VisANT 3.5: multi-scale network visualization, analysis and inference based on the gene ontology

In silico analysis of NAC gene family in the mangrove plant Avicennia marina provides clues for adaptation to intertidal habitats

NAC (NAM, ATAF1/2, CUC2) transcription factors (TFs) constitute a plant-specific gene family. It is reported that NAC TFs play important roles in plant growth and developmental processes and in response to biotic/abiotic stresses. Nevertheless, little information is known about the functional and evolutionary characteristics of NAC TFs in mangrove plants, a group of species adapting coastal intertidal habitats. Thus, we conducted a comprehensive investigation for NAC TFs in Avicennia marina, one pioneer species of mangrove plants. We totally identified 142 NAC TFs from the genome of A. marina. Combined with NAC proteins having been functionally characterized in other organisms, we built a phylogenetic tree to infer the function of NAC TFs in A. marina. Gene structure and motif sequence analyses suggest the sequence conservation and transcription regulatory regions-mediated functional diversity. Whole-genome duplication serves as the driver force to the evolution of NAC gene family. Moreover, two pairs of NAC genes were identified as positively selected genes of which AmNAC010/040 may be imposed on less constraint toward neofunctionalization. Quite a few stress/hormone-related responsive elements were found in promoter regions indicating potential response to various external factors. Transcriptome data revealed some NAC TFs were involved in pneumatophore and leaf salt gland development and response to salt, flooding and Cd stresses. Gene co-expression analysis found a few NAC TFs participates in the special biological processes concerned with adaptation to intertidal environment. In summary, this study provides detailed functional and evolutionary information about NAC gene family in mangrove plant A. marina and new perspective for adaptation to intertidal habitats.

Review and assessment of Boolean approaches for inference of gene regulatory networks

Boolean descriptions of gene regulatory networks can provide an insight into interactions between genes. Boolean networks hold predictive power, are easy to understand, and can be used to simulate the observed networks in different scenarios. We review fundamental and state-of-the-art methods for inference of Boolean networks. We introduce a methodology for a straightforward evaluation of Boolean inference approaches based on the generation of evaluation datasets, application of selected inference methods, and evaluation of performance measures to guide the selection of the best method for a given inference problem. We demonstrate this procedure on inference methods REVEAL (REVerse Engineering ALgorithm), Best-Fit Extension, MIBNI (Mutual Information-based Boolean Network Inference), GABNI (Genetic Algorithm-based Boolean Network Inference) and ATEN (AND/OR Tree ENsemble algorithm), which infers Boolean descriptions of gene regulatory networks from discretised time series data. Boolean inference approaches tend to perform better in terms of dynamic accuracy, and slightly worse in terms of structural correctness. We believe that the proposed methodology and provided guidelines will help researchers to develop Boolean inference approaches with a good predictive capability while maintaining structural correctness and biological relevance.

NORMA: The Network Makeup Artist - A Web Tool for Network Annotation Visualization

The Network Makeup Artist (NORMA) is a web tool for interactive network annotation visualization and topological analysis, able to handle multiple networks and annotations simultaneously. Precalculated annotations (e.g., Gene Ontology, Pathway enrichment, community detection, or clustering results) can be uploaded and visualized in a network, either as colored pie-chart nodes or as color-filled areas in a 2D/3D Venn-diagram-like style. In the case where no annotation exists, algorithms for automated community detection are offered. Users can adjust the network views using standard layout algorithms or allow NORMA to slightly modify them for visually better group separation. Once a network view is set, users can interactively select and highlight any group of interest in order to generate publication-ready figures. Briefly, with NORMA, users can encode three types of information simultaneously. These are 1) the network, 2) the communities or annotations of interest, and 3) node categories or expression values. Finally, NORMA offers basic topological analysis and direct topological comparison across any of the selected networks. NORMA service is available at http://norma.pavlopouloslab.info, whereas the code is available at https://github.com/PavlopoulosLab/NORMA.

Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review

Technological advances in high-throughput techniques have resulted in tremendous growth of complex biological datasets providing evidence regarding various biomolecular interactions. To cope with this data flood, computational approaches, web services, and databases have been implemented to deal with issues such as data integration, visualization, exploration, organization, scalability, and complexity. Nevertheless, as the number of such sets increases, it is becoming more and more difficult for an end user to know what the scope and focus of each repository is and how redundant the information between them is. Several repositories have a more general scope, while others focus on specialized aspects, such as specific organisms or biological systems. Unfortunately, many of these databases are self-contained or poorly documented and maintained. For a clearer view, in this article we provide a comprehensive categorization, comparison and evaluation of such repositories for different bioentity interaction types. We discuss most of the publicly available services based on their content, sources of information, data representation methods, user-friendliness, scope and interconnectivity, and we comment on their strengths and weaknesses. We aim for this review to reach a broad readership varying from biomedical beginners to experts and serve as a reference article in the field of Network Biology.

Mapping the multiscale structure of biological systems

Biological systems are by nature multiscale, consisting of subsystems that factor into progressively smaller units in a deeply hierarchical structure. At any level of the hierarchy, an ever-increasing diversity of technologies can be applied to characterize the corresponding biological units and their relations, resulting in large networks of physical or functional proximities-e.g., proximities of amino acids within a protein, of proteins within a complex, or of cell types within a tissue. Here, we review general concepts and progress in using network proximity measures as a basis for creation of multiscale hierarchical maps of biological systems. We discuss the functionalization of these maps to create predictive models, including those useful in translation of genotype to phenotype, along with strategies for model visualization and challenges faced by multiscale modeling in the near future. Collectively, these approaches enable a unified hierarchical approach to biological data, with application from the molecular to the macroscopic.

Tracing the footsteps of autophagy in computational biology

Autophagy plays a crucial role in maintaining cellular homeostasis through the degradation of unwanted materials like damaged mitochondria and misfolded proteins. However, the contribution of autophagy toward a healthy cell environment is not only limited to the cleaning process. It also assists in protein synthesis when the system lacks the amino acids’ inflow from the extracellular environment due to diet consumptions. Reduction in the autophagy process is associated with diseases like cancer, diabetes, non-alcoholic steatohepatitis, etc., while uncontrolled autophagy may facilitate cell death. We need a better understanding of the autophagy processes and their regulatory mechanisms at various levels (molecules, cells, tissues). This demands a thorough understanding of the system with the help of mathematical and computational tools. The present review illuminates how systems biology approaches are being used for the study of the autophagy process. A comprehensive insight is provided on the application of computational methods involving mathematical modeling and network analysis in the autophagy process. Various mathematical models based on the system of differential equations for studying autophagy are covered here. We have also highlighted the significance of network analysis and machine learning in capturing the core regulatory machinery governing the autophagy process. We explored the available autophagic databases and related resources along with their attributes that are useful in investigating autophagy through computational methods. We conclude the article addressing the potential future perspective in this area, which might provide a more in-depth insight into the dynamics of autophagy.

