CytoGEDEVO-global alignment of biological networks with Cytoscape

A protein-protein interaction network aligner study in the multi-objective domain

BACKGROUND AND OBJECTIVE

The protein-protein interaction (PPI) network alignment has proven to be an efficient technique in the diagnosis and prevention of certain diseases. However, the difficulty in maximizing, at the same time, the two qualities that measure the goodness of alignments (topological and biological quality) has led aligners to produce very different alignments. Thus making a comparative study among alignments of such different qualities a big challenge. Multi-objective optimization is a computer method, which is very powerful in this kind of contexts because both conflicting qualities are considered together. Analysing the alignments of each PPI network aligner with multi-objective methodologies allows you to visualize a bigger picture of the alignments and their qualities, obtaining very interesting conclusions. This paper proposes a comprehensive PPI network aligner study in the multi-objective domain.

METHODS

Alignments from each aligner and all aligners together were studied and compared to each other via Pareto dominance methodologies. The best alignments produced by each aligner and all aligners together for five different alignment scenarios were displayed in Pareto front graphs. Later, the aligners were ranked according to the topological, biological, and combined quality of their alignments. Finally, the aligners were also ranked based on their average runtimes.

RESULTS

Regarding aligners constructing the best overall alignments, we found that SAlign, BEAMS, SANA, and HubAlign are the best options. Additionally, the alignments of best topological quality are produced by: SANA, SAlign, and HubAlign aligners. On the contrary, the aligners returning the alignments of best biological quality are: BEAMS, TAME, and WAVE. However, if there are time constraints, it is recommended to select SAlign to obtain high topological quality alignments and PISwap or SAlign aligners for high biological quality alignments.

CONCLUSIONS

The use of the SANA aligner is recommended for obtaining the best alignments of topological quality, BEAMS for alignments of the best biological quality, and SAlign for alignments of the best combined topological and biological quality. Simultaneously, SANA and BEAMS have above-average runtimes. Therefore, it is suggested, if necessary due to time restrictions, to choose other, faster aligners like SAlign or PISwap whose alignments are also of high quality.

Clostridioides difficile infection is associated with differences in transcriptionally active microbial communities

Clostridioides difficile infection (CDI) is responsible for around 300,000 hospitalizations yearly in the United States, with the associated monetary cost being billions of dollars. Gut microbiome dysbiosis is known to be important to CDI. To the best of our knowledge, metatranscriptomics (MT) has only been used to characterize gut microbiome composition and function in one prior study involving CDI patients. Therefore, we utilized MT to investigate differences in active community diversity and composition between CDI+ (n = 20) and CDI- (n = 19) samples with respect to microbial taxa and expressed genes. No significant (Kruskal-Wallis, p > 0.05) differences were detected for richness or evenness based on CDI status. However, clustering based on CDI status was significant for both active microbial taxa and expressed genes datasets (PERMANOVA, p ≤ 0.05). Furthermore, differential feature analysis revealed greater expression of the opportunistic pathogens Enterocloster bolteae and Ruminococcus gnavus in CDI+ compared to CDI- samples. When only fungal sequences were considered, the family Saccharomycetaceae expressed more genes in CDI-, while 31 other fungal taxa were identified as significantly (Kruskal-Wallis p ≤ 0.05, log(LDA) ≥ 2) associated with CDI+. We also detected a variety of genes and pathways that differed significantly (Kruskal-Wallis p ≤ 0.05, log(LDA) ≥ 2) based on CDI status. Notably, differential genes associated with biofilm formation were expressed by C. difficile. This provides evidence of another possible contributor to C. difficile's resistance to antibiotics and frequent recurrence in vivo. Furthermore, the greater number of CDI+ associated fungal taxa constitute additional evidence that the mycobiome is important to CDI pathogenesis. Future work will focus on establishing if C. difficile is actively producing biofilms during infection and if any specific fungal taxa are particularly influential in CDI.

A close-up view of the Hunter syndrome

The Lysosomal Storage disease known as Mucopolysaccharidosis type II, is caused by mutations affecting the iduronate-2-sulfatase required for heparan and dermatan sulfate catabolism. The central nervous system (CNS) is mostly and severely affected by the accumulation of both substrates. The complexity of the CNS damage observed in MPS II patients has been limitedly explored. The use of mass spectrometry (MS)-based proteomics tools to identify protein profiles may yield valuable information about the pathological mechanisms of Hunter syndrome. In this further study, we provide a new comparative proteomic analysis of MPS II models by using a pipeline consisting of the identification of native protein complexes positioned selectively by using a specific antibody, coupled with mass spectrometry analysis, allowing us to identify changes involving in a significant number of new biological functions, including a specific brain antioxidant response, a down-regulated autophagic, the suppression of sulfur catabolic process, a prominent liver immune response and the stimulation of phagocytosis among others.

