CyREST: Turbocharging Cytoscape Access for External Tools via a RESTful API

Studying the O-GlcNAcome of human placentas using banked tissue samples

O-GlcNAcylation is a dynamic modulator of signaling pathways, equal in magnitude to the widely studied phosphorylation. With the rapid development of tools for its detection at the single protein level, the O-GlcNAc modification rapidly emerged as a novel diagnostic and therapeutic target in human diseases. Yet, mapping the human O-GlcNAcome in various tissues is essential for generating relevant biomarkers. In this study, we used human banked tissue as a sample source to identify O-GlcNAcylated protein targets relevant to human diseases. Using human term placentas, we propose (1) a method to clean frozen banked tissue of blood proteins; (2) an optimized protocol for the enrichment of O-GlcNAcylated proteins using immunoaffinity purification; and (3) a bioinformatic workflow to identify the most promising O-GlcNAc targets. As a proof-of-concept, we used 45 mg of banked placental samples from two pregnancies to generate intracellular protein extracts depleted of blood protein. Then, antibody-based O-GlcNAc enrichment on denatured samples yielded over 2000 unique HexNAc PSMs and 900 unique sites using 300 μg of protein lysate. Due to efficient sample cleanup, we also captured 82 HexNAc proteins with high placental expression. Finally, we provide a bioinformatic tool (CytOVS) to sort the HexNAc proteins based on their cellular localization and extract the most promising O-GlcNAc targets to explore further. To conclude, we provide a simple 3-step workflow to generate a manageable list of O-GlcNAc proteins from human tissue and improve our understanding of O-GlcNAcylation's role in health and diseases.

ERG and c-MYC regulate a critical gene network in BCR::ABL1-driven B cell acute lymphoblastic leukemia

Philadelphia chromosome-positive B cell acute lymphoblastic leukemia (B-ALL), characterized by the BCR::ABL1 fusion gene, remains a poor prognosis cancer needing new therapeutic approaches. Transcriptomic profiling identified up-regulation of oncogenic transcription factors ERG and c-MYC in BCR::ABL1 B-ALL with ERG and c-MYC required for BCR::ABL1 B-ALL in murine and human models. Profiling of ERG- and c-MYC-dependent gene expression and analysis of ChIP-seq data established ERG and c-MYC coordinate a regulatory network in BCR::ABL1 B-ALL that controls expression of genes involved in several biological processes. Prominent was control of ribosome biogenesis, including expression of RNA polymerase I (POL I) subunits, the importance of which was validated by inhibition of BCR::ABL1 cells by POL I inhibitors, including CX-5461, that prevents promoter recruitment and transcription initiation by POL I. Our results reveal an essential ERG- and c-MYC-dependent transcriptional network involved in regulation of metabolic and ribosome biogenesis pathways in BCR::ABL1 B-ALL, from which previously unidentified vulnerabilities and therapeutic targets may emerge.

Metagenomic domain substitution for the high-throughput modification of nonribosomal peptides

The modular nature of nonribosomal peptide biosynthesis has driven efforts to generate peptide analogs by substituting amino acid-specifying domains within nonribosomal peptide synthetase (NRPS) enzymes. Rational NRPS engineering has increasingly focused on finding evolutionarily favored recombination sites for domain substitution. Here we present an alternative evolution-inspired approach that involves large-scale diversification and screening. By amplifying amino acid-specifying domains en masse from soil metagenomic DNA, we substitute more than 1,000 unique domains into a pyoverdine NRPS. Initial fluorescence and mass spectrometry screens followed by sequencing reveal more than 100 functional domain substitutions, collectively yielding 16 distinct pyoverdines as major products. This metagenomic approach does not require the high success rates demanded by rational NRPS engineering but instead enables the exploration of large numbers of substitutions in parallel. This opens possibilities for the discovery and production of nonribosomal peptides with diverse biological activities.

A homoeostatic switch causing glycerol-3-phosphate and phosphoethanolamine accumulation triggers senescence by rewiring lipid metabolism

Cellular senescence affects many physiological and pathological processes and is characterized by durable cell cycle arrest, an inflammatory secretory phenotype and metabolic reprogramming. Here, by using dynamic transcriptome and metabolome profiling in human fibroblasts with different subtypes of senescence, we show that a homoeostatic switch that results in glycerol-3-phosphate (G3P) and phosphoethanolamine (pEtN) accumulation links lipid metabolism to the senescence gene expression programme. Mechanistically, p53-dependent glycerol kinase activation and post-translational inactivation of phosphate cytidylyltransferase 2, ethanolamine regulate this metabolic switch, which promotes triglyceride accumulation in lipid droplets and induces the senescence gene expression programme. Conversely, G3P phosphatase and ethanolamine-phosphate phospho-lyase-based scavenging of G3P and pEtN acts in a senomorphic way by reducing G3P and pEtN accumulation. Collectively, our study ties G3P and pEtN accumulation to controlling lipid droplet biogenesis and phospholipid flux in senescent cells, providing a potential therapeutic avenue for targeting senescence and related pathophysiology.

Escape from oncogene-induced senescence is controlled by POU2F2 and memorized by chromatin scars

Although oncogene-induced senescence (OIS) is a potent tumor-suppressor mechanism, recent studies revealed that cells could escape from OIS with features of transformed cells. However, the mechanisms that promote OIS escape remain unclear, and evidence of post-senescent cells in human cancers is missing. Here, we unravel the regulatory mechanisms underlying OIS escape using dynamic multidimensional profiling. We demonstrate a critical role for AP1 and POU2F2 transcription factors in escape from OIS and identify senescence-associated chromatin scars (SACSs) as an epigenetic memory of OIS detectable during colorectal cancer progression. POU2F2 levels are already elevated in precancerous lesions and as cells escape from OIS, and its expression and binding activity to cis-regulatory elements are associated with decreased patient survival. Our results support a model in which POU2F2 exploits a precoded enhancer landscape necessary for senescence escape and reveal POU2F2 and SACS gene signatures as valuable biomarkers with diagnostic and prognostic potential.

clusterMaker2: a major update to clusterMaker, a multi-algorithm clustering app for Cytoscape

BACKGROUND

Since the initial publication of clusterMaker, the need for tools to analyze large biological datasets has only increased. New datasets are significantly larger than a decade ago, and new experimental techniques such as single-cell transcriptomics continue to drive the need for clustering or classification techniques to focus on portions of datasets of interest. While many libraries and packages exist that implement various algorithms, there remains the need for clustering packages that are easy to use, integrated with visualization of the results, and integrated with other commonly used tools for biological data analysis. clusterMaker2 has added several new algorithms, including two entirely new categories of analyses: node ranking and dimensionality reduction. Furthermore, many of the new algorithms have been implemented using the Cytoscape jobs API, which provides a mechanism for executing remote jobs from within Cytoscape. Together, these advances facilitate meaningful analyses of modern biological datasets despite their ever-increasing size and complexity.