Analysis of the metabolic pathways affected by hot-humid or dry climate based on fecal metabolomics coupled with serum metabolic changes in broiler chickens

Air temperature and relative humidity (RH) are 2 important climatic elements that affect animal welfare and health. The prevailing hot and humid or dry climate is one of the major constraints for optimum poultry production, especially in the tropics and subtropical regions. Many studies have suggested that exposure to hot-humid or dry climate is associated with a high risk of metabolic imbalance; however, the underlying metabolic changes caused by low or high RH climate is not yet well understood. Therefore, we used a comprehensive ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry–based metabolic profiling of fecal samples to explore the effects of hot-humid and dry climate on metabolic pathways in broilers. A total of 180 twenty-eight-day-old Arbor Acres broilers were randomly allocated to 3 treatments, each containing 6 replicates of 10 birds per treatment, using a completely randomized design. Birds were reared at 35, 60, or 85% RH at 32°C (temperature increased by 3°C every 3 d from 20°C to 32°C within 15 d: 20°C, 23°C, 26°C, 29°C, 32°C) for 15 d. Results showed that significant changes in the levels of 36 metabolites were detected. Evidence of changes in gluconeogenesis associated to pyruvate metabolism, galactose metabolism, and ABC transporter was observed. In addition, hot-humid and dry stress also affected the protein translation process caused by aminoacyl-tRNA biosynthesis, which may be associated with protein synthesis and hormone secretion disorders. Furthermore, we observed significant changes in primary bile acid biosynthesis and taurine and hypotaurine metabolism, which indicated that fat synthesis was affected. We also observed significant changes in arginine and proline metabolism and histidine metabolism, which were associated with skin vasodilation and blood flow. These results provide biochemical insights into metabolic changes due to hot-humid or dry climate.

A Guide to Conquer the Biological Network Era Using Graph Theory

Networks are one of the most common ways to represent biological systems as complex sets of binary interactions or relations between different bioentities. In this article, we discuss the basic graph theory concepts and the various graph types, as well as the available data structures for storing and reading graphs. In addition, we describe several network properties and we highlight some of the widely used network topological features. We briefly mention the network patterns, motifs and models, and we further comment on the types of biological and biomedical networks along with their corresponding computer- and human-readable file formats. Finally, we discuss a variety of algorithms and metrics for network analyses regarding graph drawing, clustering, visualization, link prediction, perturbation, and network alignment as well as the current state-of-the-art tools. We expect this review to reach a very broad spectrum of readers varying from experts to beginners while encouraging them to enhance the field further.

A new grid- and modularity-based layout algorithm for complex biological networks

The visualization of biological networks is critically important to aid researchers in understanding complex biological systems and arouses interest in designing efficient layout algorithms to draw biological networks according to their topology structures, especially for those networks with potential modules. The algorithms of grid layout series have an advantage in generating compact layouts with overlap-free nodes compared to force-directed; however, extant grid layout algorithms have difficulty in drawing modular networks and often generate layouts of high visual complexity when applied to networks with dense or clustered connectivity structure. To specifically assist the study of modular networks, we propose a grid- and modularity-based layout algorithm (GML) that consists of three stages: network preprocessing, module layout and grid optimization. The algorithm can draw complex biological networks with or without predefined modules based on the grid layout algorithm. It also outperforms other existing grid-based algorithms in the measurement of computation performance, ratio of edge-edge/node-edge crossings, relative edge lengths, and connectivity F-measures. GML helps users to gain insight into the network global characteristics through module layout, as well as to discern network details with grid optimization. GML has been developed as a VisANT plugin (https://hscz.github.io/Biological-Network-Visualization/) and is freely available to the research community.

NIPS, a 3D network-integrated predictor of deleterious protein SAPs, and its application in cancer prognosis

Identifying deleterious mutations remains a challenge in cancer genome sequencing projects, reflecting the vast number of candidate mutations per tumour and the existence of interpatient heterogeneity. Based on a 3D protein interaction network profiled via large-scale cross-linking mass spectrometry, we propose a weighted average formula involving the combination of three types of information into a 'meta-score'. We assume that a single amino acid polymorphism (SAP) may have a deleterious effect if the mutation rarely occurs naturally during evolution, if it inhibits binding between a pair of interacting proteins when located at their interface, or if it plays an important role in a protein interaction (PPI) network. Cross-validation indicated that this new method presents an AUC value of 0.93 and outperforms other widely used tools. The application of this method to the CPTAC colorectal cancer dataset enabled the accurate identification of validated deleterious mutations and yielded insights into their potential pathogenesis. Survival analysis showed that the accumulation of deleterious SAPs is significantly associated with a poor prognosis. The new method provides an alternative method to identifying and ranking deleterious cancer SAPs based on a 3D PPI network and will contribute to the understanding of pathogenesis and the discovery of prognostic biomarkers.

Bipartite graphs in systems biology and medicine: a survey of methods and applications

The latest advances in high-throughput techniques during the past decade allowed the systems biology field to expand significantly. Today, the focus of biologists has shifted from the study of individual biological components to the study of complex biological systems and their dynamics at a larger scale. Through the discovery of novel bioentity relationships, researchers reveal new information about biological functions and processes. Graphs are widely used to represent bioentities such as proteins, genes, small molecules, ligands, and others such as nodes and their connections as edges within a network. In this review, special focus is given to the usability of bipartite graphs and their impact on the field of network biology and medicine. Furthermore, their topological properties and how these can be applied to certain biological case studies are discussed. Finally, available methodologies and software are presented, and useful insights on how bipartite graphs can shape the path toward the solution of challenging biological problems are provided.

