A mouse protein interactome through combined literature mining with multiple sources of interaction evidence

Protein–protein interactions (PPIs) play crucial roles in a number of biological processes. Recently, protein interaction networks (PINs) for several model organisms and humans have been generated, but few large-scale researches for mice have ever been made neither experimentally nor computationally. In the work, we undertook an effort to map a mouse PIN, in which protein interactions are hidden in enormous amount of biomedical literatures. Following a co-occurrence-based text-mining approach, a probabilistic model—naïve Bayesian was used to filter false-positive interactions by integrating heterogeneous kinds of evidence from genomic and proteomic datasets. A support vector machine algorithm was further used to choose protein pairs with physical interactions. By comparing with the currently available PPI datasets from several model organisms and humans, it showed that the derived mouse PINs have similar topological properties at the global level, but a high local divergence. The mouse protein interaction dataset is stored in the Mouse protein–protein interaction DataBase (MppDB) that is useful source of information for system-level understanding of gene function and biological processes in mammals. Access to the MppDB database is public available at http://bio.scu.edu.cn/mppi.     

GDPC: connecting researchers with multiple integrated data sources

The goal of this project is to simplify access to genomic diversity and phenotype data, thereby encouraging reuse of this data. The Genomic Diversity and Phenotype Connection (GDPC) accomplishes this by retrieving data from one or more data sources and by allowing researchers to analyze integrated data in a standard format. GDPC is written in JAVA and provides (1) data sources available as web services that transfer XML formatted data via the SOAP protocol; (2) a JAVA API for programmatic access to data sources; and (3) a front-end application that allows users to manage data sources, retrieve data based on filters, sort/group data based on property values and save/open the data as XML files. AVAILABILITY: The source code, compiled code, documentation and GDPC Browser are freely available at: www.maizegenetics.net/gdpc/index.html the current release of GDPC is version 1.0, with updated releases planned for the future. Comments are welcome.     

Multiclass microarray data classification based on confidence evaluation

Microarray technology is becoming a powerful tool for clinical diagnosis, as it has potential to discover gene expression patterns that are characteristic for a particular disease. To date, this possibility has received much attention in the context of cancer research, especially in tumor classification. However, most published articles have concentrated on the development of binary classification methods while neglected ubiquitous multiclass problems. Unfortunately, only a few multiclass classification approaches have had poor predictive accuracy. In an effort to improve classification accuracy, we developed a novel multiclass microarray data classification method. First, we applied a "one versus rest-support vector machine" to classify the samples. Then the classification confidence of each testing sample was evaluated according to its distribution in feature space and some with poor confidence were extracted. Next, a novel strategy, which we named as "class priority estimation method based on centroid distance", was used to make decisions about categories for those poor confidence samples. This approach was tested on seven benchmark multiclass microarray datasets, with encouraging results, demonstrating effectiveness and feasibility.     

A direct comparison of protein interaction confidence assignment schemes

Background  

Recent technological advances have enabled high-throughput measurements of protein-protein interactions in the cell, producing large protein interaction networks for various species at an ever-growing pace. However, common technologies like yeast two-hybrid may experience high rates of false positive detection. To combat false positive discoveries, a number of different methods have been recently developed that associate confidence scores with protein interactions. Here, we perform a rigorous comparative analysis and performance assessment among these different methods.     

Predicting protein function from protein/protein interaction data: a probabilistic approach

MOTIVATION:The development of experimental methods for genome scale analysis of molecular interaction networks has made possible new approaches to inferring protein function. This paper describes a method of assigning functions based on a probabilistic analysis of graph neighborhoods in a protein-protein interaction network. The method exploits the fact that graph neighbors are more likely to share functions than nodes which are not neighbors. A binomial model of local neighbor function labeling probability is combined with a Markov random field propagation algorithm to assign function probabilities for proteins in the network. RESULTS: We applied the method to a protein-protein interaction dataset for the yeast Saccharomyces cerevisiae using the Gene Ontology (GO) terms as function labels. The method reconstructed known GO term assignments with high precision, and produced putative GO assignments to 320 proteins that currently lack GO annotation, which represents about 10% of the unlabeled proteins in S. cerevisiae.     

