HGPS fibroblasts. We then focused on miR-376b-3p and miR-376a-3p, both overexpressed in HGPS fibroblasts. We demonstrated that their induced overexpression in control fibroblasts decreases cell proliferation and increases senescence, whereas their inhibition in HGPS fibroblasts rescues proliferation defects and senescence and decreases progerin accumulation. By targeting these major processes linked to premature aging, these two miRNAs may play a pivotal role in the pathophysiology of HGPS.

Facioscapulohumeral dystrophy weakened sarcomeric contractility is mimicked in induced pluripotent stem cells-derived innervated muscle fibres.

BACKGROUND: Facioscapulohumeral dystrophy (FSHD) is a late-onset autosomal dominant form of muscular dystrophy involving specific groups of muscles with variable weakness that precedes inflammatory response, fat infiltration, and muscle atrophy. As there is currently no cure for this disease, understanding and modelling the typical muscle weakness in FSHD remains a major milestone towards deciphering the disease pathogenesis as it will pave the way to therapeutic strategies aimed at correcting the functional muscular defect in patients. METHODS: To gain further insights into the specificity of the muscle alteration in this disease, we derived induced pluripotent stem cells from patients affected with Types 1 and 2 FSHD but also from patients affected with Bosma arhinia and microphthalmia. We differentiated these cells into contractile innervated muscle fibres and analysed their transcriptome by RNA Seq in comparison with cells derived from healthy donors. To uncover biological pathways altered in the disease, we applied MOGAMUN, a multi-objective genetic algorithm that integrates multiplex complex networks of biological interactions (protein-protein interactions, co-expression, and biological pathways) and RNA Seq expression data to identify active modules. RESULTS: We identified 132 differentially expressed genes that are specific to FSHD cells (false discovery rate < 0.05). In FSHD, the vast majority of active modules retrieved with MOGAMUN converges towards a decreased expression of genes encoding proteins involved in sarcomere organization (P value 2.63e-12 ), actin cytoskeleton (P value 9.4e-5 ), myofibril (P value 2.19e-12 ), actin-myosin sliding, and calcium handling (with P values ranging from 7.9e-35 to 7.9e-21 ). Combined with in vivo validations and functional investigations, our data emphasize a reduction in fibre contraction (P value < 0.0001) indicating that the muscle weakness that is typical of FSHD clinical spectrum might be associated with dysfunction of calcium release (P value < 0.0001), actin-myosin interactions, motor activity, mechano-transduction, and dysfunctional sarcomere contractility. CONCLUSIONS: Identification of biomarkers of FSHD muscle remain critical for understanding the process leading to the pathology but also for the definition of readouts to be used for drug design, outcome measures, and monitoring of therapies. The different pathways identified through a system biology approach have been largely overlooked in the disease. Overall, our work opens new perspectives in the definition of biomarkers able to define the muscle alteration but also in the development of novel strategies to improve muscle function as it provides functional parameters for active molecule screening.

A multi-objective genetic algorithm to find active modules in multiplex biological networks.

The identification of subnetworks of interest-or active modules-by integrating biological networks with molecular profiles is a key resource to inform on the processes perturbed in different cellular conditions. We here propose MOGAMUN, a Multi-Objective Genetic Algorithm to identify active modules in MUltiplex biological Networks. MOGAMUN optimizes both the density of interactions and the scores of the nodes (e.g., their differential expression). We compare MOGAMUN with state-of-the-art methods, representative of different algorithms dedicated to the identification of active modules in single networks. MOGAMUN identifies dense and high-scoring modules that are also easier to interpret. In addition, to our knowledge, MOGAMUN is the first method able to use multiplex networks. Multiplex networks are composed of different layers of physical and functional relationships between genes and proteins. Each layer is associated to its own meaning, topology, and biases; the multiplex framework allows exploiting this diversity of biological networks. We applied MOGAMUN to identify cellular processes perturbed in Facio-Scapulo-Humeral muscular Dystrophy, by integrating RNA-seq expression data with a multiplex biological network. We identified different active modules of interest, thereby providing new angles for investigating the pathomechanisms of this disease. Availability: MOGAMUN is available at https://github.com/elvanov/MOGAMUN and as a Bioconductor package at https://bioconductor.org/packages/release/bioc/html/MOGAMUN.html. Contact: anais.baudot@univ-amu.fr.