SANA: cross-species prediction of Gene Ontology GO annotations via topological network alignment

Topological network alignment aims to align two networks node-wise in order to maximize the observed common connection (edge) topology between them. The topological alignment of two protein-protein interaction (PPI) networks should thus expose protein pairs with similar interaction partners allowing, for example, the prediction of common Gene Ontology (GO) terms. Unfortunately, no network alignment algorithm based on topology alone has been able to achieve this aim, though those that include sequence similarity have seen some success. We argue that this failure of topology alone is due to the sparsity and incompleteness of the PPI network data of almost all species, which provides the network topology with a small signal-to-noise ratio that is effectively swamped when sequence information is added to the mix. Here we show that the weak signal can be detected using multiple stochastic samples of "good" topological network alignments, which allows us to observe regions of the two networks that are robustly aligned across multiple samples. The resulting network alignment frequency (NAF) strongly correlates with GO-based Resnik semantic similarity and enables the first successful cross-species predictions of GO terms based on topology-only network alignments. Our best predictions have an AUPR of about 0.4, which is competitive with state-of-the-art algorithms, even when there is no observable sequence similarity and no known homology relationship. While our results provide only a "proof of concept" on existing network data, we hypothesize that predicting GO terms from topology-only network alignments will become increasingly practical as the volume and quality of PPI network data increase.

On the current failure-but bright future-of topology-driven biological network alignment

Since the function of a protein is defined by its interaction partners, and since we expect similar interaction patterns across species, the alignment of protein-protein interaction (PPI) networks between species, based on network topology alone, should uncover functionally related proteins across species. Surprisingly, despite the publication of more than fifty algorithms aimed at performing PPI network alignment, few have demonstrated a statistically significant link between network topology and functional similarity, and none have demonstrated that orthologs can be recovered using network topology alone. We find that the major contributing factors to this surprising failure are: (i) edge densities in most currently available experimental PPI networks are demonstrably too low to expect topological network alignment to succeed; (ii) in the few cases where the edge densities are high enough, some measures of topological similarity easily uncover functionally similar proteins while others do not; and (iii) most network alignment algorithms to date perform poorly at optimizing even their own topological objective functions, hampering their ability to use topology effectively. We demonstrate that SANA-the Simulated Annealing Network Aligner-significantly outperforms existing aligners at optimizing their own objective functions, even achieving near-optimal solutions when the optimal solution is known. We offer the first demonstration of global network alignments based on topology alone that align functionally similar proteins with p-values in some cases below 10^-300. We predict that topological network alignment has a bright future as edge densities increase toward the value where good alignments become possible. We demonstrate that when enough common topology is present at high enough edge densities-for example in the recent, partly synthetic networks of the Integrated Interaction Database-topological network alignment easily recovers most orthologs, paving the way toward high-throughput functional prediction based on topology-driven network alignment.

The protein-protein interaction network alignment using recurrent neural network

The main challenge of biological network alignment is that the problem of finding the alignments in two graphs is NP-hard. The discovery of protein-protein interaction (PPI) networks is of great importance in bioinformatics due to their utilization in identifying the cellular pathways, finding new medicines, and disease recognition. In this regard, we describe the network alignment method in the form of a classification problem for the very first time and introduce a deep network that finds the alignment of nodes present in the two networks. We call this method RENA, which means Network Alignment using REcurrent neural network. The proposed solution consists of three steps; in the first phase, we obtain the sequence and topological similarities from the networks' structure. For the second phase, the dataset needed for the transformation of the problem into a classification problem is created from obtained features. In the third phase, we predict the nodes' alignment between two networks using deep learning. We used Biogrid dataset for RENA evaluation. The RENA method is compared with three classification approaches of support vector machine, K-nearest neighbors, and linear discriminant analysis. The experimental results demonstrate the efficiency of the RENA method and 100% accuracy in PPI network alignment prediction.

Exploring the conservation of Alzheimer-related pathways between H. sapiens and C. elegans: a network alignment approach

Alzheimer disease (AD) is a neurodegenerative disorder with an –as of yet– unclear etiology and pathogenesis. Research to unveil disease processes underlying AD often relies on the use of neurodegenerative disease model organisms, such as Caenorhabditis elegans. This study sought to identify biological pathways implicated in AD that are conserved in Homo sapiens and C. elegans. Protein–protein interaction networks were assembled for amyloid precursor protein (APP) and Tau in H. sapiens—two proteins whose aggregation is a hallmark in AD—and their orthologs APL-1 and PTL-1 for C. elegans. Global network alignment was used to compare these networks and determine similar, likely conserved, network regions. This comparison revealed that two prominent pathways, the APP-processing and the Tau-phosphorylation pathways, are highly conserved in both organisms. While the majority of interactions between proteins in those pathways are known to be associated with AD in human, they remain unexamined in C. elegans, signifying the need for their further investigation. In this work, we have highlighted conserved interactions related to AD in humans and have identified specific proteins that can act as targets for experimental studies in C. elegans, aiming to uncover the underlying mechanisms of AD.