Analyzing yeast protein-protein interaction data obtained from different sources

High-throughput methods for detecting protein interactions, such as mass spectrometry and yeast two-hybrid assays, continue to produce vast amounts of data that may be exploited to infer protein function and regulation. As this article went to press, the pool of all published interaction information on Saccharomyces cerevisiae was 15,143 interactions among 4,825 proteins, and power-law scaling supports an estimate of 20,000 specific protein interactions. To investigate the biases, overlaps, and complementarities among these data, we have carried out an analysis of two high-throughput mass spectrometry (HMS)-based protein interaction data sets from budding yeast, comparing them to each other and to other interaction data sets. Our analysis reveals 198 interactions among 222 proteins common to both data sets, many of which reflect large multiprotein complexes. It also indicates that a "spoke" model that directly pairs bait proteins with associated proteins is roughly threefold more accurate than a "matrix" model that connects all proteins. In addition, we identify a large, previously unsuspected nucleolar complex of 148 proteins, including 39 proteins of unknown function. Our results indicate that existing large-scale protein interaction data sets are nonsaturating and that integrating many different experimental data sets yields a clearer biological view than any single method alone.     

Marine reserve spillover: Modelling from multiple data sources

The functional form of spillover, measured as a gradient of abundance of fish, may provide insight about processes that control the spatial distribution of fish inside and outside the MPA. In this study, we aimed to infer on spillover mechanism of Diplodus spp. (family Sparidae) from a Mediterranean MPA (Carry-le-Rouet, France) from visual censuses and artisanal fisheries data. From the existing literature, three potential functional forms of spillover such as a linear gradient, an exponential gradient and a logistic gradient are defined. Each functional form is included in a spatial generalized linear mixed model allowing accounting for spatial autocorrelation of data. We select between the different forms of gradients by using a Bayesian model selection procedure. In a first step, the functional form of the spillover for visual census and artisanal fishing data is assessed separately. For both sets of data, our model selection favoured the negative exponential model, evidencing a decrease of the spatial abundance of fish vanishing around 1000 m from the MPA border. We combined both datasets in a joint model by including an observability parameter. This parameter captures how the different sources of data quantify the underlying spatial distribution of the harvested species. This enabled us to demonstrate that the different sampling methods do not affect the estimation of the underlying spatial distribution of Diplodus spp. inside and outside the MPA. We show that data from different sources can be pooled through spatial generalized linear mixed model. Our findings allow to better understand the underlying mechanisms that control spillover of fish from MPA.     

In silico gene prioritization by integrating multiple data sources

Identifying disease genes is crucial to the understanding of disease pathogenesis, and to the improvement of disease diagnosis and treatment. In recent years, many researchers have proposed approaches to prioritize candidate genes by considering the relationship of candidate genes and existing known disease genes, reflected in other data sources. In this paper, we propose an expandable framework for gene prioritization that can integrate multiple heterogeneous data sources by taking advantage of a unified graphic representation. Gene-gene relationships and gene-disease relationships are then defined based on the overall topology of each network using a diffusion kernel measure. These relationship measures are in turn normalized to derive an overall measure across all networks, which is utilized to rank all candidate genes. Based on the informativeness of available data sources with respect to each specific disease, we also propose an adaptive threshold score to select a small subset of candidate genes for further validation studies. We performed large scale cross-validation analysis on 110 disease families using three data sources. Results have shown that our approach consistently outperforms other two state of the art programs. A case study using Parkinson disease (PD) has identified four candidate genes (UBB, SEPT5, GPR37 and TH) that ranked higher than our adaptive threshold, all of which are involved in the PD pathway. In particular, a very recent study has observed a deletion of TH in a patient with PD, which supports the importance of the TH gene in PD pathogenesis. A web tool has been implemented to assist scientists in their genetic studies.     

Greedily building protein networks with confidence

MOTIVATION: With genome sequences complete for human and model organisms, it is essential to understand how individual genes and proteins are organized into biological networks. Much of the organization is revealed by proteomics experiments that now generate torrents of data. Extracting relevant complexes and pathways from high-throughput proteomics data sets has posed a challenge, however, and new methods to identify and extract networks are essential. We focus on the problem of building pathways starting from known proteins of interest. RESULTS: We have developed an efficient, greedy algorithm, SEEDY, that extracts biologically relevant biological networks from protein-protein interaction data, building out from selected seed proteins. The algorithm relies on our previous study establishing statistical confidence levels for interactions generated by two-hybrid screens and inferred from mass spectrometric identification of protein complexes. We demonstrate the ability to extract known yeast complexes from high-throughput protein interaction data with a tunable parameter that governs the trade-off between sensitivity and selectivity. DNA damage repair pathways are presented as a detailed example. We highlight the ability to join heterogeneous data sets, in this case protein-protein interactions and genetic interactions, and the appearance of cross-talk between pathways caused by re-use of shared components. SIGNIFICANCE AND COMPARISON: The significance of the SEEDY algorithm is that it is fast, running time O[(E + V) log V] for V proteins and E interactions, a single adjustable parameter controls the size of the pathways that are generated, and an associated P-value indicates the statistical confidence that the pathways are enriched for proteins with a coherent function. Previous approaches have focused on extracting sub-networks by identifying motifs enriched in known biological networks. SEEDY provides the complementary ability to perform a directed search based on proteins of interest. AVAILABILITY: SEEDY software (Perl source), data tables and confidence score models (R source) are freely available from the author.     