AKT Signaling Modifies the Balance between Cell Proliferation and Migration in Neural Crest Cells from Patients Affected with Bosma Arhinia and Microphthalmia Syndrome.

Over the recent years, the SMCHD1 (Structural Maintenance of Chromosome flexible Hinge Domain Containing 1) chromatin-associated factor has triggered increasing interest after the identification of variants in three rare and unrelated diseases, type 2 Facio Scapulo Humeral Dystrophy (FSHD2), Bosma Arhinia and Microphthalmia Syndrome (BAMS), and the more recently isolated hypogonadotrophic hypogonadism (IHH) combined pituitary hormone deficiency (CPHD) and septo-optic dysplasia (SOD). However, it remains unclear why certain mutations lead to a specific muscle defect in FSHD while other are associated with severe congenital anomalies. To gain further insights into the specificity of SMCHD1 variants and identify pathways associated with the BAMS phenotype and related neural crest defects, we derived induced pluripotent stem cells from patients carrying a mutation in this gene. We differentiated these cells in neural crest stem cells and analyzed their transcriptome by RNA-Seq. Besides classical differential expression analyses, we analyzed our data using MOGAMUN, an algorithm allowing the extraction of active modules by integrating differential expression data with biological networks. We found that in BAMS neural crest cells, all subnetworks that are associated with differentially expressed genes converge toward a predominant role for AKT signaling in the control of the cell proliferation-migration balance. Our findings provide further insights into the distinct mechanism by which defects in neural crest migration might contribute to the craniofacial anomalies in BAMS.

MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach.

Network embedding approaches are gaining momentum to analyse a large variety of networks. Indeed, these approaches have demonstrated their effectiveness in tasks such as community detection, node classification, and link prediction. However, very few network embedding methods have been specifically designed to handle multiplex networks, i.e. networks composed of different layers sharing the same set of nodes but having different types of edges. Moreover, to our knowledge, existing approaches cannot embed multiple nodes from multiplex-heterogeneous networks, i.e. networks composed of several multiplex networks containing both different types of nodes and edges. In this study, we propose MultiVERSE, an extension of the VERSE framework using Random Walks with Restart on Multiplex (RWR-M) and Multiplex-Heterogeneous (RWR-MH) networks. MultiVERSE is a fast and scalable method to learn node embeddings from multiplex and multiplex-heterogeneous networks. We evaluate MultiVERSE on several biological and social networks and demonstrate its performance. MultiVERSE indeed outperforms most of the other methods in the tasks of link prediction and network reconstruction for multiplex network embedding, and is also efficient in link prediction for multiplex-heterogeneous network embedding. Finally, we apply MultiVERSE to study rare disease-gene associations using link prediction and clustering. MultiVERSE is freely available on github at https://github.com/Lpiol/MultiVERSE .

Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer.

High-dimensional multi-omics data are now standard in biology. They can greatly enhance our understanding of biological systems when effectively integrated. To achieve proper integration, joint Dimensionality Reduction (jDR) methods are among the most efficient approaches. However, several jDR methods are available, urging the need for a comprehensive benchmark with practical guidelines. We perform a systematic evaluation of nine representative jDR methods using three complementary benchmarks. First, we evaluate their performances in retrieving ground-truth sample clustering from simulated multi-omics datasets. Second, we use TCGA cancer data to assess their strengths in predicting survival, clinical annotations and known pathways/biological processes. Finally, we assess their classification of multi-omics single-cell data. From these in-depth comparisons, we observe that intNMF performs best in clustering, while MCIA offers an effective behavior across many contexts. The code developed for this benchmark study is implemented in a Jupyter notebook-multi-omics mix (momix)-to foster reproducibility, and support users and future developers.

Overlap of vitamin A and vitamin D target genes with CAKUT-related processes.

