DEMA: a distance-bounded energy-field minimization algorithm to model and layout biomolecular networks with quantitative features.

SUMMARY: In biology, graph layout algorithms can reveal comprehensive biological contexts by visually positioning graph nodes in their relevant neighborhoods. A layout software algorithm/engine commonly takes a set of nodes and edges and produces layout coordinates of nodes according to edge constraints. However, current layout engines normally do not consider node, edge or node-set properties during layout and only curate these properties after the layout is created. Here, we propose a new layout algorithm, distance-bounded energy-field minimization algorithm (DEMA), to natively consider various biological factors, i.e., the strength of gene-to-gene association, the gene's relative contribution weight and the functional groups of genes, to enhance the interpretation of complex network graphs. In DEMA, we introduce a parameterized energy model where nodes are repelled by the network topology and attracted by a few biological factors, i.e., interaction coefficient, effect coefficient and fold change of gene expression. We generalize these factors as gene weights, protein-protein interaction weights, gene-to-gene correlations and the gene set annotations-four parameterized functional properties used in DEMA. Moreover, DEMA considers further attraction/repulsion/grouping coefficient to enable different preferences in generating network views. Applying DEMA, we performed two case studies using genetic data in autism spectrum disorder and Alzheimer's disease, respectively, for gene candidate discovery. Furthermore, we implement our algorithm as a plugin to Cytoscape, an open-source software platform for visualizing networks; hence, it is convenient. Our software and demo can be freely accessed at http://discovery.informatics.uab.edu/dema. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Pathway and network analysis in proteomics.

Proteomics is inherently a systems science that studies not only measured protein and their expressions in a cell, but also the interplay of proteins, protein complexes, signaling pathways, and network modules. There is a rapid accumulation of Proteomics data in recent years. However, Proteomics data are highly variable, with results sensitive to data preparation methods, sample condition, instrument types, and analytical methods. To address the challenge in Proteomics data analysis, we review current tools being developed to incorporate biological function and network topological information. We categorize these tools into four types: tools with basic functional information and little topological features (e.g., GO category analysis), tools with rich functional information and little topological features (e.g., GSEA), tools with basic functional information and rich topological features (e.g., Cytoscape), and tools with rich functional information and rich topological features (e.g., PathwayExpress). We first review the potential application of these tools to Proteomics; then we review tools that can achieve automated learning of pathway modules and features, and tools that help perform integrated network visual analytics.

Discovering breast cancer drug candidates from biomedical literature.

We developed a new paradigm with the ultimate goal of enabling disease-specific drug candidate discovery with molecular-level evidences generated from literature and prior knowledge. We showed how to implement the paradigm by building a prototype literature-mining framework and performing drug-protein association mining for breast cancer drug discovery. In a molecular pharmacology study of breast cancer, 79.2% of 729 enriched drugs in 'Organic Chemicals' category were validated to be disease-related, and the remaining 20.8% were also investigated as potential for future molecular therapeutics studies. 'Doxorubicin', 'Etoposide' and 'Paclitaxel' were identified as having similar pharmacological profiles to treat breast cancer.

Network topological reordering revealing systemic patterns in yeast protein interaction networks.

Identifying candidate genes/proteins involved in human disease specific molecular pathways or networks has been a primary focus of biomedical research. Although node ranking and graph clustering methods can help identify localized topological properties in a network, it remains unclear how the results should be interpreted in biological functional context in systems-level. In complex biomolecular interaction networks, biomolecular entities may not have absolute ranks or clear cluster boundary among them. We presented Ant Colony Optimization Reordering (ACOR) method to examine emerging network properties. The task of reordering nodes is represented as the problem of finding optimal density distribution of "ant colony" on all nodes of the network. We applied ACOR method to re-analyze a yeast protein-protein interaction (PPI) network annotated with functional information (i.e., lethality), which revealed intriguing systems-level functional features.

Building disease-specific drug-protein connectivity maps from molecular interaction networks and PubMed abstracts.

The recently proposed concept of molecular connectivity maps enables researchers to integrate experimental measurements of genes, proteins, metabolites, and drug compounds under similar biological conditions. The study of these maps provides opportunities for future toxicogenomics and drug discovery applications. We developed a computational framework to build disease-specific drug-protein connectivity maps. We integrated gene/protein and drug connectivity information based on protein interaction networks and literature mining, without requiring gene expression profile information derived from drug perturbation experiments on disease samples. We described the development and application of this computational framework using Alzheimer's Disease (AD) as a primary example in three steps. First, molecular interaction networks were incorporated to reduce bias and improve relevance of AD seed proteins. Second, PubMed abstracts were used to retrieve enriched drug terms that are indirectly associated with AD through molecular mechanistic studies. Third and lastly, a comprehensive AD connectivity map was created by relating enriched drugs and related proteins in literature. We showed that this molecular connectivity map development approach outperformed both curated drug target databases and conventional information retrieval systems. Our initial explorations of the AD connectivity map yielded a new hypothesis that diltiazem and quinidine may be investigated as candidate drugs for AD treatment. Molecular connectivity maps derived computationally can help study molecular signature differences between different classes of drugs in specific disease contexts. To achieve overall good data coverage and quality, a series of statistical methods have been developed to overcome high levels of data noise in biological networks and literature mining results. Further development of computational molecular connectivity maps to cover major disease areas will likely set up a new model for drug development, in which therapeutic/toxicological profiles of candidate drugs can be checked computationally before costly clinical trials begin.

