Biana: a software framework for compiling biological interactions and analyzing networks

Garcı́a-Garcı́a, Javier; Güney, Emre; Aragüés, Ramón; Planas-Iglesias, Joan; Oliva, Baldo

doi:10.1186/1471-2105-11-56

Cited by 79 publications

(51 citation statements)

References 68 publications

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“…An initial two-dimensional differential in-gel electrophoresis assessed the distinct expression of proteins in MDA-MB-231 (231) and its bone metastatic variant, BO2 cells (7,15). To describe the protein-protein interaction network (PPIN) in breast cancer cells that metastasize in bone, we used bioinformatics tools such as the Biological Interactions and Network Analysis (BIANA) 1 (18) and GUILD (19) in combination with data from proteomic and transcriptomic analysis. BIANA creates and analyzes biological networks and GUILD prioritizes the biomolecules in the network according to their relevance to a given phenotype.…”

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confidence: 99%

A Transcriptome-proteome Integrated Network Identifies Endoplasmic Reticulum thiol oxidoreductase (ERp57) as a Hub that Mediates Bone Metastasis

Santana-Codina

Carretero

Sanz‐Pamplona

et al. 2013

Molecular & Cellular Proteomics

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Bone metastasis is the most common distant relapse in breast cancer. The identification of key proteins involved in the osteotropic phenotype would represent a major step toward the development of new prognostic markers and therapeutic improvements. The aim of this study was to characterize functional phenotypes that favor bone metastasis in human breast cancer. We used the human breast cancer cell line MDA-MB-231 and its osteotropic BO2 subclone to identify crucial proteins in bone metastatic growth. We identified 31 proteins, 15 underexpressed and 16 overexpressed, in BO2 cells compared with parental cells. We employed a network-modeling approach in which these 31 candidate proteins were prioritized with respect to their potential in metastasis formation, based on the topology of the protein-protein interaction network and differential expression. The protein-protein interaction network provided a framework to study the functional relationships between biological molecules by attributing functions to genes whose functions had not been characterized. The combination of expression profiles and protein interactions revealed an endoplasmic reticulum-thiol oxidoreductase, ERp57, function- Large-scale genomic analysis has provided a wealth of information on biologically relevant systems, and the ability to analyze this information is crucial to uncovering important biological relationships. In breast cancer, microarray gene expression analysis is a promising technique for providing consistent patterns of variation in bone metastasis gene expression; the most common metastasis (80%) in those women who progress to an advanced stage of disease (1-4). However, a large number of genes with many diverse functions are identified as prognostic markers, without revealing much about the underlying biological mechanism.Genes that enhance or suppress bone metastasis are associated with multiple cellular processes that normally occur during metastasis progression, including survival and proliferation in the bone marrow microenvironment, and modification of bone structure and function (1). Many genes in this group encode secretory or cell surface proteins involved in cell homing to bone, angiogenesis, invasion, and osteoclast recruitment (1, 2, 5). Moreover, emerging evidence from murine models suggests that tumor-specific endocrine factors systematically stimulate the quiescent bone marrow compartment (BM), resulting in a BM-derived tumor microenvironment From the ‡Biological Clues

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confidence: 99%

A Transcriptome-proteome Integrated Network Identifies Endoplasmic Reticulum thiol oxidoreductase (ERp57) as a Hub that Mediates Bone Metastasis

Santana-Codina

Carretero

Sanz‐Pamplona

et al. 2013

Molecular & Cellular Proteomics

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“…Therefore, integration of information listed in different databases is essential because each database has a different coverage and there is sufficient diversity across databases. For example, the BIANA platform provides one such capability to retrieve interactions across many relevant databases through a single interface [17]. The only pathogen for which listing of interactions in databases is not the rate-limiting step is HIV-1, for which thousands of PPI are listed in several databases, most comprehensively in the HIV-1, Human Protein Interaction Database (http://www.ncbi.nlm.nih.gov/RefSeq/HIVInteractions/index.html).…”

