SimCT: a generic tool to visualize ontology-based relationships for biological objects

Visualizing GO Annotations

Contemporary techniques in biology produce readouts for large numbers of genes simultaneously, the typical example being differential gene expression measurements. Moreover, those genes are often richly annotated using GO terms that describe gene function and that can be used to summarize the results of the genome-scale experiments. However, making sense of such GO enrichment analyses may be challenging. For instance, overrepresented GO functions in a set of differentially expressed genes are typically output as a flat list, a format not adequate to capture the complexities of the hierarchical structure of the GO annotation labels.In this chapter, we survey various methods to visualize large, difficult-to-interpret lists of GO terms. We catalog their availability-Web-based or standalone, the main principles they employ in summarizing large lists of GO terms, and the visualization styles they support. These brief commentaries on each software are intended as a helpful inventory, rather than comprehensive descriptions of the underlying algorithms. Instead, we show examples of their use and suggest that the choice of an appropriate visualization tool may be crucial to the utility of GO in biological discovery.

Protein aggregation, structural disorder and RNA-binding ability: a new approach for physico-chemical and gene ontology classification of multiple datasets

Background

Comparison between multiple protein datasets requires the choice of an appropriate reference system and a number of variables to describe their differences. Here we introduce an innovative approach to discriminate multiple protein datasets (multiCM) and to measure enrichments in gene ontology terms (cleverGO) using semantic similarities.

Results

We illustrate the powerfulness of our approach by investigating the links between RNA-binding ability and other protein features, such as structural disorder and aggregation, in S. cerevisiae, C. elegans, M. musculus and H. sapiens. Our results are in striking agreement with available experimental evidence and unravel features that are key to understand the mechanisms regulating cellular homeostasis.

Conclusions

In an intuitive way, multiCM and cleverGO provide accurate classifications of physico-chemical features and annotations of biological processes, molecular functions and cellular components, which is extremely useful for the discovery and characterization of new trends in protein datasets. The multiCM and cleverGO can be freely accessed on the Web at http://www.tartaglialab.com/cs_multi/submission and http://www.tartaglialab.com/GO_analyser/universal. Each of the pages contains links to the corresponding documentation and tutorial.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2280-z) contains supplementary material, which is available to authorized users.

Relationships between predicted moonlighting proteins, human diseases, and comorbidities from a network perspective

Moonlighting proteins are a subset of multifunctional proteins characterized by their multiple, independent, and unrelated biological functions. We recently set up a large-scale identification of moonlighting proteins using a protein-protein interaction (PPI) network approach. We established that 3% of the current human interactome is composed of predicted moonlighting proteins. We found that disease-related genes are over-represented among those candidates. Here, by comparing moonlighting candidates to non-candidates as groups, we further show that (i) they are significantly involved in more than one disease, (ii) they contribute to complex rather than monogenic diseases, (iii) the diseases in which they are involved are phenotypically different according to their annotations, finally, (iv) they are enriched for diseases pairs showing statistically significant comorbidity patterns based on Medicare records. Altogether, our results suggest that some observed comorbidities between phenotypically different diseases could be due to a shared protein involved in unrelated biological processes.

The functional landscape of Hsp27 reveals new cellular processes such as DNA repair and alternative splicing and proposes novel anticancer targets

Previously, we identified the stress-induced chaperone, Hsp27, as highly overexpressed in castration-resistant prostate cancer and developed an Hsp27 inhibitor (OGX-427) currently tested in phase I/II clinical trials as a chemosensitizing agent in different cancers. To better understand the Hsp27 poorly-defined cytoprotective functions in cancers and increase the OGX-427 pharmacological safety, we established the Hsp27-protein interaction network using a yeast two-hybrid approach and identified 226 interaction partners. As an example, we showed that targeting Hsp27 interaction with TCTP, a partner protein identified in our screen increases therapy sensitivity, opening a new promising field of research for therapeutic approaches that could decrease or abolish toxicity for normal cells. Results of an in-depth bioinformatics network analysis allying the Hsp27 interaction map into the human interactome underlined the multifunctional character of this protein. We identified interactions of Hsp27 with proteins involved in eight well known functions previously related to Hsp27 and uncovered 17 potential new ones, such as DNA repair and RNA splicing. Validation of Hsp27 involvement in both processes in human prostate cancer cells supports our system biology-predicted functions and provides new insights into Hsp27 roles in cancer cells.