HAPPI: an online database of comprehensive human annotated and predicted protein interactions.

BACKGROUND: Human protein-protein interaction (PPIs) data are the foundation for understanding molecular signalling networks and the functional roles of biomolecules. Several human PPI databases have become available; however, comparisons of these datasets have suggested limited data coverage and poor data quality. Ongoing collection and integration of human PPIs from different sources, both experimentally and computationally, can enable disease-specific network biology modelling in translational bioinformatics studies. RESULTS: We developed a new web-based resource, the Human Annotated and Predicted Protein Interaction (HAPPI) database, located at http://bio.informatics.iupui.edu/HAPPI/. The HAPPI database was created by extracting and integrating publicly available protein interaction databases, including HPRD, BIND, MINT, STRING, and OPHID, using database integration techniques. We designed a unified entity-relationship data model to resolve semantic level differences of diverse concepts involved in PPI data integration. We applied a unified scoring model to give each PPI a measure of its reliability that can place each PPI at one of the five star rank levels from 1 to 5. We assessed the quality of PPIs contained in the new HAPPI database, using evolutionary conserved co-expression pairs called "MetaGene" pairs to measure the extent of MetaGene pair and PPI pair overlaps. While the overall quality of the HAPPI database across all star ranks is comparable to the overall qualities of HPRD or IntNetDB, the subset of the HAPPI database with star ranks between 3 and 5 has a much higher average quality than all other human PPI databases. As of summer 2008, the database contains 142,956 non-redundant, medium to high-confidence level human protein interaction pairs among 10,592 human proteins. The HAPPI database web application also provides ..." should be "The HAPPI database web application also provides hyperlinked information of genes, pathways, protein domains, protein structure displays, and sequence feature maps for interactive exploration of PPI data in the database. CONCLUSION: HAPPI is by far the most comprehensive public compilation of human protein interaction information. It enables its users to fully explore PPI data with quality measures and annotated information necessary for emerging network biology studies.

Dissecting the human plasma proteome and inflammatory response biomarkers.

A central focus of clinical proteomics is to search for biomarkers in plasma for diagnostic and therapeutic use. We studied a set of plasma proteins accessed from the Healthy Human Individual's Integrated Plasma Proteome (HIP(2)) database, a larger set of curated human proteins, and a subset of inflammatory proteins, for overlap with sets of known protein biomarkers, drug targets, and secreted proteins. Most inflammatory proteins were found to occur in plasma, and over three times the level of biomarkers were found in inflammatory plasma proteins and their interacting protein neighbors compared to the sets of plasma and curated human proteins. Percentage overlaps with Gene Ontology terms were similar between the curated human set and plasma protein set, yet the set of inflammatory plasma proteins had a distinct ontology-based profile. Most of the major hub proteins within protein-protein interaction networks of tissue-specific sets of inflammatory proteins were found to occur in disease pathways. The present study presents a systematic approach for profiling a plasma subproteome's relationship to both its potential range of clinical application and its overlap with complex disease.

Pattern discovery in breast cancer specific protein interaction network.

The interest in indentifying novel biomarkers for early stage breast cancer (BRCA) detection has become grown significantly in recent years. From a view of network biology, one of the emerging themes today is to re-characterize a protein's biological functions in its molecular network. Although many methods have been presented, including network-based gene ranking for molecular biomarker discovery, and graph clustering for functional module discovery, it is still hard to find systems-level properties hidden in disease specific molecular networks. We reconstructed BRCA-related protein interaction network by using BRCA-associated genes/proteins as seeds, and expanding them in an integrated protein interaction database. We further developed a computational framework based on Ant Colony Optimization to rank network nodes. The task of ranking nodes is represented as the problem of finding optimal density distributions of "ant colonies" on all nodes of the network. Our results revealed some interesting systems-level pattern in BRCA-related protein interaction network.

HIP2: an online database of human plasma proteins from healthy individuals.