BioGuideSRS: querying multiple sources with a user-centric perspective

Biologists are frequently faced with the problem of integrating information from multiple heterogeneous sources with their own experimental data. Given the large number of public sources, it is difficult to choose which sources to integrate without assistance. When doing this manually, biologists differ in their preferences concerning the sources to be queried as well as the strategies, i.e. the querying process they follow for navigating through the sources. In response to these findings, we have developed BioGuide to assist scientists search for relevant data within external sources while taking their preferences and strategies into account. In this article, we present BioGuideSRS, a user-friendly system which automatically retrieves instances of data by using BioGuide on top of the sequence retrieval system (SRS). BioGuideSRS is an Applet that can be run from its web page on any system with Java 5.0. AVAILABILITY: http://www.bioguide-project.net.     

Iterative cluster analysis of protein interaction data

MOTIVATION: Generation of fast tools of hierarchical clustering to be applied when distances among elements of a set are constrained, causing frequent distance ties, as happens in protein interaction data. RESULTS: We present in this work the program UVCLUSTER, that iteratively explores distance datasets using hierarchical clustering. Once the user selects a group of proteins, UVCLUSTER converts the set of primary distances among them (i.e. the minimum number of steps, or interactions, required to connect two proteins) into secondary distances that measure the strength of the connection between each pair of proteins when the interactions for all the proteins in the group are considered. We show that this novel strategy has advantages over conventional clustering methods to explore protein-protein interaction data. UVCLUSTER easily incorporates the information of the largest available interaction datasets to generate comprehensive primary distance tables. The versatility, simplicity of use and high speed of UVCLUSTER on standard personal computers suggest that it can be a benchmark analytical tool for interactome data analysis. AVAILABILITY: The program is available upon request from the authors, free for academic users. Additional information available at http://www.uv.es/genomica/UVCLUSTER.     

PHIDIAS: a pathogen-host interaction data integration and analysis system

The Pathogen-Host Interaction Data Integration and Analysis System (PHIDIAS) is a web-based database system that serves as a centralized source to search, compare, and analyze integrated genome sequences, conserved domains, and gene expression data related to pathogen-host interactions (PHIs) for pathogen species designated as high priority agents for public health and biological security. In addition, PHIDIAS allows submission, search and analysis of PHI genes and molecular networks curated from peer-reviewed literature. PHIDIAS is publicly available at .     

Confirmation of human protein interaction data by human expression data

Background  

With microarray technology the expression of thousands of genes can be measured simultaneously. It is well known that the expression levels of genes of interacting proteins are correlated significantly more strongly in Saccharomyces cerevisiae than those of proteins that are not interacting. The objective of this work is to investigate whether this observation extends to the human genome.     

Inference of protein-protein interaction networks from multiple heterogeneous data

Protein-protein interaction (PPI) prediction is a central task in achieving a better understanding of cellular and intracellular processes. Because high-throughput experimental methods are both expensive and time-consuming, and are also known of suffering from the problems of incompleteness and noise, many computational methods have been developed, with varied degrees of success. However, the inference of PPI network from multiple heterogeneous data sources remains a great challenge. In this work, we developed a novel method based on approximate Bayesian computation and modified differential evolution sampling (ABC-DEP) and regularized laplacian (RL) kernel. The method enables inference of PPI networks from topological properties and multiple heterogeneous features including gene expression and Pfam domain profiles, in forms of weighted kernels. The optimal weights are obtained by ABC-DEP, and the kernel fusion built based on optimal weights serves as input to RL to infer missing or new edges in the PPI network. Detailed comparisons with control methods have been made, and the results show that the accuracy of PPI prediction measured by AUC is increased by up to 23 %, as compared to a baseline without using optimal weights. The method can provide insights into the relations between PPIs and various feature kernels and demonstrates strong capability of predicting faraway interactions that cannot be well detected by traditional RL method.