RESULTS

The use of clusterMaker2 is exemplified by reanalyzing the yeast heat shock expression experiment that was included in our original paper; however, here we explored this dataset in significantly more detail. Combining this dataset with the yeast protein-protein interaction network from STRING, we were able to perform a variety of analyses and visualizations from within clusterMaker2, including Leiden clustering to break the entire network into smaller clusters, hierarchical clustering to look at the overall expression dataset, dimensionality reduction using UMAP to find correlations between our hierarchical visualization and the UMAP plot, fuzzy clustering, and cluster ranking. Using these techniques, we were able to explore the highest-ranking cluster and determine that it represents a strong contender for proteins working together in response to heat shock. We found a series of clusters that, when re-explored as fuzzy clusters, provide a better presentation of mitochondrial processes.

CONCLUSIONS

clusterMaker2 represents a significant advance over the previously published version, and most importantly, provides an easy-to-use tool to perform clustering and to visualize clusters within the Cytoscape network context. The new algorithms should be welcome to the large population of Cytoscape users, particularly the new dimensionality reduction and fuzzy clustering techniques.

Mena regulates nesprin-2 to control actin-nuclear lamina associations, trans-nuclear membrane signalling and gene expression

Interactions between cells and the extracellular matrix, mediated by integrin adhesion complexes, play key roles in fundamental cellular processes, including the sensing and transduction of mechanical cues. Here, we investigate systems-level changes in the integrin adhesome in patient-derived cutaneous squamous cell carcinoma cells and identify the actin regulatory protein Mena as a key node in the adhesion complex network. Mena is connected within a subnetwork of actin-binding proteins to the LINC complex component nesprin-2, with which it interacts and co-localises at the nuclear envelope. Moreover, Mena potentiates the interactions of nesprin-2 with the actin cytoskeleton and the nuclear lamina. CRISPR-mediated Mena depletion causes altered nuclear morphology, reduces tyrosine phosphorylation of the nuclear membrane protein emerin and downregulates expression of the immunomodulatory gene PTX3 via the recruitment of its enhancer to the nuclear periphery. We uncover an unexpected role for Mena at the nuclear membrane, where it controls nuclear architecture, chromatin repositioning and gene expression. Our findings identify an adhesion protein that regulates gene transcription via direct signalling across the nuclear envelope.

A transcriptome atlas of leg muscles from healthy human volunteers reveals molecular and cellular signatures associated with muscle location

Skeletal muscles support the stability and mobility of the skeleton but differ in biomechanical properties and physiological functions. The intrinsic factors that regulate muscle-specific characteristics are poorly understood. To study these, we constructed a large atlas of RNA-seq profiles from six leg muscles and two locations from one muscle, using biopsies from 20 healthy young males. We identified differential expression patterns and cellular composition across the seven tissues using three bioinformatics approaches confirmed by large-scale newly developed quantitative immune-histology procedures. With all three procedures, the muscle samples clustered into three groups congruent with their anatomical location. Concomitant with genes marking oxidative metabolism, genes marking fast- or slow-twitch myofibers differed between the three groups. The groups of muscles with higher expression of slow-twitch genes were enriched in endothelial cells and showed higher capillary content. In addition, expression profiles of Homeobox (HOX) transcription factors differed between the three groups and were confirmed by spatial RNA hybridization. We created an open-source graphical interface to explore and visualize the leg muscle atlas (https://tabbassidaloii.shinyapps.io/muscleAtlasShinyApp/). Our study reveals the molecular specialization of human leg muscles, and provides a novel resource to study muscle-specific molecular features, which could be linked with (patho)physiological processes.

Translating desktop success to the web in the cytoscape project

Cytoscape is an open-source bioinformatics environment for the analysis, integration, visualization, and query of biological networks. In this perspective piece, we describe our project to bring the Cytoscape desktop application to the web while explaining our strategy in ways relevant to others in the bioinformatics community. We examine opportunities and challenges in developing bioinformatics software that spans both the desktop and web, and we describe our ongoing efforts to build a Cytoscape web application, highlighting the principles that guide our development.

Processes in DNA damage response from a whole-cell multi-omics perspective

Technological advances have made it feasible to collect multi-condition multi-omic time courses of cellular response to perturbation, but the complexity of these datasets impedes discovery due to challenges in data management, analysis, visualization, and interpretation. Here, we report a whole-cell mechanistic analysis of HL-60 cellular response to bendamustine. We integrate both enrichment and network analysis to show the progression of DNA damage and programmed cell death over time in molecular, pathway, and process-level detail using an interactive analysis framework for multi-omics data. Our framework, Mechanism of Action Generator Involving Network analysis (MAGINE), automates network construction and enrichment analysis across multiple samples and platforms, which can be integrated into our annotated gene-set network to combine the strengths of networks and ontology-driven analysis. Taken together, our work demonstrates how multi-omics integration can be used to explore signaling processes at various resolutions and demonstrates multi-pathway involvement beyond the canonical bendamustine mechanism.

Transcriptional profiling of human Vδ1 T cells reveals a pathogen-driven adaptive differentiation program

γδ T cells are generally considered innate-like lymphocytes, however, an ‘‘adaptive-like’’ γδ compartment has now emerged. To understand transcriptional regulation of adaptive γδ T cell immunobiology, we combined single-cell transcriptomics, T cell receptor (TCR)-clonotype assignment, ATAC-seq, and immunophenotyping. We show that adult Vδ1⁺ T cells segregate into TCF7⁺LEF1⁺Granzyme B^neg (T_naive) or T-bet⁺Eomes⁺ BLIMP-1⁺Granzyme B⁺ (T_effector) transcriptional subtypes, with clonotypically expanded TCRs detected exclusively in T_effector cells. Transcriptional reprogramming mirrors changes within CD8⁺ αβ T cells following antigen-specific maturation and involves chromatin remodeling, enhancing cytokine production and cytotoxicity. Consistent with this, in vitro TCR engagement induces comparable BLIMP-1, Eomes, and T-bet expression in naive Vδ1⁺ and CD8⁺ T cells. Finally, both human cytomegalovirus and Plasmodium falciparum infection in vivo drive adaptive Vδ1 T cell differentiation from T_naive to T_effector transcriptional status, alongside clonotypic expansion. Contrastingly, semi-invariant Vγ9⁺Vδ2⁺ T cells exhibit a distinct ‘‘innate-effector’’ transcriptional program established by early childhood. In summary, adaptive-like γδ subsets undergo a pathogen-driven differentiation process analogous to conventional CD8⁺ T cells.

Using single-cell transcriptomics, TCR repertoire analysis, ATAC-seq, and immunophenotyping, McMurray et al. show naive Vδ1⁺ T cells can undergo transcriptional reprogramming to an effector state extremely similar to CD8 T_EMRA cells. Infections, including CMV and malaria, drive both clonotypic Vδ1⁺ T cell expansion and differentiation to this highly conserved effector program.

Nezzle: an interactive and programmable visualization of biological networks in Python

Summary

High-quality visualization of biological networks often requires both manual curation for proper alignment and programming to map external data to the graphical components. Nezzle is a network visualization software written in Python, which provides programmable and interactive interfaces for facilitating both manual and automatic curation of the graphical components of networks to create high-quality figures.

Availability and implementation

Nezzle is an open-source project under MIT license and is available from https://github.com/dwgoon/nezzle.

Supplementary information

Supplementary data are available at Bioinformatics online.