BACKGROUND: With the introduction of increasingly powerful mass spectrometry (MS) techniques for clinical research, several recent large-scale MS proteomics studies have sought to characterize the entire human plasma proteome with a general objective for identifying thousands of proteins leaked from tissues in the circulating blood. Understanding the basic constituents, diversity, and variability of the human plasma proteome is essential to the development of sensitive molecular diagnosis and treatment monitoring solutions for future biomedical applications. Biomedical researchers today, however, do not have an integrated online resource in which they can search for plasma proteins collected from different mass spectrometry platforms, experimental protocols, and search software for healthy individuals. The lack of such a resource for comparisons has made it difficult to interpret proteomics profile changes in patients' plasma and to design protein biomarker discovery experiments. DESCRIPTION: To aid future protein biomarker studies of disease and health from human plasma, we developed an online database, HIP2 (Healthy Human Individual's Integrated Plasma Proteome). The current version contains 12,787 protein entries linked to 86,831 peptide entries identified using different MS platforms. CONCLUSION: This web-based database will be useful to biomedical researchers involved in biomarker discovery research. This database has been developed to be the comprehensive collection of healthy human plasma proteins, and has protein data captured in a relational database schema built to contain mappings of supporting peptide evidence from several high-quality and high-throughput mass-spectrometry (MS) experimental data sets. Users can search for plasma protein/peptide annotations, peptide/protein alignments, and experimental/sample conditions with options for filter-based retrieval to achieve greater analytical power for discovery and validation.

A case study of integrating protein interaction data using semantic web technology.

We describe a new ontology-driven semantic data integration approach for post-genome biology studies. Here, a view-based global schema can be automatically generated by merging RDF schemas from local databases. The semantic inconsistency of the merged schema is resolved by the creation of 'RDF ontology maps'. Data querying capability is accomplished with a virtual data repository, in which a D2RQ-based 'relational-to-RDF' map is developed to link schema to the relational database backend. With sample RDQL queries, we demonstrate that our approach significantly simplifies the retrieval of human protein interaction data from different databases containing hundreds of thousands of records.

Mining Alzheimer disease relevant proteins from integrated protein interactome data.

Huge unrealized post-genome opportunities remain in the understanding of detailed molecular mechanisms for Alzheimer Disease (AD). In this work, we developed a computational method to rank-order AD-related proteins, based on an initial list of AD-related genes and public human protein interaction data. In this method, we first collected an initial seed list of 65 AD-related genes from the OMIM database and mapped them to 70 AD seed proteins. We then expanded the seed proteins to an enriched AD set of 765 proteins using protein interactions from the Online Predicated Human Interaction Database (OPHID). We showed that the expanded AD-related proteins form a highly connected and statistically significant protein interaction sub-network. We further analyzed the sub-network to develop an algorithm, which can be used to automatically score and rank-order each protein for its biological relevance to AD pathways(s). Our results show that functionally relevant AD proteins were consistently ranked at the top: among the top 20 of 765 expanded AD proteins, 19 proteins are confirmed to belong to the original 70 AD seed protein set. Our method represents a novel use of protein interaction network data for Alzheimer disease studies and may be generalized for other disease areas in the future.

A statistical framework to discover true associations from multiprotein complex pull-down proteomics data sets.

Experimental processes to collect and process proteomics data are increasingly complex, and the computational methods to assess the quality and significance of these data remain unsophisticated. These challenges have led to many biological oversights and computational misconceptions. We developed an empirical Bayes model to analyze multiprotein complex (MPC) proteomics data derived from peptide mass spectrometry detections of purified protein complex pull-down experiments. Using our model and two yeast proteomics data sets, we estimated that there should be an average of about 20 true associations per MPC, almost 10 times as high as was previously estimated. For data sets generated to mimic a real proteome, our model achieved on average 80% sensitivity in detecting true associations, as compared with the 3% sensitivity in previous work, while maintaining a comparable false discovery rate of 0.3%. Cross-examination of our results with protein complexes confirmed by various experimental techniques demonstrates that many true associations that cannot be identified by previous approach are identified by our method.

Discover true association rates in multi-protein complex proteomics data sets.

Experimental processes to collect and process proteomics data are increasingly complex, while the computational methods to assess the quality and significance of these data remain unsophisticated. These challenges have led to many biological oversights and computational misconceptions. We developed a complete empirical Bayes model to analyze multi-protein complex (MPC) proteomics data derived from peptide mass spectrometry detections of purified protein complex pull-down experiments. Our model considers not only bait-prey associations, but also prey-prey associations missed in previous work. Using our model and a yeast MPC proteomics data set, we estimated that there should be an average of 28 true associations per MPC, almost ten times as high as was previously estimated. For data sets generated to mimic a real proteome, our model achieved on average 80% sensitivity in detecting true associations, as compared with the 3% sensitivity in previous work, while maintaining a comparable false discovery rate of 0.3%.