Spatial-temporal transcriptional dynamics of long non-coding RNAs in human brain

The functional architecture of the human brain is greatly determined by the temporal and spatial regulation of the transcription process. However, the spatial and temporal transcriptional landscape of long non-coding RNAs (lncRNAs) during human brain development remains poorly understood. Here, we report the genome-wide lncRNA transcriptional analysis in an extensive series of 1340 post-mortem human brain specimens collected from 16 regions spanning the period from early embryo development to late adulthood. We discovered that lncRNA transcriptome dramatically changed during fetal development, while transited to a surprisingly relatively stable state after birth till the late adulthood. We also discovered that the transcription map of lncRNAs was spatially different, and that this spatial difference was developmentally regulated. Of the 16 brain regions explored (cerebellar cortex, thalamus, striatum, amygdala, hippocampus and 11 neocortex areas), cerebellar cortex showed the most distinct lncRNA expression features from all remaining brain regions throughout the whole developmental period, reflecting its unique developmental and functional features. Furthermore, by characterizing the functional modules and cellular processes of the spatial-temporal dynamic lncRNAs, we found that they were significantly associated with the RNA processing, neuron differentiation and synaptic signal transportation processes. Furthermore, we found that many lncRNAs associated with the neurodegenerative Alzheimer and Parkinson diseases were co-expressed in the fetal development of the human brain, and affected the convergent biological processes. In summary, our study provides a comprehensive map for lncRNA transcription dynamics in human brain development, which might shed light on the understanding of the molecular underpinnings of human brain function and disease.

Identification of breast cancer candidate genes using gene co-expression and protein-protein interaction information

Breast cancer (BC) is one of the most common malignancies that could threaten female health. As the molecular mechanism of BC has not yet been completely discovered, identification of related genes of this disease is an important area of research that could provide new insights into gene function as well as potential treatment targets. Here we used subnetwork extraction algorithms to identify novel BC related genes based on the known BC genes (seed genes), gene co-expression profiles and protein-protein interaction network. We computationally predicted seven key genes (EPHX2, GHRH, PPYR1, ALPP, KNG1, GSK3A and TRIT1) as putative genes of BC. Further analysis shows that six of these have been reported as breast cancer associated genes, and one (PPYR1) as cancer associated gene. Lastly, we developed an expression signature using these seven key genes which significantly stratified 1660 BC patients according to relapse free survival (hazard ratio [HR], 0.55; 95% confidence interval [CI], 0.46–0.65; Logrank p = 5.5e−13). The 7-genes signature could be established as a useful predictor of disease prognosis in BC patients. Overall, the identified seven genes might be useful prognostic and predictive molecular markers to predict the clinical outcome of BC patients.

Neoteric advancement in TB drugs and an overview on the anti-tubercular role of peptides through computational approaches

Tuberculosis (TB) is a devastating threat to human health whose treatment without the emergence of drug resistant Mycobacterium tuberculosis (M. tuberculosis) is the million-dollar question at present. The pathogenesis of M. tuberculosis has been extensively studied which represents unique defence strategies by infecting macrophages. Several anti-tubercular drugs with varied mode of action and administration from diversified sources have been used for the treatment of TB that later contributed to the emergence of multidrug-resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB). However, few of potent anti-tubercular drugs are scheduled for clinical trials status in 2017-2018. Peptides of varied origins such as human immune cells and non-immune cells, bacteria, fungi, and venoms have been widely investigated as anti-tubercular agents for the replacement of existing anti-tubercular drugs in future. In the present review, we spotlighted not only on the mechanisms of action and mode of administration of currently available anti-tubercular drugs but also the recent comprehensive report of World Health Organization (WHO) on TB epidemic, diagnosis, prevention, and treatment. The major excerpt of the study also inspects the direct contribution of different computational tools during drug designing strategies against M. tuberculosis in order to grasp the interplay between anti-tubercular peptides and targeted bacterial protein. The potentiality of some of these anti-tubercular peptides as therapeutic agents unlocks a new portal for achieving the goal of end TB strategy.

UHPLC-Q-TOF/MS based plasma metabolomics reveals the metabolic perturbations by manganese exposure in rat models

Although manganese (Mn) is an essential metal ion biological cofactor, high concentrations could potentially induce an accumulation in the brain and lead to manganism. However, there is no "gold standard" for manganism assessment due to a lack of objective biomarkers. We hypothesized that Mn-induced alterations are associated with metabolic responses to manganism. Here we use an untargeted metabolomics approach by performing ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS) on control and Mn-treated rat plasma, to identify metabolic disruptions under high Mn exposure conditions. Sprague-Dawley rats had access to deionized drinking water that was either Mn-free or contained 200 mg Mn per L for 5 weeks. Mn-exposure significantly increased liver Mn concentration in comparison with the control, and also resulted in extensive necrosis and dissolved nuclei, which suggested liver damage from hepatic histopathology. Principal component analysis readily distinguished the metabolomes between the control group and the Mn-treated group. Using multivariate and univariate analysis, Mn significantly altered the concentrations of 36 metabolites (12 metabolites showed a remarkable increase in number and 24 metabolites reduced significantly in concentration) in the plasma of the Mn-treated group. Major alterations were observed for purine metabolism, amino acid metabolism and fatty acid metabolism. These data provide metabolic evidence and putative biomarkers for the Mn-induced alterations in plasma metabolism. The targets of these metabolites have the potential to improve our understanding of cell-level Mn trafficking and homeostatic mechanisms.

Protein complex analysis: From raw protein lists to protein interaction networks

The elucidation of molecular interaction networks is one of the pivotal challenges in the study of biology. Affinity purification-mass spectrometry and other co-complex methods have become widely employed experimental techniques to identify protein complexes. These techniques typically suffer from a high number of false negatives and false positive contaminants due to technical shortcomings and purification biases. To support a diverse range of experimental designs and approaches, a large number of computational methods have been proposed to filter, infer and validate protein interaction networks from experimental pull-down MS data. Nevertheless, this expansion of available methods complicates the selection of the most optimal ones to support systems biology-driven knowledge extraction. In this review, we give an overview of the most commonly used computational methods to process and interpret co-complex results, and we discuss the issues and unsolved problems that still exist within the field. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 36:600-614, 2017.

Identification of candidate genes related to pancreatic cancer based on analysis of gene co-expression and protein-protein interaction network

Pancreatic cancer (PC) is one of the most common causes of cancer mortality worldwide. As the genetic mechanism of this complex disease is not uncovered clearly, identification of related genes of PC is of great significance that could provide new insights into gene function as well as potential therapy targets. In this study, we performed an integrated network method to discover PC candidate genes based on known PC related genes. Utilizing the subnetwork extraction algorithm with gene co-expression profiles and protein-protein interaction data, we obtained the integrated network comprising of the known PC related genes (denoted as seed genes) and the putative genes (denoted as linker genes). We then prioritized the linker genes based on their network information and inferred six key genes (KRT19, BARD1, MST1R, S100A14, LGALS1 and RNF168) as candidate genes of PC. Further analysis indicated that all of these genes have been reported as pancreatic cancer associated genes. Finally, we developed an expression signature using these six key genes which significantly stratified PC patients according to overall survival (Logrank p = 0.003) and was validated on an independent clinical cohort (Logrank p = 0.03). Overall, the identified six genes might offer helpful prognostic stratification information and be suitable to transfer to clinical use in PC patients.

