STRING: known and predicted protein-protein associations, integrated and transferred across organisms

Biomedical discovery acceleration, with applications to craniofacial development

The profusion of high-throughput instruments and the explosion of new results in the scientific literature, particularly in molecular biomedicine, is both a blessing and a curse to the bench researcher. Even knowledgeable and experienced scientists can benefit from computational tools that help navigate this vast and rapidly evolving terrain. In this paper, we describe a novel computational approach to this challenge, a knowledge-based system that combines reading, reasoning, and reporting methods to facilitate analysis of experimental data. Reading methods extract information from external resources, either by parsing structured data or using biomedical language processing to extract information from unstructured data, and track knowledge provenance. Reasoning methods enrich the knowledge that results from reading by, for example, noting two genes that are annotated to the same ontology term or database entry. Reasoning is also used to combine all sources into a knowledge network that represents the integration of all sorts of relationships between a pair of genes, and to calculate a combined reliability score. Reporting methods combine the knowledge network with a congruent network constructed from experimental data and visualize the combined network in a tool that facilitates the knowledge-based analysis of that data. An implementation of this approach, called the Hanalyzer, is demonstrated on a large-scale gene expression array dataset relevant to craniofacial development. The use of the tool was critical in the creation of hypotheses regarding the roles of four genes never previously characterized as involved in craniofacial development; each of these hypotheses was validated by further experimental work.

Uncovering new signaling proteins and potential drug targets through the interactome analysis of Mycobacterium tuberculosis

BACKGROUND

Analysis of the pathogen interactome is a powerful approach for dissecting potential signal transduction and virulence pathways. It also offers opportunities for exploring new drug targets.

RESULTS

In this study, a protein-protein interaction (PPI) network of Mycobacterium tuberculosis H37Rv was constructed using a homogenous protein mapping method, which has shown molecular chaperones, ribosomal proteins and ABC transporters to be highly interconnected proteins. A further analysis of this network unraveled the function of hypothetical proteins as well as a potential signaling pathway. A hypothetical protein, Rv2752c, which was linked to a metal cation-transporting ATPase, was characterized as a metal-beta-lactamase, through domain analysis in combination with an in vitro activity experiment. A second hypothetical protein, Rv1354c, and an unknown protein kinase, PknK, interacted with a similar group of inner membrane-associated ABC transporters in the PPI network. The interactions of Rv1354 with these proteins were also confirmed by a further bacterial two-hybrid analysis. According to protein domain structures, the unique M. tuberculosis Rv1354c gene was proposed, for the first time, to be responsible for the turnover of cyclic-di-GMP, a second messenger molecule in this bacterium. A further structure-based inhibitors screening for Rv1354c was also performed in silicon.

CONCLUSION

We constructed a comprehensive protein-protein interaction network for M. tuberculosis consisting of 738 proteins and 5639 interaction pairs. Our analysis unraveled the function of hypothetical proteins as well as a potential signaling pathway. The group of ABC transporters, PknK, and Rv1354c were proposed to constitute a potential membrane-associated signaling pathway that cooperatively responds to environmental stresses in M. tuberculosis. The study therefore provides valuable clues in exploring new signaling proteins, virulence pathways, and drug targets.

Expression profiling of hypothetical genes in Desulfovibrio vulgaris leads to improved functional annotation

Hypothetical (HyP) and conserved HyP genes account for >30% of sequenced bacterial genomes. For the sulfate-reducing bacterium Desulfovibrio vulgaris Hildenborough, 347 of the 3634 genes were annotated as conserved HyP (9.5%) along with 887 HyP genes (24.4%). Given the large fraction of the genome, it is plausible that some of these genes serve critical cellular roles. The study goals were to determine which genes were expressed and provide a more functionally based annotation. To accomplish this, expression profiles of 1234 HyP and conserved genes were used from transcriptomic datasets of 11 environmental stresses, complemented with shotgun LC–MS/MS and AMT tag proteomic data. Genes were divided into putatively polycistronic operons and those predicted to be monocistronic, then classified by basal expression levels and grouped according to changes in expression for one or multiple stresses. One thousand two hundred and twelve of these genes were transcribed with 786 producing detectable proteins. There was no evidence for expression of 17 predicted genes. Except for the latter, monocistronic gene annotation was expanded using the above criteria along with matching Clusters of Orthologous Groups. Polycistronic genes were annotated in the same manner with inferences from their proximity to more confidently annotated genes. Two targeted deletion mutants were used as test cases to determine the relevance of the inferred functional annotations.