Uncovering in vivo biochemical patterns from time-series metabolic dynamics

System biology relies on holistic biomolecule measurements, and untangling biochemical networks requires time-series metabolomics profiling. With current metabolomic approaches, time-series measurements can be taken for hundreds of metabolic features, which decode underlying metabolic regulation. Such a metabolomic dataset is untargeted with most features unannotated and inaccessible to statistical analysis and computational modeling. The high dimensionality of the metabolic space also causes mechanistic modeling to be rather cumbersome computationally. We implemented a faster exploratory workflow to visualize and extract chemical and biochemical dependencies. Time-series metabolic features (about 300 for each dataset) were extracted by Ridge Tracking-based Extract (RTExtract) on measurements from continuous in vivo monitoring of metabolism by NMR (CIVM-NMR) in Neurospora crassa under different conditions. The metabolic profiles were then smoothed and projected into lower dimensions, enabling a comparison of metabolic trends in the cultures. Next, we expanded incomplete metabolite annotation using a correlation network. Lastly, we uncovered meaningful metabolic clusters by estimating dependencies between smoothed metabolic profiles. We thus sidestepped the processes of time-consuming mechanistic modeling, difficult global optimization, and labor-intensive annotation. Multiple clusters guided insights into central energy metabolism and membrane synthesis. Dense connections with glucose 1-phosphate indicated its central position in metabolism in N. crassa. Our approach was benchmarked on simulated random network dynamics and provides a novel exploratory approach to analyzing high-dimensional metabolic dynamics.

A systems genomics approach to uncover patient-specific pathogenic pathways and proteins in ulcerative colitis

We describe a precision medicine workflow, the integrated single nucleotide polymorphism network platform (iSNP), designed to determine the mechanisms by which SNPs affect cellular regulatory networks, and how SNP co-occurrences contribute to disease pathogenesis in ulcerative colitis (UC). Using SNP profiles of 378 UC patients we map the regulatory effects of the SNPs to a human signalling network containing protein-protein, miRNA-mRNA and transcription factor binding interactions. With unsupervised clustering algorithms we group these patient-specific networks into four distinct clusters driven by PRKCB, HLA, SNAI1/CEBPB/PTPN1 and VEGFA/XPO5/POLH hubs. The pathway analysis identifies calcium homeostasis, wound healing and cell motility as key processes in UC pathogenesis. Using transcriptomic data from an independent patient cohort, with three complementary validation approaches focusing on the SNP-affected genes, the patient specific modules and affected functions, we confirm the regulatory impact of non-coding SNPs. iSNP identified regulatory effects for disease-associated non-coding SNPs, and by predicting the patient-specific pathogenic processes, we propose a systems-level way to stratify patients.

Single Nucleotide Polymorphisms (SNPs) affect cellular regulatory networks, and SNP co-occurrences contribute to disease pathogenesis in ulcerative colitis (UC). Here the authors introduce iSNP, a precision medicine pipeline that combines genomics and network biology approaches to uncover patient specific pathways affected in complex diseases.

Assessment of Greenhouse Tomato Anthesis Rate Through Metabolomics Using LASSO Regularized Linear Regression Model

While the high year-round production of tomatoes has been facilitated by solar greenhouse cultivation, these yields readily fluctuate in response to changing environmental conditions. Mathematic modeling has been applied to forecast phenotypes of tomatoes using environmental measurements (e.g., temperature) as indirect parameters. In this study, metabolome data, as direct parameters reflecting plant internal status, were used to construct a predictive model of the anthesis rate of greenhouse tomatoes. Metabolome data were obtained from tomato leaves and used as variables for linear regression with the least absolute shrinkage and selection operator (LASSO) for prediction. The constructed model accurately predicted the anthesis rate, with an R² value of 0.85. Twenty-nine of the 161 metabolites were selected as candidate markers. The selected metabolites were further validated for their association with anthesis rates using the different metabolome datasets. To assess the importance of the selected metabolites in cultivation, the relationships between the metabolites and cultivation conditions were analyzed via correspondence analysis. Trigonelline, whose content did not exhibit a diurnal rhythm, displayed major contributions to the cultivation, and is thus a potential metabolic marker for predicting the anthesis rate. This study demonstrates that machine learning can be applied to metabolome data to identify metabolites indicative of agricultural traits.

MODalyseR-a novel software for inference of disease module hub regulators identified a putative multiple sclerosis regulator supported by independent eQTL data

Motivation

Network-based disease modules have proven to be a powerful concept for extracting knowledge about disease mechanisms, predicting for example disease risk factors and side effects of treatments. Plenty of tools exist for the purpose of module inference, but less effort has been put on simultaneously utilizing knowledge about regulatory mechanisms for predicting disease module hub regulators.

Results

We developed MODalyseR, a novel software for identifying disease module regulators and reducing modules to the most disease-associated genes. This pipeline integrates and extends previously published software packages MODifieR and ComHub and hereby provides a user-friendly network medicine framework combining the concepts of disease modules and hub regulators for precise disease gene identification from transcriptomics data. To demonstrate the usability of the tool, we designed a case study for multiple sclerosis that revealed IKZF1 as a promising hub regulator, which was supported by independent ChIP-seq data.

Availability and implementation

MODalyseR is available as a Docker image at https://hub.docker.com/r/ddeweerd/modalyser with user guide and installation instructions found at https://gustafsson-lab.gitlab.io/MODalyseR/.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

A roadmap for metagenomic enzyme discovery

Covering: up to 2021Metagenomics has yielded massive amounts of sequencing data offering a glimpse into the biosynthetic potential of the uncultivated microbial majority. While genome-resolved information about microbial communities from nearly every environment on earth is now available, the ability to accurately predict biocatalytic functions directly from sequencing data remains challenging. Compared to primary metabolic pathways, enzymes involved in secondary metabolism often catalyze specialized reactions with diverse substrates, making these pathways rich resources for the discovery of new enzymology. To date, functional insights gained from studies on environmental DNA (eDNA) have largely relied on PCR- or activity-based screening of eDNA fragments cloned in fosmid or cosmid libraries. As an alternative, shotgun metagenomics holds underexplored potential for the discovery of new enzymes directly from eDNA by avoiding common biases introduced through PCR- or activity-guided functional metagenomics workflows. However, inferring new enzyme functions directly from eDNA is similar to searching for a 'needle in a haystack' without direct links between genotype and phenotype. The goal of this review is to provide a roadmap to navigate shotgun metagenomic sequencing data and identify new candidate biosynthetic enzymes. We cover both computational and experimental strategies to mine metagenomes and explore protein sequence space with a spotlight on natural product biosynthesis. Specifically, we compare in silico methods for enzyme discovery including phylogenetics, sequence similarity networks, genomic context, 3D structure-based approaches, and machine learning techniques. We also discuss various experimental strategies to test computational predictions including heterologous expression and screening. Finally, we provide an outlook for future directions in the field with an emphasis on meta-omics, single-cell genomics, cell-free expression systems, and sequence-independent methods.