Empirical Comparison of Visualization Tools for Larger-Scale Network Analysis

Gene expression, signal transduction, protein/chemical interactions, biomedical literature cooccurrences, and other concepts are often captured in biological network representations where nodes represent a certain bioentity and edges the connections between them. While many tools to manipulate, visualize, and interactively explore such networks already exist, only few of them can scale up and follow today's indisputable information growth. In this review, we shortly list a catalog of available network visualization tools and, from a user-experience point of view, we identify four candidate tools suitable for larger-scale network analysis, visualization, and exploration. We comment on their strengths and their weaknesses and empirically discuss their scalability, user friendliness, and postvisualization capabilities.

Computational dynamic approaches for temporal omics data with applications to systems medicine

Modeling and predicting biological dynamic systems and simultaneously estimating the kinetic structural and functional parameters are extremely important in systems and computational biology. This is key for understanding the complexity of the human health, drug response, disease susceptibility and pathogenesis for systems medicine. Temporal omics data used to measure the dynamic biological systems are essentials to discover complex biological interactions and clinical mechanism and causations. However, the delineation of the possible associations and causalities of genes, proteins, metabolites, cells and other biological entities from high throughput time course omics data is challenging for which conventional experimental techniques are not suited in the big omics era. In this paper, we present various recently developed dynamic trajectory and causal network approaches for temporal omics data, which are extremely useful for those researchers who want to start working in this challenging research area. Moreover, applications to various biological systems, health conditions and disease status, and examples that summarize the state-of-the art performances depending on different specific mining tasks are presented. We critically discuss the merits, drawbacks and limitations of the approaches, and the associated main challenges for the years ahead. The most recent computing tools and software to analyze specific problem type, associated platform resources, and other potentials for the dynamic trajectory and interaction methods are also presented and discussed in detail.

Weighted gene co-expression network analysis of gene modules for the prognosis of esophageal cancer

Esophageal cancer is a common malignant tumor, whose pathogenesis and prognosis factors are not fully understood. This study aimed to discover the gene clusters that have similar functions and can be used to predict the prognosis of esophageal cancer. The matched microarray and RNA sequencing data of 185 patients with esophageal cancer were downloaded from The Cancer Genome Atlas (TCGA), and gene co-expression networks were built without distinguishing between squamous carcinoma and adenocarcinoma. The result showed that 12 modules were associated with one or more survival data such as recurrence status, recurrence time, vital status or vital time. Furthermore, survival analysis showed that 5 out of the 12 modules were related to progression-free survival (PFS) or overall survival (OS). As the most important module, the midnight blue module with 82 genes was related to PFS, apart from the patient age, tumor grade, primary treatment success, and duration of smoking and tumor histological type. Gene ontology enrichment analysis revealed that "glycoprotein binding" was the top enriched function of midnight blue module genes. Additionally, the blue module was the exclusive gene clusters related to OS. Platelet activating factor receptor (PTAFR) and feline Gardner-Rasheed (FGR) were the top hub genes in both modeling datasets and the STRING protein interaction database. In conclusion, our study provides novel insights into the prognosis-associated genes and screens out candidate biomarkers for esophageal cancer.

Methods, Tools and Current Perspectives in Proteogenomics

With combined technological advancements in high-throughput next-generation sequencing and deep mass spectrometry-based proteomics, proteogenomics, i.e. the integrative analysis of proteomic and genomic data, has emerged as a new research field. Early efforts in the field were focused on improving protein identification using sample-specific genomic and transcriptomic sequencing data. More recently, integrative analysis of quantitative measurements from genomic and proteomic studies have identified novel insights into gene expression regulation, cell signaling, and disease. Many methods and tools have been developed or adapted to enable an array of integrative proteogenomic approaches and in this article, we systematically classify published methods and tools into four major categories, (1) Sequence-centric proteogenomics; (2) Analysis of proteogenomic relationships; (3) Integrative modeling of proteogenomic data; and (4) Data sharing and visualization. We provide a comprehensive review of methods and available tools in each category and highlight their typical applications.

Genome to Phenome: A Systems Biology Approach to PTSD Using an Animal Model

Post-traumatic stress disorder (PTSD) is a debilitating illness that imposes significant emotional and financial burdens on military families. The understanding of PTSD etiology remains elusive; nonetheless, it is clear that PTSD is manifested by a cluster of symptoms including hyperarousal, reexperiencing of traumatic events, and avoidance of trauma reminders. With these characteristics in mind, several rodent models have been developed eliciting PTSD-like features. Animal models with social dimensions are of particular interest, since the social context plays a major role in the development and manifestation of PTSD.For civilians, a core trauma that elicits PTSD might be characterized by a singular life-threatening event such as a car accident. In contrast, among war veterans, PTSD might be triggered by repeated threats and a cumulative psychological burden that coalesced in the combat zone. In capturing this fundamental difference, the aggressor-exposed social stress (Agg-E SS) model imposes highly threatening conspecific trauma on naïve mice repeatedly and randomly.There is abundant evidence that suggests the potential role of genetic contributions to risk factors for PTSD. Specific observations include putatively heritable attributes of the disorder, the cited cases of atypical brain morphology, and the observed neuroendocrine shifts away from normative. Taken together, these features underscore the importance of multi-omics investigations to develop a comprehensive picture. More daunting will be the task of downstream analysis with integration of these heterogeneous genotypic and phenotypic data types to deliver putative clinical biomarkers. Researchers are advocating for a systems biology approach, which has demonstrated an increasingly robust potential for integrating multidisciplinary data. By applying a systems biology approach here, we have connected the tissue-specific molecular perturbations to the behaviors displayed by mice subjected to Agg-E SS. A molecular pattern that links the atypical fear plasticity to energy deficiency was thereby identified to be causally associated with many behavioral shifts and transformations.PTSD is a multifactorial illness sensitive to environmental influence. Accordingly, it is essential to employ the optimal animal model approximating the environmental condition that elicits PTSD-like symptoms. Integration of an optimal animal model with a systems biology approach can contribute to a more knowledge-driven and efficient next-generation care management system and, potentially, prevention of PTSD.