Extended interaction network of procollagen C-proteinase enhancer-1 in the extracellular matrix

PCPE-1 (procollagen C-proteinase enhancer-1) is an extracellular matrix glycoprotein that can stimulate procollagen processing by procollagen C-proteinases such as BMP-1 (bone morphogenetic protein 1). PCPE-1 interacts with several proteins in addition to procollagens and BMP-1, suggesting that it could be involved in biological processes other than collagen maturation. We thus searched for additional partners of PCPE-1 in the extracellular matrix, which could provide new insights into its biological roles. We identified 17 new partners of PCPE-1 by SPR (surface plasmon resonance) imaging. PCPE-1 forms a transient complex with the β-amyloid peptide, whereas it forms high or very high affinity complexes with laminin-111 (KD=58.8 pM), collagen VI (KD=9.5 nM), TSP-1 (thrombospondin-1) (KD1=19.9 pM, KD2=14.5 nM), collagen IV (KD=49.4 nM) and endostatin, a fragment of collagen XVIII (KD1=0.30 nM, KD2=1.1 nM). Endostatin binds to the NTR (netrin-like) domain of PCPE-1 and decreases the degree of superstimulation of PCPE-1 enhancing activity by heparin. The analysis of the PCPE-1 interaction network based on Gene Ontology terms suggests that, besides its role in collagen deposition, PCPE-1 might be involved in tumour growth, neurodegenerative diseases and angiogenesis. In vitro assays have indeed shown that the CUB1CUB2 (where CUB is complement protein subcomponents C1r/C1s, urchin embryonic growth factor and BMP-1) fragment of PCPE-1 inhibits angiogenesis.

Multifunctional proteins revealed by overlapping clustering in protein interaction network

Motivation: Multifunctional proteins perform several functions. They are expected to interact specifically with distinct sets of partners, simultaneously or not, depending on the function performed. Current graph clustering methods usually allow a protein to belong to only one cluster, therefore impeding a realistic assignment of multifunctional proteins to clusters.

Results: Here, we present Overlapping Cluster Generator (OCG), a novel clustering method which decomposes a network into overlapping clusters and which is, therefore, capable of correct assignment of multifunctional proteins. The principle of OCG is to cover the graph with initial overlapping classes that are iteratively fused into a hierarchy according to an extension of Newman's modularity function. By applying OCG to a human protein–protein interaction network, we show that multifunctional proteins are revealed at the intersection of clusters and demonstrate that the method outperforms other existing methods on simulated graphs and PPI networks.

Availability: This software can be downloaded from http://tagc.univ-mrs.fr/welcome/spip.php?rubrique197

Contact:brun@tagc.univ-mrs.fr

Supplementary information:Supplementary data are available at Bioinformatics online.

Intrinsic disorder of the extracellular matrix

The extracellular matrix is very well organized at the supramolecular and tissue levels and little is known on the potential role of intrinsic disorder in promoting its organization. We predicted the amount of disorder and identified disordered regions in the human extracellular proteome with established computational tools. The extracellular proteome is significantly enriched in proteins comprising more than 50% of disorder compared to the complete human proteome. The enrichment is mostly due to long disordered regions containing at least 100 consecutive disordered residues. The amount of intrinsic disorder is heterogeneous in the extracellular protein families, with the most disordered being collagens and the small integrin-binding ligand N-linked glycoproteins. Although most domains found in extracellular proteins are structured, the fibronectin III domains contain a variable amount of disordered residues (up to 92%). Binding sites for heparin and integrins are found in disordered sequences of extracellular proteins. Intrinsic disorder is evenly distributed in hubs and ends in the interaction network of extracellular proteins with their extracellular partners. In contrast, extracellular hubs are significantly enriched in disorder in the network of extracellular proteins with their extracellular, membrane and intracellular partners. Disorder could thus provide the structural plasticity required for the hubs to interact with membrane and intracellular proteins. Organization and assembly of the extracellular matrix, development of mineralized tissues and cell-matrix adhesion are the biological processes overrepresented in the most disordered extracellular proteins. Extracellular disorder is associated with binding to growth factors, glycosaminoglycans and integrins at the molecular level.