Deletion of pancreas-specific miR-216a reduces beta-cell mass and inhibits pancreatic cancer progression in mice

miRNAs have crucial functions in many biological processes and are candidate biomarkers of disease. Here, we show that miR-216a is a conserved, pancreas-specific miRNA with important roles in pancreatic islet and acinar cells. Deletion of miR-216a in mice leads to a reduction in islet size, β-cell mass, and insulin levels. Single-cell RNA sequencing reveals a subpopulation of β-cells with upregulated acinar cell markers under a high-fat diet. miR-216a is induced by TGF-β signaling, and inhibition of miR-216a increases apoptosis and decreases cell proliferation in pancreatic cells. Deletion of miR-216a in the pancreatic cancer-prone mouse line KrasG12D;Ptf1aCreER reduces the propensity of pancreatic cancer precursor lesions. Notably, circulating miR-216a levels are elevated in both mice and humans with pancreatic cancer. Collectively, our study gives insights into how β-cell mass and acinar cell growth are modulated by a pancreas-specific miRNA and also suggests miR-216a as a potential biomarker for diagnosis of pancreatic diseases.

Comparison of metabolic states using genome-scale metabolic models

Genome-scale metabolic models (GEMs) are comprehensive knowledge bases of cellular metabolism and serve as mathematical tools for studying biological phenotypes and metabolic states or conditions in various organisms and cell types. Given the sheer size and complexity of human metabolism, selecting parameters for existing analysis methods such as metabolic objective functions and model constraints is not straightforward in human GEMs. In particular, comparing several conditions in large GEMs to identify condition- or disease-specific metabolic features is challenging. In this study, we showcase a scalable, model-driven approach for an in-depth investigation and comparison of metabolic states in large GEMs which enables identifying the underlying functional differences. Using a combination of flux space sampling and network analysis, our approach enables extraction and visualisation of metabolically distinct network modules. Importantly, it does not rely on known or assumed objective functions. We apply this novel approach to extract the biochemical differences in adipocytes arising due to unlimited vs blocked uptake of branched-chain amino acids (BCAAs, considered as biomarkers in obesity) using a human adipocyte GEM (iAdipocytes1809). The biological significance of our approach is corroborated by literature reports confirming our identified metabolic processes (TCA cycle and Fatty acid metabolism) to be functionally related to BCAA metabolism. Additionally, our analysis predicts a specific altered uptake and secretion profile indicating a compensation for the unavailability of BCAAs. Taken together, our approach facilitates determining functional differences between any metabolic conditions of interest by offering a versatile platform for analysing and comparing flux spaces of large metabolic networks.

Cellular metabolism is a highly complex and interconnected system. As many lifestyle diseases in humans have a strong metabolic component, it is important to understand metabolic differences between healthy and diseased states. In systems biology, metabolic behaviours are investigated using genome-scale metabolic models. In addition to the sheer size and complexity of the genome-scale metabolic models of human systems, using existing analysis methods is challenging and the parameter selection is not straightforward. Therefore, novel methodological frameworks are necessary for analysing metabolic conditions despite the challenges posed by human models. Particularly, an ongoing challenge has been that of comparing several phenotypes for identifying condition- or disease-specific metabolic signatures. We address this significant challenge by developing a scalable and model-driven approach, ComMet (Comparison of Metabolic states). ComMet enables an in-depth investigation and comparison of metabolic phenotypes in large models while also identifying the underlying functional differences. Novel hypotheses can be generated using ComMet for not only understanding known metabolic phenotypes better but also for guiding the design of new experiments to validate the processes predicted by ComMet.

Recent ultra-rare inherited variants implicate new autism candidate risk genes

Autism is a highly heritable complex disorder in which de novo mutation (DNM) variation contributes significantly to risk. Using whole-genome sequencing data from 3,474 families, we investigate another source of large-effect risk variation, ultra-rare variants. We report and replicate a transmission disequilibrium of private, likely gene-disruptive (LGD) variants in probands but find that 95% of this burden resides outside of known DNM-enriched genes. This variant class more strongly affects multiplex family probands and supports a multi-hit model for autism. Candidate genes with private LGD variants preferentially transmitted to probands converge on the E3 ubiquitin-protein ligase complex, intracellular transport and Erb signaling protein networks. We estimate that these variants are approximately 2.5 generations old and significantly younger than other variants of similar type and frequency in siblings. Overall, private LGD variants are under strong purifying selection and appear to act on a distinct set of genes not yet associated with autism.

scNetViz: from single cells to networks using Cytoscape

Single-cell RNA-sequencing (scRNA-seq) has revolutionized molecular biology and medicine by enabling high-throughput studies of cellular heterogeneity in diverse tissues. Applying network biology approaches to scRNA-seq data can provide useful insights into genes driving heterogeneous cell-type compositions of tissues. Here, we present scNetViz-  a Cytoscape app to aid biological interpretation of cell clusters in scRNA-seq data using network analysis. scNetViz calculates the differential expression of each gene across clusters and then creates a cluster-specific gene functional interaction network between the significantly differentially expressed genes for further analysis, such as pathway enrichment analysis. To automate a complete data analysis workflow, scNetViz integrates parts of the Scanpy software, which is a popular Python package for scRNA-seq data analysis, with Cytoscape apps such as stringApp, cyPlot, and enhancedGraphics. We describe our implementation of methods for accessing data from public single cell atlas projects, differential expression analysis, visualization, and automation. scNetViz enables users to analyze data from public atlases or their own experiments, which we illustrate with two use cases. Analysis can be performed via the Cytoscape GUI or CyREST programming interface using R (RCy3) or Python (py4cytoscape).

NetSets.js: a JavaScript framework for compositional assessment and comparison of biological networks through Venn-integrated network diagrams

MOTIVATION

Venn diagrams are frequently used to compare composition of datasets (e.g. datasets containing list of proteins and genes). Network diagram constructed using such datasets are usually generated using 'list of edges', popularly known as edge-lists. An edge-list and the corresponding generated network are, however, composed of two elements, namely, edges (e.g. protein-protein interactions) and nodes (e.g. proteins). Researchers often use individual lists of edges and nodes to compare composition of biological networks using existing Venn diagram tools. However, specialized analysis workflows are required for comparison of nodes as well as edges. Apart from this, different tools or graph libraries are needed for visualizing any specific edges of interest (e.g. protein-protein interactions which are present across all networks or are shared between subset of networks or are exclusively present in a selected network). Further, these results are required to be exported in the form of publication worthy network diagram(s), particularly for small networks.

RESULTS

We introduce a (server independent) JavaScript framework (called NetSets.js) that integrates popular Venn and network diagrams in a single application. A free to use intuitive web application (utilizing NetSets.js), specifically designed to perform both compositional comparisons (e.g. for identifying common/exclusive edges or nodes) and interactive user defined visualizations of network (for the identified common/exclusive interactions across multiple networks) using simple edge-lists is also presented. The tool also enables connection to Cytoscape desktop application using the Netsets-Cyapp. We demonstrate the utility of our tool using real world biological networks (microbiome, gene interaction, multiplex and protein-protein interaction networks).