PhenoStacks: Cross-Sectional Cohort Phenotype Comparison Visualizations

Cross-sectional phenotype studies are used by genetics researchers to better understand how phenotypes vary across patients with genetic diseases, both within and between cohorts. Analyses within cohorts identify patterns between phenotypes and patients (e.g., co-occurrence) and isolate special cases (e.g., potential outliers). Comparing the variation of phenotypes between two cohorts can help distinguish how different factors affect disease manifestation (e.g., causal genes, age of onset, etc.). PhenoStacks is a novel visual analytics tool that supports the exploration of phenotype variation within and between cross-sectional patient cohorts. By leveraging the semantic hierarchy of the Human Phenotype Ontology, phenotypes are presented in context, can be grouped and clustered, and are summarized via overviews to support the exploration of phenotype distributions. The design of PhenoStacks was motivated by formative interviews with genetics researchers: we distil high-level tasks, present an algorithm for simplifying ontology topologies for visualization, and report the results of a deployment evaluation with four expert genetics researchers. The results suggest that PhenoStacks can help identify phenotype patterns, investigate data quality issues, and inform data collection design.

Bcl-2 overexpression contributes to laryngeal carcinoma cell survival by forming a complex with Hsp90β

Laryngeal carcinoma (LC) is one of the most common malignant tumors of all head and neck squamous cell carcinomas (HNSCCs). However, the molecular mechanism and genetic basis of the development of LC have not been fully elucidated. To explore the possible mechanism, targeted proteomic analysis was performed on Bcl-2-associated proteins from LC cells. According to our results, 35 proteins associated with Bcl-2 were identified and Hsp90β was confirmed by co-immunoprecipitation and western blot analysis. Protein‑protein interaction (PPI) analysis indicated that Bcl-2‑Hsp90β interactions may be involved in the anti-apoptotic progression of LC. Further results revealed that disruption of the Bcl-2-Hsp90β interaction inhibited the anti-apoptotic ability of Bcl-2 and decreased the caspase activation in LC, which has broad implications for the better understanding of tumor formation, tumor cell survival and development of metastasis due to Bcl-2. Collectively, we report the mechanism by which Bcl-2 functions in LC as an anti-apoptotic factor in relation to its association with proteins and potentially identify a Bcl-2/Hsp90β axis as a novel target for LC therapy.

Undiscovered Physiology of Transcript and Protein Networks

The past two decades have witnessed a rapid evolution in our ability to measure RNA and protein from biological systems. As a result, new principles have arisen regarding how information is processed in cells, how decisions are made, and the role of networks in biology. This essay examines this technological evolution, reviewing (and critiquing) the conceptual framework that has emerged to explain how RNA and protein networks control cellular function. We identify how future investigations into transcriptomes, proteomes, and other cellular networks will enable development of more robust, quantitative models of cellular behavior whilst also providing new avenues to use knowledge of biological networks to improve human health. © 2016 American Physiological Society. Compr Physiol 6:1851-1872, 2016.

Visualization of Metabolic Interaction Networks in Microbial Communities Using VisANT 5.0

The complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space), a dynamic stoichiometric modeling framework. Using VisANT's unique metagraph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction network between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the "symbiotic layout" of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues. VisANT is freely available at: http://visant.bu.edu and COMETS at http://comets.bu.edu.

Systems Analysis for Interpretation of Phosphoproteomics Data

Global phosphoproteomics investigations yield overwhelming datasets with up to tens of thousands of quantified phosphosites. The main challenge after acquiring such large-scale data is to extract the biological meaning and relate this to the experimental question at hand. Systems level analysis provides the best means for extracting functional insights from such types of datasets, and this has primed a rapid development of bioinformatics tools and resources over the last decade. Many of these tools are specialized databases that can be mined for annotation and pathway enrichment, whereas others provide a platform to generate functional protein networks and explore the relations between proteins of interest. The use of these tools requires careful consideration with regard to the input data, and the interpretation demands a critical approach. This chapter provides a summary of the most appropriate tools for systems analysis of phosphoproteomics datasets, and the considerations that are critical for acquiring meaningful output.

KnowEnG: a knowledge engine for genomics

We describe here the vision, motivations, and research plans of the National Institutes of Health Center for Excellence in Big Data Computing at the University of Illinois, Urbana-Champaign. The Center is organized around the construction of "Knowledge Engine for Genomics" (KnowEnG), an E-science framework for genomics where biomedical scientists will have access to powerful methods of data mining, network mining, and machine learning to extract knowledge out of genomics data. The scientist will come to KnowEnG with their own data sets in the form of spreadsheets and ask KnowEnG to analyze those data sets in the light of a massive knowledge base of community data sets called the "Knowledge Network" that will be at the heart of the system. The Center is undertaking discovery projects aimed at testing the utility of KnowEnG for transforming big data to knowledge. These projects span a broad range of biological enquiry, from pharmacogenomics (in collaboration with Mayo Clinic) to transcriptomics of human behavior.

PathwayMatrix: visualizing binary relationships between proteins in biological pathways

Background

Molecular activation pathways are inherently complex, and understanding relations across many biochemical reactions and reaction types is difficult. Visualizing and analyzing a pathway is a challenge due to the network size and the diversity of relations between proteins and molecules.

Results

In this paper, we introduce PathwayMatrix, a visualization tool that presents the binary relations between proteins in the pathway via the use of an interactive adjacency matrix. We provide filtering, lensing, clustering, and brushing and linking capabilities in order to present relevant details about proteins within a pathway.

Conclusions

We evaluated PathwayMatrix by conducting a series of in-depth interviews with domain experts who provided positive feedback, leading us to believe that our visualization technique could be helpful for the larger community of researchers utilizing pathway visualizations. PathwayMatrix is freely available at https://github.com/CreativeCodingLab/PathwayMatrix.

Visualizing genome and systems biology: technologies, tools, implementation techniques and trends, past, present and future

"Α picture is worth a thousand words." This widely used adage sums up in a few words the notion that a successful visual representation of a concept should enable easy and rapid absorption of large amounts of information. Although, in general, the notion of capturing complex ideas using images is very appealing, would 1000 words be enough to describe the unknown in a research field such as the life sciences? Life sciences is one of the biggest generators of enormous datasets, mainly as a result of recent and rapid technological advances; their complexity can make these datasets incomprehensible without effective visualization methods. Here we discuss the past, present and future of genomic and systems biology visualization. We briefly comment on many visualization and analysis tools and the purposes that they serve. We focus on the latest libraries and programming languages that enable more effective, efficient and faster approaches for visualizing biological concepts, and also comment on the future human-computer interaction trends that would enable for enhancing visualization further.