Computational systems biology of the cell cycle

One of the early success stories of computational systems biology was the work done on cell-cycle regulation. The earliest mathematical descriptions of cell-cycle control evolved into very complex, detailed computational models that describe the regulation of cell division in many different cell types. On the way these models predicted several dynamical properties and unknown components of the system that were later experimentally verified/identified. Still, research on this field is far from over. We need to understand how the core cell-cycle machinery is controlled by internal and external signals, also in yeast cells and in the more complex regulatory networks of higher eukaryotes. Furthermore, there are many computational challenges what we face as new types of data appear thanks to continuing advances in experimental techniques. We have to deal with cell-to-cell variations, revealed by single cell measurements, as well as the tremendous amount of data flowing from high throughput machines. We need new computational concepts and tools to handle these data and develop more detailed, more precise models of cell-cycle regulation in various organisms. Here we review past and present of computational modeling of cell-cycle regulation, and discuss possible future directions of the field.

Prediction of protein-protein interactions based on PseAA composition and hybrid feature selection

Based on pseudo amino acid (PseAA) composition and a novel hybrid feature selection frame, this paper presents a computational system to predict the PPIs (protein-protein interactions) using 8796 protein pairs. These pairs are coded by PseAA composition, resulting in 114 features. A hybrid feature selection system, mRMR-KNNs-wrapper, is applied to obtain an optimized feature set by excluding poor-performed and/or redundant features, resulting in 103 remaining features. Using the optimized 103-feature subset, a prediction model is trained and tested in the k-nearest neighbors (KNNs) learning system. This prediction model achieves an overall accurate prediction rate of 76.18%, evaluated by 10-fold cross-validation test, which is 1.46% higher than using the initial 114 features and is 6.51% higher than the 20 features, coded by amino acid compositions. The PPIs predictor, developed for this research, is available for public use at http://chemdata.shu.edu.cn/ppi.

Ortholog-based protein-protein interaction prediction and its application to inter-species interactions

BACKGROUND

The rapid growth of protein-protein interaction (PPI) data has led to the emergence of PPI network analysis. Despite advances in high-throughput techniques, the interactomes of several model organisms are still far from complete. Therefore, it is desirable to expand these interactomes with ortholog-based and other methods.

RESULTS

Orthologous pairs of 18 eukaryotic species were expanded and merged with experimental PPI datasets. The contributions of interologs from each species were evaluated. The expanded orthologous pairs enable the inference of interologs for various species. For example, more than 32,000 human interactions can be predicted. The same dataset has also been applied to the prediction of host-pathogen interactions. PPIs between P. falciparum calmodulin and several H. sapiens proteins are predicted, and these interactions may contribute to the maintenance of host cell Ca2+ concentration. Using comparisons with Bayesian and structure-based approaches, interactions between putative HSP40 homologs of P. falciparum and the H. sapiens TNF receptor associated factor family are revealed, suggesting a role for these interactions in the interference of the human immune response to P. falciparum.

CONCLUSION

The PPI datasets are available from POINT http://point.bioinformatics.tw/ and POINeT http://poinet.bioinformatics.tw/. Further development of methods to predict host-pathogen interactions should incorporate multiple approaches in order to improve sensitivity, and should facilitate the identification of targets for drug discovery and design.

Combining multiple positive training sets to generate confidence scores for protein-protein interactions

Motivation: High-throughput experimental and computational methods are generating a wealth of protein–protein interaction data for a variety of organisms. However, data produced by current state-of-the-art methods include many false positives, which can hinder the analyses needed to derive biological insights. One way to address this problem is to assign confidence scores that reflect the reliability and biological significance of each interaction. Most previously described scoring methods use a set of likely true positives to train a model to score all interactions in a dataset. A single positive training set, however, may be biased and not representative of true interaction space.

Results: We demonstrate a method to score protein interactions by utilizing multiple independent sets of training positives to reduce the potential bias inherent in using a single training set. We used a set of benchmark yeast protein interactions to show that our approach outperforms other scoring methods. Our approach can also score interactions across data types, which makes it more widely applicable than many previously proposed methods. We applied the method to protein interaction data from both Drosophila melanogaster and Homo sapiens. Independent evaluations show that the resulting confidence scores accurately reflect the biological significance of the interactions.

Contact:rfinley@wayne.edu

Supplementary information:Supplementary data are available at Bioinformatics Online.