AVAILABILITYAND IMPLEMENTATION

http://web.rniapps.net/netsets (freely available for academic use).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Knowledge Beacons: Web services for data harvesting of distributed biomedical knowledge

The continually expanding distributed global compendium of biomedical knowledge is diffuse, heterogeneous and huge, posing a serious challenge for biomedical researchers in knowledge harvesting: accessing, compiling, integrating and interpreting data, information and knowledge. In order to accelerate research towards effective medical treatments and optimizing health, it is critical that efficient and automated tools for identifying key research concepts and their experimentally discovered interrelationships are developed. As an activity within the feasibility phase of a project called “Translator” (https://ncats.nih.gov/translator) funded by the National Center for Advancing Translational Sciences (NCATS) to develop a biomedical science knowledge management platform, we designed a Representational State Transfer (REST) web services Application Programming Interface (API) specification, which we call a Knowledge Beacon. Knowledge Beacons provide a standardized basic API for the discovery of concepts, their relationships and associated supporting evidence from distributed online repositories of biomedical knowledge. This specification also enforces the annotation of knowledge concepts and statements to the NCATS endorsed the Biolink Model data model and semantic encoding standards (https://biolink.github.io/biolink-model/). Implementation of this API on top of diverse knowledge sources potentially enables their uniform integration behind client software which will facilitate research access and integration of biomedical knowledge.

XlinkCyNET: A Cytoscape Application for Visualization of Protein Interaction Networks Based on Cross-Linking Mass Spectrometry Identifications

Software tools that allow the visualization and analysis of protein interaction networks are essential for studies in systems biology. One of the most popular network visualization tools in biology is Cytoscape, which offers a great selection of plug-ins for the interpretation of network data. Chemical cross-linking coupled to mass spectrometry (XL-MS) is an increasingly important source for protein interaction data; however, to date, no Cytoscape tools are available to analyze XL-MS results. In light of the suitability of the Cytoscape platform and to expand its toolbox, here we introduce XlinkCyNET, an open-source Cytoscape Java plug-in for exploring large-scale XL-MS-based protein interaction networks. XlinkCyNET offers the rapid and easy visualization of intra- and interprotein cross-links in a rectangular-bar style as well as on the 3D structure, allowing the interrogation of protein interaction networks at the residue level. XlinkCyNET is freely available from the Cytoscape App Store (http://apps.cytoscape.org/apps/xlinkcynet) and at the Liu lab webpage (https://www.theliulab.com/software/xlinkcynet).

Noise regularization removes correlation artifacts in single-cell RNA-seq data preprocessing

With the rapid advancement of single-cell RNA-sequencing (scRNA-seq) technology, many data-preprocessing methods have been proposed to address numerous systematic errors and technical variabilities inherent in this technology. While these methods have been demonstrated to be effective in recovering individual gene expression, the suitability to the inference of gene-gene associations and subsequent gene network reconstruction have not been systemically investigated. In this study, we benchmarked five representative scRNA-seq normalization/imputation methods on Human Cell Atlas bone marrow data with respect to their impacts on inferred gene-gene associations. Our results suggested that a considerable amount of spurious correlations was introduced during the data-preprocessing steps due to oversmoothing of the raw data. We proposed a model-agnostic noise-regularization method that can effectively eliminate the correlation artifacts. The noise-regularized gene-gene correlations were further used to reconstruct a gene co-expression network and successfully revealed several known immune cell modules.

Network Analysis of Integrin Adhesion Complexes

Cell-surface adhesion receptors mediate interactions with the extracellular matrix (ECM) to control many fundamental aspects of cell behavior, including cell migration, survival, and proliferation. Integrin adhesion receptors recruit structural and signaling proteins to form multimolecular adhesion complexes that link the plasma membrane to the actomyosin cytoskeleton. The assembly and turnover of adhesion complexes are tightly regulated, governed in part by the networks of physical protein interactions and functional signaling associations between components of the adhesome. Proteomic profiling of adhesion complexes has begun to reveal their molecular complexity and diversity. To interrogate the composition of cell-ECM adhesions, we detail herein an approach for the network analysis of adhesion complex proteomes. Integration of these proteomic data with adhesome databases in the context of predicted protein interactions enables the mapping of experimentally defined adhesion complex networks. Computational analysis of resultant network models can identify subnetworks of putative functionally linked adhesion protein communities. This approach provides a framework to predict functional adhesion protein relationships and generate new mechanistic hypotheses for further experimental testing.

Data mining combined with experiments to validate CEP55 as a prognostic biomarker in colorectal cancer

INTRODUCTION

Colorectal cancer (CRC) is a common tumor with high morbidity and mortality. Current specific diagnosis regarding CRC remains complicated and costly, and specific diagnostic biomarkers are lacking.

METHODS

To find potential diagnostic and prognostic biomarkers for CRC, we screened and analyzed many CRC sequencing data by The Cancer Genome Atlas Program and Gene Expression Omnibus, and validated that CEP55 may be a potential diagnostic biomarker for CRC by molecular cytological experiments and immunohistochemistry, among others.

RESULTS

We found that CEP55 is upregulated in CRC tissues and tumor cells and can promote CRC proliferation and metastasis by activating the p53/p21 axis and that CEP55 mutations in tumor patients result in worse overall survival and disease-free survival time. Besides, we also found that genes, such as CDK1, CCNB1, NEK2, KIF14, CDCA5, and RFC3 were upregulated in tumors, and their mutations would affect the prognosis of CRC patients, but these results await for more experimental evidence.

CONCLUSION

Our study validates CEP55 as a potential diagnostic and prognostic biomarker for CRC, and we also provide multiple genes and potential molecular mechanisms that may serve as diagnostic and prognostic markers for CRC.

Identification of Hub Genes and Key Pathways Associated with Peripheral T-cell Lymphoma

Peripheral T-cell lymphoma (PTCL) is a very aggressive and heterogeneous hematological malignancy and has no effective targeted therapy. The molecular pathogenesis of PTCL remains unknown. In this study, we chose the gene expression profile of GSE6338 from the Gene Expression Omnibus (GEO) database to identify hub genes and key pathways and explore possible molecular pathogenesis of PTCL by bioinformatic analysis. Differentially expressed genes (DEGs) between PTCL and normal T cells were selected using GEO2R tool. Gene ontology (GO) analysis and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis were performed using Database for Annotation, Visualization and Integrated Discovery (DAVID). Moreover, the Search Tool for the Retrieval of Interacting Genes (STRING) and Molecular Complex Detection (MCODE) were utilized to construct protein-protein interaction (PPI) network and perform module analysis of these DEGs. A total of 518 DEGs were identified, including 413 down-regulated and 105 up-regulated genes. The down-regulated genes were enriched in osteoclast differentiation, Chagas disease and mitogen-activated protein kinase (MAPK) signaling pathway. The up-regulated genes were mainly associated with extracellular matrix (ECM)-receptor interaction, focal adhesion and pertussis. Four important modules were detected from the PPI network by using MCODE software. Fifteen hub genes with a high degree of connectivity were selected. Our study identified DEGs, hub genes and pathways associated with PTCL by bioinformatic analysis. Results provide a basis for further study on the pathogenesis of PTCL.