Algorithms for modeling global and context-specific functional relationship networks

Functional genomics has enormous potential to facilitate our understanding of normal and disease-specific physiology. In the past decade, intensive research efforts have been focused on modeling functional relationship networks, which summarize the probability of gene co-functionality relationships. Such modeling can be based on either expression data only or heterogeneous data integration. Numerous methods have been deployed to infer the functional relationship networks, while most of them target the global (non-context-specific) functional relationship networks. However, it is expected that functional relationships consistently reprogram under different tissues or biological processes. Thus, advanced methods have been developed targeting tissue-specific or developmental stage-specific networks. This article brings together the state-of-the-art functional relationship network modeling methods, emphasizes the need for heterogeneous genomic data integration and context-specific network modeling and outlines future directions for functional relationship networks.

Transcriptional networks implicated in human nonalcoholic fatty liver disease

The transcriptome of nonalcoholic fatty liver disease (NAFLD) was investigated in several studies. However, the implications of transcriptional networks in progressive NAFLD are not clear and mechanisms inducing transition from nonalcoholic simple fatty liver (NAFL) to nonalcoholic steatohepatitis (NASH) are still elusive. The aims of this study were to (1) construct networks for progressive NAFLD, (2) identify hub genes and functional modules in these networks and (3) infer potential linkages among hub genes, transcription factors and microRNAs (miRNA) for NAFLD progression. A systems biology approach by combining differential expression analysis and weighted gene co-expression network analysis (WGCNA) was utilized to dissect transcriptional profiles in 19 normal, 10 NAFL and 16 NASH patients. Based on this framework, 3 modules related to chromosome organization, proteasomal ubiquitin-dependent protein degradation and immune response were identified in NASH network. Furthermore, 9 modules of co-expressed genes associated with NAFL/NASH transition were found. Further characterization of these modules defined 13 highly connected hub genes in NAFLD progression network. Interestingly, 11 significantly changed miRNAs were predicted to target 10 of the 13 hub genes. Characterization of modules and hub genes that may be regulated by miRNAs could facilitate the identification of candidate genes and pathways responsible for NAFL/NASH transition and lead to a better understanding of NAFLD pathogenesis. The identified modules and hub genes may point to potential targets for therapeutic interventions.

Prediction of host - pathogen protein interactions between Mycobacterium tuberculosis and Homo sapiens using sequence motifs

Background

Emergence of multiple drug resistant strains of M. tuberculosis (MDR-TB) threatens to derail global efforts aimed at reigning in the pathogen. Co-infections of M. tuberculosis with HIV are difficult to treat. To counter these new challenges, it is essential to study the interactions between M. tuberculosis and the host to learn how these bacteria cause disease.

Results

We report a systematic flow to predict the host pathogen interactions (HPIs) between M. tuberculosis and Homo sapiens based on sequence motifs. First, protein sequences were used as initial input for identifying the HPIs by ‘interolog’ method. HPIs were further filtered by prediction of domain-domain interactions (DDIs). Functional annotations of protein and publicly available experimental results were applied to filter the remaining HPIs. Using such a strategy, 118 pairs of HPIs were identified, which involve 43 proteins from M. tuberculosis and 48 proteins from Homo sapiens. A biological interaction network between M. tuberculosis and Homo sapiens was then constructed using the predicted inter- and intra-species interactions based on the 118 pairs of HPIs. Finally, a web accessible database named PATH (Protein interactions of M. tuberculosis and Human) was constructed to store these predicted interactions and proteins.

Conclusions

This interaction network will facilitate the research on host-pathogen protein-protein interactions, and may throw light on how M. tuberculosis interacts with its host.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0535-y) contains supplementary material, which is available to authorized users.

Empowering biologists with multi-omics data: colorectal cancer as a paradigm

Motivation: Recent completion of the global proteomic characterization of The Cancer Genome Atlas (TCGA) colorectal cancer (CRC) cohort resulted in the first tumor dataset with complete molecular measurements at DNA, RNA and protein levels. Using CRC as a paradigm, we describe the application of the NetGestalt framework to provide easy access and interpretation of multi-omics data.

Results: The NetGestalt CRC portal includes genomic, epigenomic, transcriptomic, proteomic and clinical data for the TCGA CRC cohort, data from other CRC tumor cohorts and cell lines, and existing knowledge on pathways and networks, giving a total of more than 17 million data points. The portal provides features for data query, upload, visualization and integration. These features can be flexibly combined to serve various needs of the users, maximizing the synergy among omics data, human visualization and quantitative analysis. Using three case studies, we demonstrate that the portal not only provides user-friendly data query and visualization but also enables efficient data integration within a single omics data type, across multiple omics data types, and over biological networks.

Availability and implementation: The NetGestalt CRC portal can be freely accessed at http://www.netgestalt.org.

Contact:bing.zhang@vanderbilt.edu

Supplementary Information:Supplementary data are available at Bioinformatics online.

Co-expression analysis of fetal weight-related genes in ovine skeletal muscle during mid and late fetal development stages

BACKGROUND

Muscle development and lipid metabolism play important roles during fetal development stages. The commercial Texel sheep are more muscular than the indigenous Ujumqin sheep.

RESULTS

We performed serial transcriptomics assays and systems biology analyses to investigate the dynamics of gene expression changes associated with fetal longissimus muscles during different fetal stages in two sheep breeds. Totally, we identified 1472 differentially expressed genes during various fetal stages using time-series expression analysis. A systems biology approach, weighted gene co-expression network analysis (WGCNA), was used to detect modules of correlated genes among these 1472 genes. Dramatically different gene modules were identified in four merged datasets, corresponding to the mid fetal stage in Texel and Ujumqin sheep, the late fetal stage in Texel and Ujumqin sheep, respectively. We further detected gene modules significantly correlated with fetal weight, and constructed networks and pathways using genes with high significances. In these gene modules, we identified genes like TADA3, LMNB1, TGF-β3, EEF1A2, FGFR1, MYOZ1, and FBP2 correlated with fetal weight.

CONCLUSION

Our study revealed the complex network characteristics involved in muscle development and lipid metabolism during fetal development stages. Diverse patterns of the network connections observed between breeds and fetal stages could involve some hub genes, which play central roles in fetal development, correlating with fetal weight. Our findings could provide potential valuable biomarkers for selection of body weight-related traits in sheep and other livestock.