Functional integrative levels in the human interactome recapitulate organ organization

Interactome networks represent sets of possible physical interactions between proteins. They lack spatio-temporal information by construction. However, the specialized functions of the differentiated cell types which are assembled into tissues or organs depend on the combinatorial arrangements of proteins and their physical interactions. Is tissue-specificity, therefore, encoded within the interactome? In order to address this question, we combined protein-protein interactions, expression data, functional annotations and interactome topology. We first identified a subnetwork formed exclusively of proteins whose interactions were observed in all tested tissues. These are mainly involved in housekeeping functions and are located at the topological center of the interactome. This ‘Largest Common Interactome Network’ represents a ‘functional interactome core’. Interestingly, two types of tissue-specific interactions are distinguished when considering function and network topology: tissue-specific interactions involved in regulatory and developmental functions are central whereas tissue-specific interactions involved in organ physiological functions are peripheral. Overall, the functional organization of the human interactome reflects several integrative levels of functions with housekeeping and regulatory tissue-specific functions at the center and physiological tissue-specific functions at the periphery. This gradient of functions recapitulates the organization of organs, from cells to organs. Given that several gradients have already been identified across interactomes, we propose that gradients may represent a general principle of protein-protein interaction network organization.

REVIGO summarizes and visualizes long lists of gene ontology terms

Outcomes of high-throughput biological experiments are typically interpreted by statistical testing for enriched gene functional categories defined by the Gene Ontology (GO). The resulting lists of GO terms may be large and highly redundant, and thus difficult to interpret.REVIGO is a Web server that summarizes long, unintelligible lists of GO terms by finding a representative subset of the terms using a simple clustering algorithm that relies on semantic similarity measures. Furthermore, REVIGO visualizes this non-redundant GO term set in multiple ways to assist in interpretation: multidimensional scaling and graph-based visualizations accurately render the subdivisions and the semantic relationships in the data, while treemaps and tag clouds are also offered as alternative views. REVIGO is freely available at http://revigo.irb.hr/.

RedundancyMiner: De-replication of redundant GO categories in microarray and proteomics analysis

BACKGROUND

The Gene Ontology (GO) Consortium organizes genes into hierarchical categories based on biological process, molecular function and subcellular localization. Tools such as GoMiner can leverage GO to perform ontological analysis of microarray and proteomics studies, typically generating a list of significant functional categories. Two or more of the categories are often redundant, in the sense that identical or nearly-identical sets of genes map to the categories. The redundancy might typically inflate the report of significant categories by a factor of three-fold, create an illusion of an overly long list of significant categories, and obscure the relevant biological interpretation.

RESULTS

We now introduce a new resource, RedundancyMiner, that de-replicates the redundant and nearly-redundant GO categories that had been determined by first running GoMiner. The main algorithm of RedundancyMiner, MultiClust, performs a novel form of cluster analysis in which a GO category might belong to several category clusters. Each category cluster follows a "complete linkage" paradigm. The metric is a similarity measure that captures the overlap in gene mapping between pairs of categories.

CONCLUSIONS

RedundancyMiner effectively eliminated redundancies from a set of GO categories. For illustration, we have applied it to the clarification of the results arising from two current studies: (1) assessment of the gene expression profiles obtained by laser capture microdissection (LCM) of serial cryosections of the retina at the site of final optic fissure closure in the mouse embryos at specific embryonic stages, and (2) analysis of a conceptual data set obtained by examining a list of genes deemed to be "kinetochore" genes.

REVIGO summarizes and visualizes long lists of gene ontology terms

Outcomes of high-throughput biological experiments are typically interpreted by statistical testing for enriched gene functional categories defined by the Gene Ontology (GO). The resulting lists of GO terms may be large and highly redundant, and thus difficult to interpret.REVIGO is a Web server that summarizes long, unintelligible lists of GO terms by finding a representative subset of the terms using a simple clustering algorithm that relies on semantic similarity measures. Furthermore, REVIGO visualizes this non-redundant GO term set in multiple ways to assist in interpretation: multidimensional scaling and graph-based visualizations accurately render the subdivisions and the semantic relationships in the data, while treemaps and tag clouds are also offered as alternative views. REVIGO is freely available at http://revigo.irb.hr/.