Glomerular endothelial cell heterogeneity in Alport syndrome

Glomerular endothelial cells (GEC) are a crucial component of the glomerular physiology and their damage contributes to the progression of chronic kidney diseases. How GEC affect the pathology of Alport syndrome (AS) however, is unclear. We characterized GEC from wild type (WT) and col4α5 knockout AS mice, a hereditary disorder characterized by progressive renal failure. We used endothelial-specific Tek-tdTomato reporter mice to isolate GEC by FACS and performed transcriptome analysis on them from WT and AS mice, followed by in vitro functional assays and confocal and intravital imaging studies. Biopsies from patients with chronic kidney disease, including AS were compared with our findings in mice. We identified two subpopulations of GEC (dim^tdT and bright^tdT) based on the fluorescence intensity of the Tek^tdT signal. In AS mice, the bright^tdT cell number increased and presented differential expression of endothelial markers compared to WT. RNA-seq analysis revealed differences in the immune and metabolic signaling pathways. In AS mice, dim^tdT and bright^tdT cells had different expression profiles of matrix-associated genes (Svep1, Itgβ6), metabolic activity (Apom, Pgc1α) and immune modulation (Apelin, Icam1) compared to WT mice. We confirmed a new pro-inflammatory role of Apelin in AS mice and in cultured human GEC. Gene modulations were identified comparable to the biopsies from patients with AS and focal segmental glomerulosclerosis, possibly indicating that the same mechanisms apply to humans. We report the presence of two GEC subpopulations that differ between AS and healthy mice or humans. This finding paves the way to a better understanding of the pathogenic role of GEC in AS progression and could lead to novel therapeutic targets.

PINE: An Automation Tool to Extract and Visualize Protein-Centric Functional Networks

Recent surges in mass spectrometry-based proteomics studies demand a concurrent rise in speedy and optimized data processing tools and pipelines. Although several stand-alone bioinformatics tools exist that provide protein-protein interaction (PPI) data, we developed Protein Interaction Network Extractor (PINE) as a fully automated, user-friendly, graphical user interface application for visualization and exploration of global proteome and post-translational modification (PTM) based networks. PINE also supports overlaying differential expression, statistical significance thresholds, and PTM sites on functionally enriched visualization networks to gain insights into proteome-wide regulatory mechanisms and PTM-mediated networks. To illustrate the relevance of the tool, we explore the total proteome and its PTM-associated relationships in two different nonalcoholic steatohepatitis (NASH) mouse models to demonstrate different context-specific case studies. The strength of this tool relies in its ability to (1) perform accurate protein identifier mapping to resolve ambiguity, (2) retrieve interaction data from multiple publicly available PPI databases, and (3) assimilate these complex networks into functionally enriched pathways, ontology categories, and terms. Ultimately, PINE can be used as an extremely powerful tool for novel hypothesis generation to understand underlying disease mechanisms.

An Erg-driven transcriptional program controls B cell lymphopoiesis

B lymphoid development is initiated by the differentiation of hematopoietic stem cells into lineage committed progenitors, ultimately generating mature B cells. This highly regulated process generates clonal immunological diversity via recombination of immunoglobulin V, D and J gene segments. While several transcription factors that control B cell development and V(D)J recombination have been defined, how these processes are initiated and coordinated into a precise regulatory network remains poorly understood. Here, we show that the transcription factor ETS Related Gene (Erg) is essential for early B lymphoid differentiation. Erg initiates a transcriptional network involving the B cell lineage defining genes, Ebf1 and Pax5, which directly promotes expression of key genes involved in V(D)J recombination and formation of the B cell receptor. Complementation of Erg deficiency with a productively rearranged immunoglobulin gene rescued B lineage development, demonstrating that Erg is an essential and stage-specific regulator of the gene regulatory network controlling B lymphopoiesis.

B cell development is tightly regulated in a stepwise manner to ensure proper generation of repertoire diversity via somatic gene rearrangements. Here, the authors show that a transcription factor, Erg, functions at the earliest stage to critically control two downstream factors, Ebf1 and Pax5, for modulating this gene rearrangement process.

Automated exploration of gene ontology term and pathway networks with ClueGO-REST

Summary

Large scale technologies produce massive amounts of experimental data that need to be investigated. To improve their biological interpretation we have developed ClueGO, a Cytoscape App that selects representative Gene Onology terms and pathways for one or multiple lists of genes/proteins and visualizes them into functionally organized networks. Because of its reliability, userfriendliness and support of many species ClueGO gained a large community of users. To further allow scientists programmatic access to ClueGO with R, Python, JavaScript etc., we implemented the cyREST API into ClueGO. In this article we describe this novel, complementary way of accessing ClueGO via REST, and provide R and Phyton examples to demonstrate how ClueGO workflows can be integrated into bioinformatic analysis pipelines.

Availability and implementation

ClueGO is available in the Cytoscape App Store (http://apps.cytoscape.org/apps/cluego).

Supplementary information

Supplementary data are available at Bioinformatics online.

Gaussian and Mixed Graphical Models as (multi-)omics data analysis tools

Gaussian Graphical Models (GGMs) are tools to infer dependencies between biological variables. Popular applications are the reconstruction of gene, protein, and metabolite association networks. GGMs are an exploratory research tool that can be useful to discover interesting relations between genes (functional clusters) or to identify therapeutically interesting genes, but do not necessarily infer a network in the mechanistic sense. Although GGMs are well investigated from a theoretical and applied perspective, important extensions are not well known within the biological community. GGMs assume, for instance, multivariate normal distributed data. If this assumption is violated Mixed Graphical Models (MGMs) can be the better choice. In this review, we provide the theoretical foundations of GGMs, present extensions such as MGMs or multi-class GGMs, and illustrate how those methods can provide insight in biological mechanisms. We summarize several applications and present user-friendly estimation software. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

EFMviz: A COBRA Toolbox extension to visualize Elementary Flux Modes in Genome-Scale Metabolic Models

Elementary Flux Modes (EFMs) are a tool for constraint-based modeling and metabolic network analysis. However, systematic and automated visualization of EFMs, capable of integrating various data types is still a challenge. In this study, we developed an extension for the widely adopted COBRA Toolbox, EFMviz, for analysis and graphical visualization of EFMs as networks of reactions, metabolites and genes. The analysis workflow offers a platform for EFM visualization to improve EFM interpretability by connecting COBRA toolbox with the network analysis and visualization software Cytoscape. The biological applicability of EFMviz is demonstrated in two use cases on medium (Escherichia coli, iAF1260) and large (human, Recon 2.2) genome-scale metabolic models. EFMviz is open-source and integrated into COBRA Toolbox. The analysis workflows used for the two use cases are detailed in the two tutorials provided with EFMviz along with the data used in this study.

Extended Human G-Protein Coupled Receptor Network: Cell-Type-Specific Analysis of G-Protein Coupled Receptor Signaling Pathways

G-protein coupled receptors (GPCRs) mediate crucial physiological functions in humans, have been implicated in an array of diseases, and are therefore prime drug targets. GPCRs signal via a multitude of pathways, mainly through G-proteins and β-arrestins, to regulate effectors responsible for cellular responses. The limited number of transducers results in different GPCRs exerting control on the same pathway, while the availability of signaling proteins in a cell defines the result of GPCR activation. The aim of this study was to construct the extended human GPCR network (hGPCRnet) and examine the effect that cell-type specificity has on GPCR signaling pathways. To achieve this, protein-protein interaction data between GPCRs, G-protein coupled receptor kinases (GRKs), Gα subunits, β-arrestins, and effectors were combined with protein expression data in cell types. This resulted in the hGPCRnet, a very large interconnected network, and similar cell-type-specific networks in which, distinct GPCR signaling pathways were formed. Finally, a user friendly web application, hGPCRnet ( http://bioinformatics.biol.uoa.gr/hGPCRnet ), was created to allow for the visualization and exploration of these networks and of GPCR signaling pathways. This work, and the resulting application, can be useful in further studies of GPCR function and pharmacology.