Molecular interaction networks for the analysis of human disease: utility, limitations, and considerations

High-throughput '-omics' data can be combined with large-scale molecular interaction networks, for example, protein-protein interaction networks, to provide a unique framework for the investigation of human molecular biology. Interest in these integrative '-omics' methods is growing rapidly because of their potential to understand complexity and association with disease; such approaches have a focus on associations between phenotype and "network-type." The potential of this research is enticing, yet there remain a series of important considerations. Here, we discuss interaction data selection, data quality, the relative merits of using data from large high-throughput studies versus a meta-database of smaller literature-curated studies, and possible issues of sociological or inspection bias in interaction data. Other work underway, especially international consortia to establish data formats, quality standards and address data redundancy, and the improvements these efforts are making to the field, is also evaluated. We present options for researchers intending to use large-scale molecular interaction networks as a functional context for protein or gene expression data, including microRNAs, especially in the context of human disease.

Functional characterization of breast cancer using pathway profiles

BACKGROUND

The molecular characteristics of human diseases are often represented by a list of genes termed "signature genes". A significant challenge facing this approach is that of reproducibility: signatures developed on a set of patients may fail to perform well on different sets of patients. As diseases are resulted from perturbed cellular functions, irrespective of the particular genes that contribute to the function, it may be more appropriate to characterize diseases based on these perturbed cellular functions.

METHODS

We proposed a profile-based approach to characterize a disease using a binary vector whose elements indicate whether a given function is perturbed based on the enrichment analysis of expression data between normal and tumor tissues. Using breast cancer and its four primary clinically relevant subtypes as examples, this approach is evaluated based on the reproducibility, accuracy and resolution of the resulting pathway profiles.

RESULTS

Pathway profiles for breast cancer and its subtypes are constructed based on data obtained from microarray and RNA-Seq data sets provided by The Cancer Genome Atlas (TCGA), and an additional microarray data set provided by The European Genome-phenome Archive (EGA). An average reproducibility of 68% is achieved between different data sets (TCGA microarray vs. EGA microarray data) and 67% average reproducibility is achieved between different technologies (TCGA microarray vs. TCGA RNA-Seq data). Among the enriched pathways, 74% of them are known to be associated with breast cancer or other cancers. About 40% of the identified pathways are enriched in all four subtypes, with 4, 2, 4, and 7 pathways enriched only in luminal A, luminal B, triple-negative, and HER2+ subtypes, respectively. Comparison of profiles between subtypes, as well as other diseases, shows that luminal A and luminal B subtypes are more similar to the HER2+ subtype than to the triple-negative subtype, and subtypes of breast cancer are more likely to be closer to each other than to other diseases.

CONCLUSIONS

Our results demonstrate that pathway profiles can successfully characterize both common and distinct functional characteristics of four subtypes of breast cancer and other related diseases, with acceptable reproducibility, high accuracy and reasonable resolution.

Exploring mechanisms of human disease through structurally resolved protein interactome networks

The study of the molecular basis of human disease has gained increasing attention over the past decade. With significant improvements in sequencing efficiency and throughput, a wealth of genotypic data has become available. However the translation of this information into concrete advances in diagnostic and clinical setups has proved far more challenging. Two major reasons for this are the lack of functional annotation for genomic variants and the complex nature of genotype-to-phenotype relationships. One fundamental approach to bypass these issues is to examine the effects of genetic variation at the level of proteins as they are directly involved in carrying out biological functions. Within the cell, proteins function by interacting with other proteins as a part of an underlying interactome network. This network can be determined using interactome mapping - a combination of high-throughput experimental toolkits and curation from small-scale studies. Integrating structural information from co-crystals with the network allows generation of a structurally resolved network. Within the context of this network, the structural principles of disease mutations can be examined and used to generate reliable mechanistic hypotheses regarding disease pathogenesis.

Proteogenomic convergence for understanding cancer pathways and networks

During the past several decades, the understanding of cancer at the molecular level has been primarily focused on mechanisms on how signaling molecules transform homeostatically balanced cells into malignant ones within an individual pathway. However, it is becoming more apparent that pathways are dynamic and crosstalk at different control points of the signaling cascades, making the traditional linear signaling models inadequate to interpret complex biological systems. Recent technological advances in high throughput, deep sequencing for the human genomes and proteomic technologies to comprehensively characterize the human proteomes in conjunction with multiplexed targeted proteomic assays to measure panels of proteins involved in biologically relevant pathways have made significant progress in understanding cancer at the molecular level. It is undeniable that proteomic profiling of differentially expressed proteins under many perturbation conditions, or between normal and "diseased" states is important to capture a first glance at the overall proteomic landscape, which has been a main focus of proteomics research during the past 15-20 years. However, the research community is gradually shifting its heavy focus from that initial discovery step to protein target verification using multiplexed quantitative proteomic assays, capable of measuring changes in proteins and their interacting partners, isoforms, and post-translational modifications (PTMs) in response to stimuli in the context of signaling pathways and protein networks. With a critical link to genotypes (i.e., high throughput genomics and transcriptomics data), new and complementary information can be gleaned from multi-dimensional omics data to (1) assess the effect of genomic and transcriptomic aberrations on such complex molecular machinery in the context of cell signaling architectures associated with pathological diseases such as cancer (i.e., from genotype to proteotype to phenotype); and (2) target pathway- and network-driven changes and map the fluctuations of these functional units (proteins) responsible for cellular activities in response to perturbation in a spatiotemporal fashion to better understand cancer biology as a whole system.

Interactive Visualization for Patient-to-Patient Comparison

A visual analysis approach and the developed supporting technology provide a comprehensive solution for analyzing large and complex integrated genomic and biomedical data. This paper presents a methodology that is implemented as an interactive visual analysis technology for extracting knowledge from complex genetic and clinical data and then visualizing it in a meaningful and interpretable way. By synergizing the domain knowledge into development and analysis processes, we have developed a comprehensive tool that supports a seamless patient-to-patient analysis, from an overview of the patient population in the similarity space to the detailed views of genes. The system consists of multiple components enabling the complete analysis process, including data mining, interactive visualization, analytical views, and gene comparison. We demonstrate our approach with medical scientists on a case study of childhood cancer patients on how they use the tool to confirm existing hypotheses and to discover new scientific insights.