Comparative analysis of neurological disorders focuses genome-wide search for autism genes

The behaviors of autism overlap with a diverse array of other neurological disorders, suggesting common molecular mechanisms. We conducted a large comparative analysis of the network of genes linked to autism with those of 432 other neurological diseases to circumscribe a multi-disorder subcomponent of autism. We leveraged the biological process and interaction properties of these multi-disorder autism genes to overcome the across-the-board multiple hypothesis corrections that a purely data-driven approach requires. Using prior knowledge of biological process, we identified 154 genes not previously linked to autism of which 42% were significantly differentially expressed in autistic individuals. Then, using prior knowledge from interaction networks of disorders related to autism, we uncovered 334 new genes that interact with published autism genes, of which 87% were significantly differentially regulated in autistic individuals. Our analysis provided a novel picture of autism from the perspective of related neurological disorders and suggested a model by which prior knowledge of interaction networks can inform and focus genome-scale studies of complex neurological disorders.

STRING 8--a global view on proteins and their functional interactions in 630 organisms

Functional partnerships between proteins are at the core of complex cellular phenotypes, and the networks formed by interacting proteins provide researchers with crucial scaffolds for modeling, data reduction and annotation. STRING is a database and web resource dedicated to protein–protein interactions, including both physical and functional interactions. It weights and integrates information from numerous sources, including experimental repositories, computational prediction methods and public text collections, thus acting as a meta-database that maps all interaction evidence onto a common set of genomes and proteins. The most important new developments in STRING 8 over previous releases include a URL-based programming interface, which can be used to query STRING from other resources, improved interaction prediction via genomic neighborhood in prokaryotes, and the inclusion of protein structures. Version 8.0 of STRING covers about 2.5 million proteins from 630 organisms, providing the most comprehensive view on protein–protein interactions currently available. STRING can be reached at http://string-db.org/.

Proteome-wide prediction of PKA phosphorylation sites in eukaryotic kingdom

Protein phosphorylation is one of the most essential post-translational modifications (PTMs), and orchestrates a variety of cellular functions and processes. Besides experimental studies, numerous computational predictors implemented in various algorithms have been developed for phosphorylation sites prediction. However, large-scale predictions of kinase-specific phosphorylation sites have not been successfully pursued and remained to be a great challenge. In this work, we raised a "kiss farewell" model and conducted a high-throughput prediction of cAMP-dependent kinase (PKA) phosphorylation sites. Since a protein kinase (PK) should at least "kiss" its substrates and then run away, we proposed a PKA-binding protein to be a potential PKA substrate if at least one PKA site was predicted. To improve the prediction specificity, we reduced false positive rate (FPR) less than 1% when the cut-off value was set as 4. Successfully, we predicted 1387, 630, 568 and 912 potential PKA sites from 410, 217, 173 and 260 PKA-interacting proteins in Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster and Homo sapiens, respectively. Most of these potential phosphorylation sites remained to be experimentally verified. In addition, we detected two sites in one of PKA regulatory subunits to be conserved in eukaryotes as potentially ancient regulatory signals. Our prediction results provide an excellent resource for delineating PKA-mediated signaling pathways and their system integration underlying cellular dynamics and plasticity.

Text mining

One of the fastest-growing fields in bioinformatics is text mining: the application of natural language processing techniques to problems of knowledge management and discovery, using large collections of biological or biomedical text such as MEDLINE. The techniques used in text mining range from the very simple (e.g., the inference of relationships between genes from frequent proximity in documents) to the complex and computationally intensive (e.g., the analysis of sentence structures with parsers in order to extract facts about protein-protein interactions from statements in the text). This chapter presents a general introduction to some of the key principles and challenges of natural language processing, and introduces some of the tools available to end-users and developers. A case study describes the construction and testing of a simple tool designed to tackle a task that is crucial to almost any application of text mining in bioinformatics--identifying gene/protein names in text and mapping them onto records in an external database.

MetaSim: a sequencing simulator for genomics and metagenomics

Background

The new research field of metagenomics is providing exciting insights into various, previously unclassified ecological systems. Next-generation sequencing technologies are producing a rapid increase of environmental data in public databases. There is great need for specialized software solutions and statistical methods for dealing with complex metagenome data sets.

Methodology/Principal Findings

To facilitate the development and improvement of metagenomic tools and the planning of metagenomic projects, we introduce a sequencing simulator called MetaSim. Our software can be used to generate collections of synthetic reads that reflect the diverse taxonomical composition of typical metagenome data sets. Based on a database of given genomes, the program allows the user to design a metagenome by specifying the number of genomes present at different levels of the NCBI taxonomy, and then to collect reads from the metagenome using a simulation of a number of different sequencing technologies. A population sampler optionally produces evolved sequences based on source genomes and a given evolutionary tree.

