BioNERO: an all-in-one R/Bioconductor package for comprehensive and easy biological network reconstruction

Currently, standard network analysis workflows rely on many different packages, often requiring users to have a solid statistics and programming background. Here, we present BioNERO, an R package that aims to integrate all aspects of network analysis workflows, including expression data preprocessing, gene coexpression and regulatory network inference, functional analyses, and intra and interspecies network comparisons. The state-of-the-art methods implemented in BioNERO ensure that users can perform all analyses with a single package in a simple pipeline, without needing to learn a myriad of package-specific syntaxes. BioNERO offers a user-friendly framework that can be easily incorporated in systems biology pipelines. Availability and implementation The package is available at Bioconductor (http://bioconductor.org/packages/BioNERO).

Juxtapose: a gene-embedding approach for comparing co-expression networks

Background Gene co-expression networks (GCNs) are not easily comparable due to their complex structure. In this paper, we propose a tool, Juxtapose, together with similarity measures that can be utilized for comparative transcriptomics between a set of organisms. While we focus on its application to comparing co-expression networks across species in evolutionary studies, Juxtapose is also generalizable to co-expression network comparisons across tissues or conditions within the same species. Methods A word embedding strategy commonly used in natural language processing was utilized in order to generate gene embeddings based on walks made throughout the GCNs. Juxtapose was evaluated based on its ability to embed the nodes of synthetic structures in the networks consistently while also generating biologically informative results. Evaluation of the techniques proposed in this research utilized RNA-seq datasets from GTEx, a multi-species experiment of prefrontal cortex samples from the Gene Expression Omnibus, as well as synthesized datasets. Biological evaluation was performed using gene set enrichment analysis and known gene relationships in literature. Results We show that Juxtapose is capable of globally aligning synthesized networks as well as identifying areas that are conserved in real gene co-expression networks without reliance on external biological information. Furthermore, output from a matching algorithm that uses cosine distance between GCN embeddings is shown to be an informative measure of similarity that reflects the amount of topological similarity between networks. Conclusions Juxtapose can be used to align GCNs without relying on known biological similarities and enables post-hoc analyses using biological parameters, such as orthology of genes, or conserved or variable pathways. Availability A development version of the software used in this paper is available at https://github.com/klovens/juxtapose

Deep sea sediments associated with cold seeps are a subsurface reservoir of viral diversity

In marine ecosystems, viruses exert control on the composition and metabolism of microbial communities, influencing overall biogeochemical cycling. Deep sea sediments associated with cold seeps are known to host taxonomically diverse microbial communities, but little is known about viruses infecting these microorganisms. Here, we probed metagenomes from seven geographically diverse cold seeps across global oceans to assess viral diversity, virus–host interaction, and virus-encoded auxiliary metabolic genes (AMGs). Gene-sharing network comparisons with viruses inhabiting other ecosystems reveal that cold seep sediments harbour considerable unexplored viral diversity. Most cold seep viruses display high degrees of endemism with seep fluid flux being one of the main drivers of viral community composition. In silico predictions linked 14.2% of the viruses to microbial host populations with many belonging to poorly understood candidate bacterial and archaeal phyla. Lysis was predicted to be a predominant viral lifestyle based on lineage-specific virus/host abundance ratios. Metabolic predictions of prokaryotic host genomes and viral AMGs suggest that viruses influence microbial hydrocarbon biodegradation at cold seeps, as well as other carbon, sulfur and nitrogen cycling via virus-induced mortality and/or metabolic augmentation. Overall, these findings reveal the global diversity and biogeography of cold seep viruses and indicate how viruses may manipulate seep microbial ecology and biogeochemistry.

Explanatory Item Response Models for Dyadic Data from Multiple Groups

Like other quantitative social scientists, network researchers benefit from pooling information from multiple observed variables to infer underlying (latent) attributes or social processes. Appropriate network data for this task is increasingly available. The inherent dependencies in relational data, however, pose unique challenges. This is especially true for the ascendant tasks of cross-network comparisons and multilevel network analysis. The author draws on item response theory and multilevel (mixed effects) modeling to propose a methodological approach that accounts for these dependencies and allows the analyst to model variation of latent dyadic traits across relations, actors, and groups precisely and parsimoniously. Examples demonstrate the approach’s utility for three important research areas: tie strength in adolescent friendships, group differences in how discussing personal problems relates to tie strength, and the analysis of multiple relations.