Congenital Anomalies of the Kidney and Urinary Tract (CAKUT) are a group of abnormalities affecting the kidneys and their outflow tracts, which include the ureters, the bladder, and the urethra. CAKUT patients display a large clinical variability as well as a complex aetiology, as only 5% to 20% of the cases have a monogenic origin. It is thereby suspected that interactions of both genetic and environmental factors contribute to the disease. Vitamins are among the environmental factors that are considered for CAKUT aetiology. In this study, we collected vitamin A and vitamin D target genes and computed their overlap with CAKUT-related gene sets. We observed significant overlaps between vitamin A target genes and CAKUT causal genes, or with genes involved in renal system development, which indicates that an excess or deficiency of vitamin A might be relevant to a broad range of urogenital abnormalities.

Interpreting molecular similarity between patients as a determinant of disease comorbidity relationships.

Comorbidity is a medical condition attracting increasing attention in healthcare and biomedical research. Little is known about the involvement of potential molecular factors leading to the emergence of a specific disease in patients affected by other conditions. We present here a disease interaction network inferred from similarities between patients' molecular profiles, which significantly recapitulates epidemiologically documented comorbidities. Furthermore, we identify disease patient-subgroups that present different molecular similarities with other diseases, some of them opposing the general tendencies observed at the disease level. Analyzing the generated patient-subgroup network, we identify genes involved in such relations, together with drugs whose effects are potentially associated with the observed comorbidities. All the obtained associations are available at the disease PERCEPTION portal (http://disease-perception.bsc.es).

Integrative proteomic and phosphoproteomic profiling of prostate cell lines.

BACKGROUND: Prostate cancer is a major public health issue, mainly because patients relapse after androgen deprivation therapy. Proteomic strategies, aiming to reflect the functional activity of cells, are nowadays among the leading approaches to tackle the challenges not only of better diagnosis, but also of unraveling mechanistic details related to disease etiology and progression. METHODS: We conducted here a large SILAC-based Mass Spectrometry experiment to map the proteomes and phosphoproteomes of four widely used prostate cell lines, namely PNT1A, LNCaP, DU145 and PC3, representative of different cancerous and hormonal status. RESULTS: We identified more than 3000 proteins and phosphosites, from which we quantified more than 1000 proteins and 500 phosphosites after stringent filtering. Extensive exploration of this proteomics and phosphoproteomics dataset allowed characterizing housekeeping as well as cell-line specific proteins, phosphosites and functional features of each cell line. In addition, by comparing the sensitive and resistant cell lines, we identified protein and phosphosites differentially expressed in the resistance context. Further data integration in a molecular network highlighted the differentially expressed pathways, in particular migration and invasion, RNA splicing, DNA damage repair response and transcription regulation. CONCLUSIONS: Overall, this study proposes a valuable resource toward the characterization of proteome and phosphoproteome of four widely used prostate cell lines and reveals candidates to be involved in prostate cancer progression for further experimental validation.

Molecular Inverse Comorbidity between Alzheimer's Disease and Lung Cancer: New Insights from Matrix Factorization.

Matrix factorization (MF) is an established paradigm for large-scale biological data analysis with tremendous potential in computational biology. Here, we challenge MF in depicting the molecular bases of epidemiologically described disease-disease (DD) relationships. As a use case, we focus on the inverse comorbidity association between Alzheimer's disease (AD) and lung cancer (LC), described as a lower than expected probability of developing LC in AD patients. To this day, the molecular mechanisms underlying DD relationships remain poorly explained and their better characterization might offer unprecedented clinical opportunities. To this goal, we extend our previously designed MF-based framework for the molecular characterization of DD relationships. Considering AD-LC inverse comorbidity as a case study, we highlight multiple molecular mechanisms, among which we confirm the involvement of processes related to the immune system and mitochondrial metabolism. We then distinguish mechanisms specific to LC from those shared with other cancers through a pan-cancer analysis. Additionally, new candidate molecular players, such as estrogen receptor (ER), cadherin 1 (CDH1) and histone deacetylase (HDAC), are pinpointed as factors that might underlie the inverse relationship, opening the way to new investigations. Finally, some lung cancer subtype-specific factors are also detected, also suggesting the existence of heterogeneity across patients in the context of inverse comorbidity.

Transcriptomic metaanalyses of autistic brains reveals shared gene expression and biological pathway abnormalities with cancer.