Conclusions/Significance

MetaSim allows the user to simulate individual read datasets that can be used as standardized test scenarios for planning sequencing projects or for benchmarking metagenomic software.

DroID: the Drosophila Interactions Database, a comprehensive resource for annotated gene and protein interactions

BACKGROUND

Charting the interactions among genes and among their protein products is essential for understanding biological systems. A flood of interaction data is emerging from high throughput technologies, computational approaches, and literature mining methods. Quick and efficient access to this data has become a critical issue for biologists. Several excellent multi-organism databases for gene and protein interactions are available, yet most of these have understandable difficulty maintaining comprehensive information for any one organism. No single database, for example, includes all available interactions, integrated gene expression data, and comprehensive and searchable gene information for the important model organism, Drosophila melanogaster.

DESCRIPTION

DroID, the Drosophila Interactions Database, is a comprehensive interactions database designed specifically for Drosophila. DroID houses published physical protein interactions, genetic interactions, and computationally predicted interactions, including interologs based on data for other model organisms and humans. All interactions are annotated with original experimental data and source information. DroID can be searched and filtered based on interaction information or a comprehensive set of gene attributes from Flybase. DroID also contains gene expression and expression correlation data that can be searched and used to filter datasets, for example, to focus a study on sub-networks of co-expressed genes. To address the inherent noise in interaction data, DroID employs an updatable confidence scoring system that assigns a score to each physical interaction based on the likelihood that it represents a biologically significant link.

CONCLUSION

DroID is the most comprehensive interactions database available for Drosophila. To facilitate downstream analyses, interactions are annotated with original experimental information, gene expression data, and confidence scores. All data in DroID are freely available and can be searched, explored, and downloaded through three different interfaces, including a text based web site, a Java applet with dynamic graphing capabilities (IM Browser), and a Cytoscape plug-in. DroID is available at http://www.droidb.org.

The interactome: predicting the protein-protein interactions in cells

The term Interactome describes the set of all molecular interactions in cells, especially in the context of protein-protein interactions. These interactions are crucial for most cellular processes, so the full representation of the interaction repertoire is needed to understand the cell molecular machinery at the system biology level. In this short review, we compare various methods for predicting protein-protein interactions using sequence and structure information. The ultimate goal of those approaches is to present the complete methodology for the automatic selection of interaction partners using their amino acid sequences and/or three dimensional structures, if known. Apart from a description of each method, details of the software or web interface needed for high throughput prediction on the whole genome scale are also provided. The proposed validation of the theoretical methods using experimental data would be a better assessment of their accuracy.

Evolutionary origins of human apoptosis and genome-stability gene networks

Apoptosis is essential for complex multicellular organisms and its failure is associated with genome instability and cancer. Interactions between apoptosis and genome-maintenance mechanisms have been extensively documented and include transactivation-independent and -dependent functions, in which the tumor-suppressor protein p53 works as a 'molecular node' in the DNA-damage response. Although apoptosis and genome stability have been identified as ancient pathways in eukaryote phylogeny, the biological evolution underlying the emergence of an integrated system remains largely unknown. Here, using computational methods, we reconstruct the evolutionary scenario that linked apoptosis with genome stability pathways in a functional human gene/protein association network. We found that the entanglement of DNA repair, chromosome stability and apoptosis gene networks appears with the caspase gene family and the antiapoptotic gene BCL2. Also, several critical nodes that entangle apoptosis and genome stability are cancer genes (e.g. ATM, BRCA1, BRCA2, MLH1, MSH2, MSH6 and TP53), although their orthologs have arisen in different points of evolution. Our results demonstrate how genome stability and apoptosis were co-opted during evolution recruiting genes that merge both systems. We also provide several examples to exploit this evolutionary platform, where we have judiciously extended information on gene essentiality inferred from model organisms to human.

Transcriptomic signature of bexarotene (rexinoid LGD1069) on mammary gland from three transgenic mouse mammary cancer models

Background

The rexinoid bexarotene (LGD1069, Targretin) is a highly selective retinoid × receptor (RXR) agonist that inhibits the growth of pre-malignant and malignant breast cells. Bexarotene was shown to suppress the development of breast cancer in transgenic mice models without side effects. The chemopreventive effects of bexarotene are due to transcriptional modulation of cell proliferation, differentiation and apoptosis. Our goal in the present study was to obtain a profile of the genes modulated by bexarotene on mammary gland from three transgenic mouse mammary cancer models in an effort to elucidate its molecular mechanism of action and for the identification of biomarkers of effectiveness.