Reverse engineering of Alzheimer's disease based on biomarker pathways analysis

Alzheimer's disease (AD) poses an increasingly profound problem to society, yet progress toward a genuine understanding of the disease remains worryingly slow. Perhaps, the most outstanding problem with the biology of AD is the question of its mechanistic origins, that is, it remains unclear wherein the molecular failures occur that underlie the disease. We demonstrate how molecular biomarkers could help define the nature of AD in terms of the early biochemical events that correlate with disease progression. We use a novel panel of biomolecules that appears in cerebrospinal fluid of AD patients. As changes in the relative abundance of these molecular markers are associated with progression to AD from mild cognitive impairment, we make the assumption that by tracking their origins we can identify the biochemical conditions that predispose their presence and consequently cause the onset of AD. We couple these protein markers with an analysis of a series of genetic factors and together this hypothesis essentially allows us to redefine AD in terms of the molecular pathways that underlie the disease.

BiNA: a visual analytics tool for biological network data

Interactive visual analysis of biological high-throughput data in the context of the underlying networks is an essential task in modern biomedicine with applications ranging from metabolic engineering to personalized medicine. The complexity and heterogeneity of data sets require flexible software architectures for data analysis. Concise and easily readable graphical representation of data and interactive navigation of large data sets are essential in this context. We present BiNA--the Biological Network Analyzer--a flexible open-source software for analyzing and visualizing biological networks. Highly configurable visualization styles for regulatory and metabolic network data offer sophisticated drawings and intuitive navigation and exploration techniques using hierarchical graph concepts. The generic projection and analysis framework provides powerful functionalities for visual analyses of high-throughput omics data in the context of networks, in particular for the differential analysis and the analysis of time series data. A direct interface to an underlying data warehouse provides fast access to a wide range of semantically integrated biological network databases. A plugin system allows simple customization and integration of new analysis algorithms or visual representations. BiNA is available under the 3-clause BSD license at http://bina.unipax.info/.

ANIA: ANnotation and Integrated Analysis of the 14-3-3 interactome

The dimeric 14-3-3 proteins dock onto pairs of phosphorylated Ser and Thr residues on hundreds of proteins, and thereby regulate many events in mammalian cells. To facilitate global analyses of these interactions, we developed a web resource named ANIA: ANnotation and Integrated Analysis of the 14-3-3 interactome, which integrates multiple data sets on 14-3-3-binding phosphoproteins. ANIA also pinpoints candidate 14-3-3-binding phosphosites using predictor algorithms, assisted by our recent discovery that the human 14-3-3-interactome is highly enriched in 2R-ohnologues. 2R-ohnologues are proteins in families of two to four, generated by two rounds of whole genome duplication at the origin of the vertebrate animals. ANIA identifies candidate ‘lynchpins’, which are 14-3-3-binding phosphosites that are conserved across members of a given 2R-ohnologue protein family. Other features of ANIA include a link to the catalogue of somatic mutations in cancer database to find cancer polymorphisms that map to 14-3-3-binding phosphosites, which would be expected to interfere with 14-3-3 interactions. We used ANIA to map known and candidate 14-3-3-binding enzymes within the 2R-ohnologue complement of the human kinome. Our projections indicate that 14-3-3s dock onto many more human kinases than has been realized. Guided by ANIA, PAK4, 6 and 7 (p21-activated kinases 4, 6 and 7) were experimentally validated as a 2R-ohnologue family of 14-3-3-binding phosphoproteins. PAK4 binding to 14-3-3 is stimulated by phorbol ester, and involves the ‘lynchpin’ site phosphoSer99 and a major contribution from Ser181. In contrast, PAK6 and PAK7 display strong phorbol ester-independent binding to 14-3-3, with Ser113 critical for the interaction with PAK6. These data point to differential 14-3-3 regulation of PAKs in control of cell morphology.

Database URL: https://ania-1433.lifesci.dundee.ac.uk/prediction/webserver/index.py

Computational Prediction of Protein-Protein Interaction Networks: Algo-rithms and Resources

Protein interactions play an important role in the discovery of protein functions and pathways in biological processes. This is especially true in case of the diseases caused by the loss of specific protein-protein interactions in the organism. The accuracy of experimental results in finding protein-protein interactions, however, is rather dubious and high throughput experimental results have shown both high false positive beside false negative information for protein interaction. Computational methods have attracted tremendous attention among biologists because of the ability to predict protein-protein interactions and validate the obtained experimental results. In this study, we have reviewed several computational methods for protein-protein interaction prediction as well as describing major databases, which store both predicted and detected protein-protein interactions, and the tools used for analyzing protein interaction networks and improving protein-protein interaction reliability.

Functional molecular units for guiding biomarker panel design

The field of biomarker research has experienced a major boost in recent years, and the number of publications on biomarker studies evaluating given, but also proposing novel biomarker candidates is increasing rapidly for numerous clinically relevant disease areas. However, individual markers often lack sensitivity and specificity in the clinical context, resting essentially on the intra-individual phenotype variability hampering sensitivity, or on assessing more general processes downstream of the causative molecular events characterizing a disease term, in consequence impairing disease specificity. The trend to circumvent these shortcomings goes towards utilizing multimarker panels, thus combining the strength of individual markers to further enhance performance regarding both sensitivity and specificity. A way of identifying the optimal composition of individual markers in a panel approach is to pick each marker as representative for a specific pathophysiological (mechanistic) process relevant for the disease under investigation, hence resulting in a multimarker panel for covering the set of pathophysiological processes underlying the frequently multifactorial composition of a clinical phenotype.Here we outline a procedure of identifying such sets of disease-specific pathophysiological processes (units) delineated on the basis of disease-associated molecular feature lists derived from literature mining as well as aggregated, publicly available Omics profiling experiments. With such molecular units in hand, providing an improved reflection of a specific clinical phenotype, biomarker candidates can then be assigned to or novel candidates are to be selected from these units, subsequently resulting in a multimarker panel promising improved accuracy in disease diagnosis as well as prognosis.

Using VisANT to Analyze Networks

VisANT is a Web-based workbench for the integrative analysis of biological networks with unique features such as exploratory navigation of interaction network and multi-scale visualization and inference with integrated hierarchical knowledge. It provides functionalities for convenient construction, visualization, and analysis of molecular and higher order networks based on functional (e.g., expression profiles, phylogenetic profiles) and physical (e.g., yeast two-hybrid, chromatin-immunoprecipitation and drug target) relations from either the Predictome database or user-defined data sets. Analysis capabilities include network structure analysis, overrepresentation analysis, expression enrichment analysis etc. Additionally, network can be saved, accessed, and shared online. VisANT is able to develop and display meta-networks for meta-nodes that are structural complexes or pathways or any kind of subnetworks. Further, VisANT supports a growing number of standard exchange formats and database referencing standards, e.g., PSI-MI, KGML, BioPAX, SBML(in progress) Multiple species are supported to the extent that interactions or associations are available (i.e., public datasets or Predictome database).