Identification of core differentially methylated genes in glioma

Differentially methylated genes (DMGs) serve a crucial role in the pathogenesis of glioma via the regulation of the cell cycle, proliferation, apoptosis, migration, infiltration, DNA repair and signaling pathways. This study aimed to identify aberrant DMGs and pathways by comprehensive bioinformatics analysis. The gene expression profile of GSE28094 was downloaded from the Gene Expression Omnibus (GEO) database, and the GEO2R online tool was used to find DMGs. Gene Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of the DMGs were performed by using the Database for Annotation Visualization and Integrated Discovery. A protein-protein interaction (PPI) network was constructed with Search Tool for the Retrieval of Interacting Genes. Analysis of modules in the PPI networks was performed by Molecular Complex Detection in Cytoscape software, and four modules were performed. The hub genes with a high degree of connectivity were verified by The Cancer Genome Atlas database. A total of 349 DMGs, including 167 hypermethylation genes, were enriched in biological processes of negative and positive regulation of cell proliferation and positive regulation of transcription from RNA polymerase II promoter. Pathway analysis enrichment revealed that cancer regulated the pluripotency of stem cells and the PI3K-AKT signaling pathway, whereas 182 hypomethylated genes were enriched in biological processes of immune response, cellular response to lipopolysaccharide and peptidyl-tyrosine phosphorylation. Pathway enrichment analysis revealed cytokine-cytokine receptor interaction, type I diabetes mellitus and TNF signaling pathway. A total of 20 hub genes were identified, of which eight genes were associated with survival, including notch receptor 1 (NOTCH1), SRC proto-oncogene (also known as non-receptor tyrosine kinase, SRC), interleukin 6 (IL6), matrix metallopeptidase 9 (MMP9), interleukin 10 (IL10), caspase 3 (CASP3), erb-b2 receptor tyrosine kinase 2 (ERBB2) and epidermal growth factor (EGF). Therefore, bioinformatics analysis identified a series of core DMGs and pathways in glioma. The results of the present study may facilitate the assessment of the tumorigenicity and progression of glioma. Furthermore, the significant DMGs may provide potential methylation-based biomarkers for the precise diagnosis and targeted treatment of glioma.

RCy3: Network biology using Cytoscape from within R

RCy3 is an R package in Bioconductor that communicates with Cytoscape via its REST API, providing access to the full feature set of Cytoscape from within the R programming environment. RCy3 has been redesigned to streamline its usage and future development as part of a broader Cytoscape Automation effort. Over 100 new functions have been added, including dozens of helper functions specifically for intuitive data overlay operations. Over 40 Cytoscape apps have implemented automation support so far, making hundreds of additional operations accessible via RCy3. Two-way conversion with networks from \textit{igraph} and \textit{graph} ensures interoperability with existing network biology workflows and dozens of other Bioconductor packages. These capabilities are demonstrated in a series of use cases involving public databases, enrichment analysis pipelines, shortest path algorithms and more. With RCy3, bioinformaticians will be able to quickly deliver reproducible network biology workflows as integrations of Cytoscape functions, complex custom analyses and other R packages.

RCy3: Network biology using Cytoscape from within R

RCy3 is an R package in Bioconductor that communicates with Cytoscape via its REST API, providing access to the full feature set of Cytoscape from within the R programming environment. RCy3 has been redesigned to streamline its usage and future development as part of a broader Cytoscape Automation effort. Over 100 new functions have been added, including dozens of helper functions specifically for intuitive data overlay operations. Over 40 Cytoscape apps have implemented automation support so far, making hundreds of additional operations accessible via RCy3. Two-way conversion with networks from \textit{igraph} and \textit{graph} ensures interoperability with existing network biology workflows and dozens of other Bioconductor packages. These capabilities are demonstrated in a series of use cases involving public databases, enrichment analysis pipelines, shortest path algorithms and more. With RCy3, bioinformaticians will be able to quickly deliver reproducible network biology workflows as integrations of Cytoscape functions, complex custom analyses and other R packages.

RCy3: Network biology using Cytoscape from within R

RCy3 is an R package in Bioconductor that communicates with Cytoscape via its REST API, providing access to the full feature set of Cytoscape from within the R programming environment. RCy3 has been redesigned to streamline its usage and future development as part of a broader Cytoscape Automation effort. Over 100 new functions have been added, including dozens of helper functions specifically for intuitive data overlay operations. Over 40 Cytoscape apps have implemented automation support so far, making hundreds of additional operations accessible via RCy3. Two-way conversion with networks from \textit{igraph} and \textit{graph} ensures interoperability with existing network biology workflows and dozens of other Bioconductor packages. These capabilities are demonstrated in a series of use cases involving public databases, enrichment analysis pipelines, shortest path algorithms and more. With RCy3, bioinformaticians will be able to quickly deliver reproducible network biology workflows as integrations of Cytoscape functions, complex custom analyses and other R packages.

Cytoscape Automation: empowering workflow-based network analysis

Cytoscape is one of the most successful network biology analysis and visualization tools, but because of its interactive nature, its role in creating reproducible, scalable, and novel workflows has been limited. We describe Cytoscape Automation (CA), which marries Cytoscape to highly productive workflow systems, for example, Python/R in Jupyter/RStudio. We expose over 270 Cytoscape core functions and 34 Cytoscape apps as REST-callable functions with standardized JSON interfaces backed by Swagger documentation. Independent projects to create and publish Python/R native CA interface libraries have reached an advanced stage, and a number of automation workflows are already published.

Cytoscape StringApp: Network Analysis and Visualization of Proteomics Data

Protein networks have become a popular tool for analyzing and visualizing the often long lists of proteins or genes obtained from proteomics and other high-throughput technologies. One of the most popular sources of such networks is the STRING database, which provides protein networks for more than 2000 organisms, including both physical interactions from experimental data and functional associations from curated pathways, automatic text mining, and prediction methods. However, its web interface is mainly intended for inspection of small networks and their underlying evidence. The Cytoscape software, on the other hand, is much better suited for working with large networks and offers greater flexibility in terms of network analysis, import, and visualization of additional data. To include both resources in the same workflow, we created stringApp, a Cytoscape app that makes it easy to import STRING networks into Cytoscape, retains the appearance and many of the features of STRING, and integrates data from associated databases. Here, we introduce many of the stringApp features and show how they can be used to carry out complex network analysis and visualization tasks on a typical proteomics data set, all through the Cytoscape user interface. stringApp is freely available from the Cytoscape app store: http://apps.cytoscape.org/apps/stringapp .

Comprehensive bioinformation analysis of methylated and differentially expressed genes in esophageal squamous cell carcinoma

Differentially methylated genes (DMGs) play a crucial role in the etiology and pathogenesis of esophageal squamous cell carcinoma (ESCC).