Background: Epidemiological and clinical evidence points to cancer as a comorbidity in people with autism spectrum disorders (ASD). A significant overlap of genes and biological processes between both diseases has also been reported. Methods: Here, for the first time, we compared the gene expression profiles of ASD frontal cortex tissues and 22 cancer types obtained by differential expression meta-analysis and report gene, pathway, and drug set-based overlaps between them. Results: Four cancer types (brain, thyroid, kidney, and pancreatic cancers) presented a significant overlap in gene expression deregulations in the same direction as ASD whereas two cancer types (lung and prostate cancers) showed differential expression profiles significantly deregulated in the opposite direction from ASD. Functional enrichment and LINCS L1000 based drug set enrichment analyses revealed the implication of several biological processes and pathways that were affected jointly in both diseases, including impairments of the immune system, and impairments in oxidative phosphorylation and ATP synthesis among others. Our data also suggest that brain and kidney cancer have patterns of transcriptomic dysregulation in the PI3K/AKT/MTOR axis that are similar to those found in ASD. Conclusions: Comparisons of ASD and cancer differential gene expression meta-analysis results suggest that brain, kidney, thyroid, and pancreatic cancers are candidates for direct comorbid associations with ASD. On the other hand, lung and prostate cancers are candidates for inverse comorbid associations with ASD. Joint perturbations in a set of specific biological processes underlie these associations which include several pathways previously implicated in both cancer and ASD encompassing immune system alterations, impairments of energy metabolism, cell cycle, and signaling through PI3K and G protein-coupled receptors among others. These findings could help to explain epidemiological observations pointing towards direct and inverse comorbid associations between ASD and specific cancer types and depict a complex scenario regarding the molecular patterns of association between ASD and cancer.

Random walk with restart on multiplex and heterogeneous biological networks.

Motivation: Recent years have witnessed an exponential growth in the number of identified interactions between biological molecules. These interactions are usually represented as large and complex networks, calling for the development of appropriated tools to exploit the functional information they contain. Random walk with restart (RWR) is the state-of-the-art guilt-by-association approach. It explores the network vicinity of gene/protein seeds to study their functions, based on the premise that nodes related to similar functions tend to lie close to each other in the networks. Results: In this study, we extended the RWR algorithm to multiplex and heterogeneous networks. The walk can now explore different layers of physical and functional interactions between genes and proteins, such as protein-protein interactions and co-expression associations. In addition, the walk can also jump to a network containing different sets of edges and nodes, such as phenotype similarities between diseases. We devised a leave-one-out cross-validation strategy to evaluate the algorithms abilities to predict disease-associated genes. We demonstrate the increased performances of the multiplex-heterogeneous RWR as compared to several random walks on monoplex or heterogeneous networks. Overall, our framework is able to leverage the different interaction sources to outperform current approaches. Finally, we applied the algorithm to predict candidate genes for the Wiedemann-Rautenstrauch syndrome, and to explore the network vicinity of the SHORT syndrome. Availability and implementation: The source code is available on GitHub at: https://github.com/alberto-valdeolivas/RWR-MH. In addition, an R package is freely available through Bioconductor at: http://bioconductor.org/packages/RandomWalkRestartMH/. Supplementary information: Supplementary data are available at Bioinformatics online.

Identifying communities from multiplex biological networks by randomized optimization of modularity.

The identification of communities, or modules, is a common operation in the analysis of large biological networks. The Disease Module Identification DREAM challenge established a framework to evaluate clustering approaches in a biomedical context, by testing the association of communities with GWAS-derived common trait and disease genes. We implemented here several extensions of the MolTi software that detects communities by optimizing multiplex (and monoplex) network modularity. In particular, MolTi now runs a randomized version of the Louvain algorithm, can consider edge and layer weights, and performs recursive clustering. On simulated networks, the randomization procedure clearly improves the detection of communities. On the DREAM challenge benchmark, the results strongly depend on the selected GWAS dataset and enrichment p -value threshold. However, the randomization procedure, as well as the consideration of weighted edges and layers generally increases the number of trait and disease community detected. The new version of MolTi and the scripts used for the DMI DREAM challenge are available at: https://github.com/gilles-didier/MolTi-DREAM.