Section: Gathering the Current Salmonella–host Interactome: Literatmentioning

confidence: 99%

The current Salmonella‐host interactome

Schleker

Sun

Balachandran

et al. 2011

Proteomics Clinical Apps

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Salmonella bacteria cause millions of infections and thousands of deaths every year. This pathogen has an unusually broad host range including humans, animals, and even plants. During infection, Salmonella expresses a variety of virulence factors and effectors that are delivered into the host cell triggering cellular responses through protein–protein interactions (PPIs) with host cell proteins which make the pathogen’s invasion and replication possible. To speed up proteomic efforts in elucidating Salmonella–host interactomes, we carried out a survey of the currently published Salmonella–host PPI. Such a list can serve as the gold standard for computational models aimed at predicting Salmonella–host interactomes through integration of large-scale biological data sources. Manual literature and database search of >2200 journal articles and >100 databases resulted in a gold standard list of currently 62 PPI, including primarily interactions of Salmonella proteins with human and mouse proteins. Only six of these interactions were directly retrievable from PPI databases and 16 were highlighted in databases featuring literature extracts. Thus, the literature survey resulted in the most complete interactome available to date for Salmonella. Pathway analysis using Ingenuity and Broad Gene Set Enrichment Analysis (GSEA) software revealed among general pathways such as MAPK signaling in particular those related to cell death as well as cell morphology, turnover, and interactions, in addition to response to not only Salmonella but also other pathogenic – viral and bacterial – infections. The list of interactions is available at http://www.shiprec.org/indicationslist.htm

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“…Hence, at the present time, a vast insight into the currently known interactome can only be achieved by means of integration of -primary PPI datasets. To this end, various PPI meta-databases have been created over the past few years [11–19]. The integration process is cumbersome due to PPI annotation discrepancies between the source databases, stemming from distinct curation rules, the use of incompatible interactor descriptors and the selection of different primary protein identifier types (i.e.…”

Section: Introductionmentioning

confidence: 99%

PICKLE 2.0: A human protein-protein interaction meta-database employing data integration via genetic information ontology

2017

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It has been acknowledged that source databases recording experimentally supported human protein-protein interactions (PPIs) exhibit limited overlap. Thus, the reconstruction of a comprehensive PPI network requires appropriate integration of multiple heterogeneous primary datasets, presenting the PPIs at various genetic reference levels. Existing PPI meta-databases perform integration via normalization; namely, PPIs are merged after converted to a certain target level. Hence, the node set of the integrated network depends each time on the number and type of the combined datasets. Moreover, the irreversible a priori normalization process hinders the identification of normalization artifacts in the integrated network, which originate from the nonlinearity characterizing the genetic information flow. PICKLE (Protein InteraCtion KnowLedgebasE) 2.0 implements a new architecture for this recently introduced human PPI meta-database. Its main novel feature over the existing meta-databases is its approach to primary PPI dataset integration via genetic information ontology. Building upon the PICKLE principles of using the reviewed human complete proteome (RHCP) of UniProtKB/Swiss-Prot as the reference protein interactor set, and filtering out protein interactions with low probability of being direct based on the available evidence, PICKLE 2.0 first assembles the RHCP genetic information ontology network by connecting the corresponding genes, nucleotide sequences (mRNAs) and proteins (UniProt entries) and then integrates PPI datasets by superimposing them on the ontology network without any a priori transformations. Importantly, this process allows the resulting heterogeneous integrated network to be reversibly normalized to any level of genetic reference without loss of the original information, the latter being used for identification of normalization biases, and enables the appraisal of potential false positive interactions through PPI source database cross-checking. The PICKLE web-based interface (www.pickle.gr) allows for the simultaneous query of multiple entities and provides integrated human PPI networks at either the protein (UniProt) or the gene level, at three PPI filtering modes.

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Biana: a software framework for compiling biological interactions and analyzing networks

Cited by 79 publications

References 68 publications

A Transcriptome-proteome Integrated Network Identifies Endoplasmic Reticulum thiol oxidoreductase (ERp57) as a Hub that Mediates Bone Metastasis

A Transcriptome-proteome Integrated Network Identifies Endoplasmic Reticulum thiol oxidoreductase (ERp57) as a Hub that Mediates Bone Metastasis

The current Salmonella‐host interactome

PICKLE 2.0: A human protein-protein interaction meta-database employing data integration via genetic information ontology

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