Molecular networks in Network Medicine: Development and applications

Network Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein-protein interaction networks, correlation-based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in identifying key genes within genetic association regions, and limited applications to human diseases. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Translational, Genomic, and Systems Medicine > Translational Medicine Analytical and Computational Methods > Analytical Methods Analytical and Computational Methods > Computational Methods.

Disturbance in human gut microbiota networks by parasites and its implications in the incidence of depression

If you think you are in control of your behavior, think again. Evidence suggests that behavioral modifications, as development and persistence of depression, maybe the consequence of a complex network of communication between macro and micro-organisms capable of modifying the physiological axis of the host. Some parasites cause significant nutritional deficiencies for the host and impair the effectiveness of cognitive processes such as memory, teaching or non-verbal intelligence. Bacterial communities mediate the establishment of parasites and vice versa but this complexity approach remains little explored. We study the gut microbiota-parasite interactions using novel techniques of network analysis using data of individuals from two indigenous communities in Guerrero, Mexico. Our results suggest that Ascaris lumbricoides induce a gut microbiota perturbation affecting its network properties and also subnetworks of key species related to depression, translating in a loss of emergence. Studying these network properties changes is particularly important because recent research has shown that human health is characterized by a dynamic trade-off between emergence and self-organization, called criticality. Emergence allows the systems to generate novel information meanwhile self-organization is related to the system's order and structure. In this way, the loss of emergence means a depart from criticality and ultimately loss of health.

An Introductory Guide to Aligning Networks Using SANA, the Simulated Annealing Network Aligner

Sequence alignment has had an enormous impact on our understanding of biology, evolution, and disease. The alignment of biological networks holds similar promise. Biological networks generally model interactions between biomolecules such as proteins, genes, metabolites, or mRNAs. There is strong evidence that the network topology-the "structure" of the network-is correlated with the functions performed, so that network topology can be used to help predict or understand function. However, unlike sequence comparison and alignment-which is an essentially solved problem-network comparison and alignment is an NP-complete problem for which heuristic algorithms must be used.Here we introduce SANA, the Simulated Annealing Network Aligner. SANA is one of many algorithms proposed for the arena of biological network alignment. In the context of global network alignment, SANA stands out for its speed, memory efficiency, ease-of-use, and flexibility in the arena of producing alignments between two or more networks. SANA produces better alignments in minutes on a laptop than most other algorithms can produce in hours or days of CPU time on large server-class machines. We walk the user through how to use SANA for several types of biomolecular networks.

Leveraging User-Friendly Network Approaches to Extract Knowledge From High-Throughput Omics Datasets

Recent technological advances for the acquisition of multi-omics data have allowed an unprecedented understanding of the complex intricacies of biological systems. In parallel, a myriad of computational analysis techniques and bioinformatics tools have been developed, with many efforts directed towards the creation and interpretation of networks from this data. In this review, we begin by examining key network concepts and terminology. Then, computational tools that allow for their construction and analysis from high-throughput omics datasets are presented. We focus on the study of functional relationships such as co-expression, protein-protein interactions, and regulatory interactions that are particularly amenable to modeling using the framework of networks. We envisage that many potential users of these analytical strategies may not be completely literate in programming languages and code adaptation, and for this reason, emphasis is given to tools' user-friendliness, including plugins for the widely adopted Cytoscape software, an open-source, cross-platform tool for network analysis, visualization, and data integration.

SANA NetGO: a combinatorial approach to using Gene Ontology (GO) terms to score network alignments

Motivation

Gene Ontology (GO) terms are frequently used to score alignments between protein-protein interaction (PPI) networks. Methods exist to measure GO similarity between proteins in isolation, but proteins in a network alignment are not isolated: each pairing is dependent on every other via the alignment itself. Existing measures fail to take into account the frequency of GO terms across networks, instead imposing arbitrary rules on when to allow GO terms.