Deep sea sediments associated with cold seeps are a subsurface reservoir of viral diversity

In marine ecosystems, viruses exert control on the composition and metabolism of microbial communities, thus influencing overall biogeochemical cycling. Deep sea sediments associated with cold seeps are known to host taxonomically diverse microbial communities, but little is known about viruses infecting these microorganisms. Here, we probed metagenomes from seven geographically diverse cold seeps across global oceans, to assess viral diversity, virus-host interaction, and virus-encoded auxiliary metabolic genes (AMGs). Gene-sharing network comparisons with viruses inhabiting other ecosystems reveal that cold seep sediments harbour considerable unexplored viral diversity. Most cold seep viruses display high degrees of endemism with seep fluid flux being one of the main drivers of viral community composition. In silico predictions linked 14.2% of the viruses to microbial host populations, with many belonging to poorly understood candidate bacterial and archaeal phyla. Lysis was predicted to be a predominant viral lifestyle based on lineage-specific virus/host abundance ratios. Metabolic predictions of prokaryotic host genomes and viral AMGs suggest that viruses influence microbial hydrocarbon biodegradation at cold seeps, as well as other carbon, sulfur and nitrogen cycling via virus-induced mortality and/or metabolic augmentation. Overall, these findings reveal the global diversity and biogeography of cold seep viruses and indicate how viruses may manipulate seep microbial ecology and biogeochemistry.

Explanatory Item Response Models for Dyadic Data from Multiple Groups

Like other quantitative social scientists, network researchers benefit from pooling information from multiple observed variables to infer underlying (latent) attributes or social processes. Appropriate network data for this task is increasingly available. The inherent dependencies in relational data, however, pose unique challenges. This is especially true for those involved in the ascendant tasks of cross-network comparisons or multilevel network analysis. The author draws on item response theory and multilevel (mixed effects) modeling to propose a methodological approach that accounts for these dependencies and allows the analyst to model variation of latent dyadic traits across relations, actors, and groups precisely and parsimoniously. Examples demonstrate the approach’s utility for three important research areas: tie strength in adolescent friendships, group differences in how discussing personal problems relates to tie strength, and the analysis of multiple relations.

Unique longitudinal relationships between symptoms of psychopathology in youth: A cross-lagged panel network analysis in the ABCD study

Background: The network theory suggests that psychopathology may reflect causal relationships between individual symptoms. Several studies have examined cross-sectional relationships between individual symptoms in youth. However, these studies cannot address the directionality of the temporal relationships hypothesized by the network theory. Therefore, we estimated the longitudinal relationships between individual internalizing, externalizing, and attention symptoms in youth. Methods: Data from 4,093 youth participants in the Adolescent Brain Cognitive Development (ABCD) study were used. Symptoms were assessed using the Brief Problem Monitor, which was administered at three time points spaced six months apart. Unique longitudinal relationships between symptoms at T1 and T2 were estimated using cross-lagged panel network modeling. Network replicability was assessed by comparing this network to an identically estimated replication network of symptoms at T2 predicting symptoms at T3. Results: After controlling for all other symptoms and demographic covariates, depressed mood, inattention, and worry at T1 were most predictive of other symptoms at T2. In contrast, threats of violence and destructiveness at T2 were most prospectively predicted by other symptoms at T1. The reciprocal associations between depressed mood and worthlessness were among the strongest bivariate relationships in the network. Comparisons between the original network and the replication network (correlation between edge lists = .61; individual edge replicability = 64-84%) suggested moderate replicability. Conclusions: Although causal inferences are precluded by the observational design and methodological considerations, these findings demonstrate the directionality of relationships between individual symptoms in youth and highlight depressed mood, inattention, and worry as potential influencers of other symptoms.