Methods

Serial analysis of gene expression (SAGE) was employed to profile the transcriptome of p53-null, MMTV-ErbB2, and C3(1)-SV40 mammary cells obtained from mice treated with bexarotene and their corresponding controls.

Results

This resulted in a dataset of approximately 360,000 transcript tags representing over 20,000 mRNAs from a total of 6 different SAGE libraries. Analysis of gene expression changes induced by bexarotene in mammary gland revealed that 89 genes were dysregulated among the three transgenic mouse mammary models. From these, 9 genes were common to the three models studied.

Conclusion

Analysis of the indicated core of transcripts and protein-protein interactions of this commonly modulated genes indicate two functional modules significantly affected by rexinoid bexarotene related to protein biosynthesis and bioenergetics signatures, in addition to the targeting of cancer-causing genes related with cell proliferation, differentiation and apoptosis.

Cytological characterization of YpsB, a novel component of the Bacillus subtilis divisome

Cell division in bacteria is carried out by an elaborate molecular machine composed of more than a dozen proteins and known as the divisome. Here we describe the characterization of a new divisome protein in Bacillus subtilis called YpsB. Sequence comparisons and phylogentic analysis demonstrated that YpsB is a paralog of the division site selection protein DivIVA. YpsB is present in several gram-positive bacteria and likely originated from the duplication of a DivIVA-like gene in the last common ancestor of bacteria of the orders Bacillales and Lactobacillales. We used green fluorescent protein microscopy to determine that YpsB localizes to the divisome. Similarly to that for DivIVA, the recruitment of YpsB to the divisome requires late division proteins and occurs significantly after Z-ring formation. In contrast to DivIVA, however, YpsB is not retained at the newly formed cell poles after septation. Deletion analysis suggests that the N terminus of YpsB is required to target the protein to the divisome. The high similarity between the N termini of YpsB and DivIVA suggests that the same region is involved in the targeting of DivIVA. YpsB is not essential for septum formation and does not appear to play a role in septum positioning. However, a ypsB deletion has a synthetic effect when combined with a mutation in the cell division gene ftsA. Thus, we conclude that YpsB is a novel B. subtilis cell division protein whose function has diverged from that of its paralog DivIVA.

ProteoLens: a visual analytic tool for multi-scale database-driven biological network data mining

Background

New systems biology studies require researchers to understand how interplay among myriads of biomolecular entities is orchestrated in order to achieve high-level cellular and physiological functions. Many software tools have been developed in the past decade to help researchers visually navigate large networks of biomolecular interactions with built-in template-based query capabilities. To further advance researchers' ability to interrogate global physiological states of cells through multi-scale visual network explorations, new visualization software tools still need to be developed to empower the analysis. A robust visual data analysis platform driven by database management systems to perform bi-directional data processing-to-visualizations with declarative querying capabilities is needed.

Results

We developed ProteoLens as a JAVA-based visual analytic software tool for creating, annotating and exploring multi-scale biological networks. It supports direct database connectivity to either Oracle or PostgreSQL database tables/views, on which SQL statements using both Data Definition Languages (DDL) and Data Manipulation languages (DML) may be specified. The robust query languages embedded directly within the visualization software help users to bring their network data into a visualization context for annotation and exploration. ProteoLens supports graph/network represented data in standard Graph Modeling Language (GML) formats, and this enables interoperation with a wide range of other visual layout tools. The architectural design of ProteoLens enables the de-coupling of complex network data visualization tasks into two distinct phases: 1) creating network data association rules, which are mapping rules between network node IDs or edge IDs and data attributes such as functional annotations, expression levels, scores, synonyms, descriptions etc; 2) applying network data association rules to build the network and perform the visual annotation of graph nodes and edges according to associated data values. We demonstrated the advantages of these new capabilities through three biological network visualization case studies: human disease association network, drug-target interaction network and protein-peptide mapping network.

Conclusion

The architectural design of ProteoLens makes it suitable for bioinformatics expert data analysts who are experienced with relational database management to perform large-scale integrated network visual explorations. ProteoLens is a promising visual analytic platform that will facilitate knowledge discoveries in future network and systems biology studies.

Uncharacterized/hypothetical proteins in biomedical 'omics' experiments: is novelty being swept under the carpet?