PPIExp: A Web-Based Platform for Integration and Visualization of Protein-Protein Interaction Data and Spatiotemporal Proteomics Data

Integrating spatiotemporal proteomics data with protein-protein interaction (PPI) data can help researchers make an in-depth exploration of their proteins of interest in a dynamic manner. However, there is still a lack of proper tools for the biologists who usually have few programming skills to construct a PPI network for a protein list, visualize active PPI subnetworks, and then select key nodes for further study. We propose a web-based platform named PPIExp that can automatically construct a PPI network, perform clustering analysis according to protein abundances, and perform functional enrichment analysis. More importantly, it provides multiple effective visualization interfaces, such as the interface to display the PPI network map, the interface to display a dendrogram and heatmap for the clustering result, and the interface to display the expression pattern of a selected protein. To visualize the active PPI subnetworks in specific space or time, it provides buttons to highlight the differentially expressed proteins under each condition on the network map. Additionally, to help researchers determine which proteins are worth further attention, PPIExp provides extensive one-click interactive operations to map node centrality measures to node size on the network and highlight three types of proteins, that is, the proteins in an enriched functional term, the coexpressed proteins selected from the dendgrogram and heatmap, and the proteins input by users. PPIExp is available at http://www.fgvis.com/expressvis/PPIExp .

Cytoscape StringApp: Network Analysis and Visualization of Proteomics Data

Protein networks have become a popular tool for analyzing and visualizing the often long lists of proteins or genes obtained from proteomics and other high-throughput technologies. One of the most popular sources of such networks is the STRING database, which provides protein networks for more than 2000 organisms, including both physical interactions from experimental data and functional associations from curated pathways, automatic text mining, and prediction methods. However, its web interface is mainly intended for inspection of small networks and their underlying evidence. The Cytoscape software, on the other hand, is much better suited for working with large networks and offers greater flexibility in terms of network analysis, import, and visualization of additional data. To include both resources in the same workflow, we created stringApp, a Cytoscape app that makes it easy to import STRING networks into Cytoscape, retains the appearance and many of the features of STRING, and integrates data from associated databases. Here, we introduce many of the stringApp features and show how they can be used to carry out complex network analysis and visualization tasks on a typical proteomics data set, all through the Cytoscape user interface. stringApp is freely available from the Cytoscape app store: http://apps.cytoscape.org/apps/stringapp .

Identification of Key Residues in Proteins Through Centrality Analysis and Flexibility Prediction with RINspector

Protein structures inherently contain information that can be used to decipher their functions, but the exploitation of this knowledge is not trivial. We recently developed an app for the Cytoscape network visualization and analysis program, called RINspector, the goal of which is to integrate two different approaches that identify key residues in a protein structure or complex. The first approach consists of calculating centralities on a residue interaction network (RIN) generated from the three-dimensional structure; the second consists of predicting backbone flexibility and needs only the primary sequence. The identified residues are highly correlated with functional relevance and constitute a good set of targets for mutagenesis experiments. Here we present a protocol that details in a step-by-step fashion how to create a RIN from a structure and then calculate centralities and predict flexibilities. We also discuss how to understand and use the results of the analyses. © 2018 by John Wiley & Sons, Inc.

MetGem Software for the Generation of Molecular Networks Based on the t-SNE Algorithm

Molecular networking (MN) is becoming a standard bioinformatics tool in the metabolomic community. Its paradigm is based on the observation that compounds with a high degree of chemical similarity share comparable MS² fragmentation pathways. To afford a clear separation between MS² spectral clusters, only the most relevant similarity scores are selected using dedicated filtering steps requiring time-consuming parameter optimization. Depending on the filtering values selected, some scores are arbitrarily deleted and a part of the information is ignored. The problem of creating a reliable representation of MS² spectra data sets can be solved using algorithms developed for dimensionality reduction and pattern recognition purposes, such as t-distributed stochastic neighbor embedding (t-SNE). This multivariate embedding method pays particular attention to local details by using nonlinear outputs to represent the entire data space. To overcome the limitations inherent to the GNPS workflow and the networking architecture, we developed MetGem. Our software allows the parallel investigation of two complementary representations of the raw data set, one based on a classic GNPS-style MN and another based on the t-SNE algorithm. The t-SNE graph preserves the interactions between related groups of spectra, while the MN output allows an unambiguous separation of clusters. Additionally, almost all parameters can be tuned in real time, and new networks can be generated within a few seconds for small data sets. With the development of this unified interface ( https://metgem.github.io ), we fulfilled the need for a dedicated, user-friendly, local software for MS² comparison and spectral network generation.

mully: An R Package to Create, Modify and Visualize Multilayered Graphs

The modelling of complex biological networks such as pathways has been a necessity for scientists over the last decades. The study of these networks also imposes a need to investigate different aspects of nodes or edges within the networks, or other biomedical knowledge related to it. Our aim is to provide a generic modelling framework to integrate multiple pathway types and further knowledge sources influencing these networks. This framework is defined by a multi-layered model allowing automatic network transformations and documentation. By providing a tool that generates this model, we aim to facilitate the data integration, boost the reproducibility and increase the interoperability between different sources and databases in the field of pathways. We present mully R package that allows the user to create, modify and visualize graphs with multi-layers. The package is implemented with features to specifically handle multilayered graphs.

Copycat Layout: Network layout alignment via Cytoscape Automation

The copycatLayout app is a network-based visual differential analysis tool that improves upon the existing layoutSaver app and is delivered pre-installed with Cytoscape, beginning with v3.6.0. LayoutSaver cloned a network layout by mapping node locations from one network to another based on node attribute values, but failed to clone view scale and location, and provided no means of identifying which nodes were successfully mapped between networks. Copycat addresses these issues and provides additional layout options.

With the advent of Cytoscape Automation (packaged in Cytoscape v3.6.0), researchers can utilize the Copycat layout and its output in workflows written in their language of choice by using only a few simple REST calls. Copycat enables researchers to visually compare groups of homologous genes, generate network comparison images for publications, and quickly identify differences between similar networks at a glance without leaving their script. With a few extra REST calls, scripts can discover nodes present in one network but not in the other, which can feed into more complex analyses (e.g., modifying mismatched nodes based on new data, then re-running the layout to highlight additional network changes).

aMatReader: Importing adjacency matrices via Cytoscape Automation

Adjacency matrices are useful for storing pairwise interaction data, such as correlations between gene pairs in a pathway or similarities between genes and conditions. The aMatReader app enables users to import one or multiple adjacency matrix files into Cytoscape, where each file represents an edge attribute in a network. Our goal was to import the diverse adjacency matrix formats produced by existing scripts and libraries written in R, MATLAB, and Python, and facilitate importing that data into Cytoscape. To accelerate the import process, aMatReader attempts to predict matrix import parameters by analyzing the first two lines of the file. We also exposed CyREST endpoints to allow researchers to import network matrix data directly into Cytoscape from their language of choice. Many analysis tools deal with networks in the form of an adjacency matrix, and exposing the aMatReader API to automation users enables scripts to transfer those networks directly into Cytoscape with little effort.