Results

Here we develop NetGO, a new measure that naturally weighs infrequent, informative GO terms more heavily than frequent, less informative GO terms, without arbitrary cutoffs, instead downweighting GO terms according to their frequency in the networks being aligned. This is a global measure applicable only to alignments, independent of pairwise GO measures, in the same sense that the edge-based EC or S3 scores are global measures of topological similarity independent of pairwise topological similarities. We demonstrate the superiority of NetGO in alignments of predetermined quality and show that NetGO correlates with alignment quality better than any existing GO-based alignment measures. We also demonstrate that NetGO provides a measure of taxonomic similarity between species, consistent with existing taxonomic measuresa feature not shared with existing GObased network alignment measures. Finally, we re-score alignments produced by almost a dozen aligners from a previous study and show that NetGO does a better job at separating good alignments from bad ones.

Availability and implementation

Available as part of SANA.

Contact

whayes@uci.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Bacterial Biomarkers of Marcellus Shale Activity in Pennsylvania

Unconventional oil and gas (UOG) extraction, also known as hydraulic fracturing, is becoming more prevalent with the increasing use and demand for natural gas; however, the full extent of its environmental impacts is still unknown. Here we measured physicochemical properties and bacterial community composition of sediment samples taken from twenty-eight streams within the Marcellus shale formation in northeastern Pennsylvania differentially impacted by hydraulic fracturing activities. Fourteen of the streams were classified as UOG+, and thirteen were classified as UOG- based on the presence of UOG extraction in their respective watersheds. One stream was located in a watershed that previously had UOG extraction activities but was recently abandoned. We utilized high-throughput sequencing of the 16S rRNA gene to infer differences in sediment aquatic bacterial community structure between UOG+ and UOG- streams, as well as correlate bacterial community structure to physicochemical water parameters. Although overall alpha and beta diversity differences were not observed, there were a plethora of significantly enriched operational taxonomic units (OTUs) within UOG+ and UOG- samples. Our biomarker analysis revealed many of the bacterial taxa enriched in UOG+ streams can live in saline conditions, such as Rubrobacteraceae. In addition, several bacterial taxa capable of hydrocarbon degradation were also enriched in UOG+ samples, including Oceanospirillaceae. Methanotrophic taxa, such as Methylococcales, were significantly enriched as well. Several taxa that were identified as enriched in these samples were enriched in samples taken from different streams in 2014; moreover, partial least squares discriminant analysis (PLS-DA) revealed clustering between streams from the different studies based on the presence of hydraulic fracturing along the second axis. This study revealed significant differences between bacterial assemblages within stream sediments of UOG+ and UOG- streams and identified several potential biomarkers for evaluating and monitoring the response of autochthonous bacterial communities to potential hydraulic fracturing impacts.

CytoMCS: A Multiple Maximum Common Subgraph Detection Tool for Cytoscape

Comparative analysis of biological networks is a major problem in computational integrative systems biology. By computing the maximum common edge subgraph between a set of networks, one is able to detect conserved substructures between them and quantify their topological similarity. To aid such analyses we have developed CytoMCS, a Cytoscape app for computing inexact solutions to the maximum common edge subgraph problem for two or more graphs. Our algorithm uses an iterative local search heuristic for computing conserved subgraphs, optimizing a squared edge conservation score that is able to detect not only fully conserved edges but also partially conserved edges. It can be applied to any set of directed or undirected, simple graphs loaded as networks into Cytoscape, e.g. protein-protein interaction networks or gene regulatory networks. CytoMCS is available as a Cytoscape app at http://apps.cytoscape.org/apps/cytomcs.

NFPscanner: a webtool for knowledge-based deciphering of biomedical networks

Background

Many biological pathways have been created to represent different types of knowledge, such as genetic interactions, metabolic reactions, and gene-regulating and physical-binding relationships. Biologists are using a wide range of omics data to elaborately construct various context-specific differential molecular networks. However, they cannot easily gain insight into unfamiliar gene networks with the tools that are currently available for pathways resource and network analysis. They would benefit from the development of a standardized tool to compare functions of multiple biological networks quantitatively and promptly.

Results

To address this challenge, we developed NFPscanner, a web server for deciphering gene networks with pathway associations. Adapted from a recently reported knowledge-based framework called network fingerprint, NFPscanner integrates the annotated pathways of 7 databases, 4 algorithms, and 2 graphical visualization modules into a webtool. It implements 3 types of network analysis:

Fingerprint: Deciphering gene networks and highlighting inherent pathway modules

Alignment: Discovering functional associations by finding optimized node mapping between 2 gene networks

Enrichment: Calculating and visualizing gene ontology (GO) and pathway enrichment for genes in networks

Users can upload gene networks to NFPscanner through the web interface and then interactively explore the networks’ functions.

Conclusions

NFPscanner is open-source software for non-commercial use, freely accessible at http://biotech.bmi.ac.cn/nfs.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1673-1) contains supplementary material, which is available to authorized users.