Many 'omics' studies, gene expression microarray experiments in particular, aim at charting the molecular mechanisms of physiology, disease and drug response. This short review discusses the bias present in many such studies whereas the focus is set on the well understood and established molecular scenarios. The under-reporting rate of 'hypothetical' or uncharacterized genes and proteins, differentially regulated in disease context, is assessed here. Reasons for this bias are discussed. Particular examples from the genomics studies on respiratory diseases are presented. This review aims at increasing awareness of the unexplored genomics data and proposes remedies in order to refocus genomics studies on the less-charted territories of the genome, transcriptome and proteome. It is suggested that routine use of function prediction methods in conjunction with omics analyses may allow better interpretation of the data, and facilitate discovery of true novelty.

A computational analysis of protein-protein interaction networks in neurodegenerative diseases

BACKGROUND

Recent developments have meant that network theory is making an important contribution to the topological study of biological networks, such as protein-protein interaction (PPI) networks. The identification of differentially expressed genes in DNA array experiments is a source of information regarding the molecular pathways involved in disease. Thus, considering PPI analysis and gene expression studies together may provide a better understanding of multifactorial neurodegenerative diseases such as Multiple Sclerosis (MS) and Alzheimer disease (AD). The aim of this study was to assess whether the parameters of degree and betweenness, two fundamental measures in network theory, are properties that differentiate between implicated (seed-proteins) and non-implicated nodes (neighbors) in MS and AD. We used experimentally validated PPI information to obtain the neighbors for each seed group and we studied these parameters in four networks: MS-blood network; MS-brain network; AD-blood network; and AD-brain network.

RESULTS

Specific features of seed-proteins were revealed, whereby they displayed a lower average degree in both diseases and tissues, and a higher betweenness in AD-brain and MS-blood networks. Additionally, the heterogeneity of the processes involved indicate that these findings are not pathway specific but rather that they are spread over different pathways.

CONCLUSION

Our findings show differential centrality properties of proteins whose gene expression is impaired in neurodegenerative diseases.

Flexible nets: disorder and induced fit in the associations of p53 and 14-3-3 with their partners

Background

Proteins are involved in many interactions with other proteins leading to networks that regulate and control a wide variety of physiological processes. Some of these proteins, called hub proteins or hubs, bind to many different protein partners. Protein intrinsic disorder, via diversity arising from structural plasticity or flexibility, provide a means for hubs to associate with many partners (Dunker AK, Cortese MS, Romero P, Iakoucheva LM, Uversky VN: Flexible Nets: The roles of intrinsic disorder in protein interaction networks. FEBS J 2005, 272:5129-5148).

Results

Here we present a detailed examination of two divergent examples: 1) p53, which uses different disordered regions to bind to different partners and which also has several individual disordered regions that each bind to multiple partners, and 2) 14-3-3, which is a structured protein that associates with many different intrinsically disordered partners. For both examples, three-dimensional structures of multiple complexes reveal that the flexibility and plasticity of intrinsically disordered protein regions as well as induced-fit changes in the structured regions are both important for binding diversity.

Conclusions

These data support the conjecture that hub proteins often utilize intrinsic disorder to bind to multiple partners and provide detailed information about induced fit in structured regions.

GPS 2.0, a tool to predict kinase-specific phosphorylation sites in hierarchy

Identification of protein phosphorylation sites with their cognate protein kinases (PKs) is a key step to delineate molecular dynamics and plasticity underlying a variety of cellular processes. Although nearly 10 kinase-specific prediction programs have been developed, numerous PKs have been casually classified into subgroups without a standard rule. For large scale predictions, the false positive rate has also never been addressed. In this work, we adopted a well established rule to classify PKs into a hierarchical structure with four levels, including group, family, subfamily, and single PK. In addition, we developed a simple approach to estimate the theoretically maximal false positive rates. The on-line service and local packages of the GPS (Group-based Prediction System) 2.0 were implemented in Java with the modified version of the Group-based Phosphorylation Scoring algorithm. As the first stand alone software for predicting phosphorylation, GPS 2.0 can predict kinase-specific phosphorylation sites for 408 human PKs in hierarchy. A large scale prediction of more than 13,000 mammalian phosphorylation sites by GPS 2.0 was exhibited with great performance and remarkable accuracy. Using Aurora-B as an example, we also conducted a proteome-wide search and provided systematic prediction of Aurora-B-specific substrates including protein-protein interaction information. Thus, the GPS 2.0 is a useful tool for predicting protein phosphorylation sites and their cognate kinases and is freely available on line.

Conserved co-expression for candidate disease gene prioritization

Background

Genes that are co-expressed tend to be involved in the same biological process. However, co-expression is not a very reliable predictor of functional links between genes. The evolutionary conservation of co-expression between species can be used to predict protein function more reliably than co-expression in a single species. Here we examine whether co-expression across multiple species is also a better prioritizer of disease genes than is co-expression between human genes alone.

Results

We use co-expression data from yeast (S. cerevisiae), nematode worm (C. elegans), fruit fly (D. melanogaster), mouse and human and find that the use of evolutionary conservation can indeed improve the predictive value of co-expression. The effect that genes causing the same disease have higher co-expression than do other genes from their associated disease loci, is significantly enhanced when co-expression data are combined across evolutionarily distant species. We also find that performance can vary significantly depending on the co-expression datasets used, and just using more data does not necessarily lead to better prioritization. Instead, we find that dataset quality is more important than quantity, and using a consistent microarray platform per species leads to better performance than using more inclusive datasets pooled from various platforms.

Conclusion

We find that evolutionarily conserved gene co-expression prioritizes disease candidate genes better than human gene co-expression alone, and provide the integrated data as a new resource for disease gene prioritization tools.

Explorative data analysis of MCL reveals gene expression networks implicated in survival and prognosis supported by explorative CGH analysis

Background

Mantle cell lymphoma (MCL) is an incurable B cell lymphoma and accounts for 6% of all non-Hodgkin's lymphomas. On the genetic level, MCL is characterized by the hallmark translocation t(11;14) that is present in most cases with few exceptions. Both gene expression and comparative genomic hybridization (CGH) data vary considerably between patients with implications for their prognosis.

Methods

We compare patients over and below the median of survival. Exploratory principal component analysis of gene expression data showed that the second principal component correlates well with patient survival. Explorative analysis of CGH data shows the same correlation.

Results

On chromosome 7 and 9 specific genes and bands are delineated which improve prognosis prediction independent of the previously described proliferation signature. We identify a compact survival predictor of seven genes for MCL patients. After extensive re-annotation using GEPAT, we established protein networks correlating with prognosis. Well known genes (CDC2, CCND1) and further proliferation markers (WEE1, CDC25, aurora kinases, BUB1, PCNA, E2F1) form a tight interaction network, but also non-proliferative genes (SOCS1, TUBA1B CEBPB) are shown to be associated with prognosis. Furthermore we show that aggressive MCL implicates a gene network shift to higher expressed genes in late cell cycle states and refine the set of non-proliferative genes implicated with bad prognosis in MCL.

Conclusion

The results from explorative data analysis of gene expression and CGH data are complementary to each other. Including further tests such as Wilcoxon rank test we point both to proliferative and non-proliferative gene networks implicated in inferior prognosis of MCL and identify suitable markers both in gene expression and CGH data.

IDMap: facilitating the detection of potential leads with therapeutic targets

Pharmaceutical industry has been striving to reduce the costs of drug development and increase productivity. Among the many different attempts, drug repositioning (retargeting existing drugs) comes into the spotlight because of its financial efficiency. We introduce IDMap which predicts novel relationships between targets and chemicals and thus is capable of repositioning the marketed drugs by using text mining and chemical structure information. Also capable of mapping commercial chemicals to possible drug targets and vice versa, IDMap creates convenient environments for identifying the potential lead and its targets, especially in the field of drug repositioning.

AVAILABILITY

IDMap executable and its user manual including color images are freely available to non-commercial users at http://www.equispharm.com/idmap

Improving protein function prediction methods with integrated literature data

Background

Determining the function of uncharacterized proteins is a major challenge in the post-genomic era due to the problem's complexity and scale. Identifying a protein's function contributes to an understanding of its role in the involved pathways, its suitability as a drug target, and its potential for protein modifications. Several graph-theoretic approaches predict unidentified functions of proteins by using the functional annotations of better-characterized proteins in protein-protein interaction networks. We systematically consider the use of literature co-occurrence data, introduce a new method for quantifying the reliability of co-occurrence and test how performance differs across species. We also quantify changes in performance as the prediction algorithms annotate with increased specificity.

Results

We find that including information on the co-occurrence of proteins within an abstract greatly boosts performance in the Functional Flow graph-theoretic function prediction algorithm in yeast, fly and worm. This increase in performance is not simply due to the presence of additional edges since supplementing protein-protein interactions with co-occurrence data outperforms supplementing with a comparably-sized genetic interaction dataset. Through the combination of protein-protein interactions and co-occurrence data, the neighborhood around unknown proteins is quickly connected to well-characterized nodes which global prediction algorithms can exploit. Our method for quantifying co-occurrence reliability shows superior performance to the other methods, particularly at threshold values around 10% which yield the best trade off between coverage and accuracy. In contrast, the traditional way of asserting co-occurrence when at least one abstract mentions both proteins proves to be the worst method for generating co-occurrence data, introducing too many false positives. Annotating the functions with greater specificity is harder, but co-occurrence data still proves beneficial.

Conclusion

Co-occurrence data is a valuable supplemental source for graph-theoretic function prediction algorithms. A rapidly growing literature corpus ensures that co-occurrence data is a readily-available resource for nearly every studied organism, particularly those with small protein interaction databases. Though arguably biased toward known genes, co-occurrence data provides critical additional links to well-studied regions in the interaction network that graph-theoretic function prediction algorithms can exploit.

Protein networking: insights into global functional organization of proteomes

The formulation of network models from global protein studies is essential to understand the functioning of organisms. Network models of the proteome enable the application of Complex Network Analysis, a quantitative framework to investigate large complex networks using techniques from graph theory, statistical physics, dynamical systems and other fields. This approach has provided many insights into the functional organization of the proteome so far and will likely continue to do so. Currently, several network concepts have emerged in the field of proteomics. It is important to highlight the differences between these concepts, since different representations allow different insights into functional organization. One such concept is the protein interaction network, which contains proteins as nodes and undirected edges representing the occurrence of binding in large-scale protein-protein interaction studies. A second concept is the protein-signaling network, in which the nodes correspond to levels of post-translationally modified forms of proteins and directed edges to causal effects through post-translational modification, such as phosphorylation. Several other network concepts were introduced for proteomics. Although all formulated as networks, the concepts represent widely different physical systems. Therefore caution should be taken when applying relevant topological analysis. We review recent literature formulating and analyzing such networks.

Genomic context analysis in Archaea suggests previously unrecognized links between DNA replication and translation

Specific functional interactions of proteins involved in DNA replication and/or DNA repair or transcription might occur in Archaea, suggesting a previously unrecognized regulatory network coupling DNA replication and translation, which might also exist in Eukarya.

Background

Comparative analysis of genomes is valuable to explore evolution of genomes, deduce gene functions, or predict functional linking between proteins. Here, we have systematically analyzed the genomic environment of all known DNA replication genes in 27 archaeal genomes to infer new connections for DNA replication proteins from conserved genomic associations.

Results

Two distinct sets of DNA replication genes frequently co-localize in archaeal genomes: the first includes the genes for PCNA, the small subunit of the DNA primase (PriS), and Gins15; the second comprises the genes for MCM and Gins23. Other genomic associations of genes encoding proteins involved in informational processes that may be functionally relevant at the cellular level have also been noted; in particular, the association between the genes for PCNA, transcription factor S, and NudF. Surprisingly, a conserved cluster of genes coding for proteins involved in translation or ribosome biogenesis (S27E, L44E, aIF-2 alpha, Nop10) is almost systematically contiguous to the group of genes coding for PCNA, PriS, and Gins15. The functional relevance of this cluster encoding proteins conserved in Archaea and Eukarya is strongly supported by statistical analysis. Interestingly, the gene encoding the S27E protein, also known as metallopanstimulin 1 (MPS-1) in human, is overexpressed in multiple cancer cell lines.

Conclusion

Our genome context analysis suggests specific functional interactions for proteins involved in DNA replication between each other or with proteins involved in DNA repair or transcription. Furthermore, it suggests a previously unrecognized regulatory network coupling DNA replication and translation in Archaea that may also exist in Eukarya.

The airborne metagenome in an indoor urban environment

The indoor atmosphere is an ecological unit that impacts on public health. To investigate the composition of organisms in this space, we applied culture-independent approaches to microbes harvested from the air of two densely populated urban buildings, from which we analyzed 80 megabases genomic DNA sequence and 6000 16S rDNA clones. The air microbiota is primarily bacteria, including potential opportunistic pathogens commonly isolated from human-inhabited environments such as hospitals, but none of the data contain matches to virulent pathogens or bioterror agents. Comparison of air samples with each other and nearby environments suggested that the indoor air microbes are not random transients from surrounding outdoor environments, but rather originate from indoor niches. Sequence annotation by gene function revealed specific adaptive capabilities enriched in the air environment, including genes potentially involved in resistance to desiccation and oxidative damage. This baseline index of air microbiota will be valuable for improving designs of surveillance for natural or man-made release of virulent pathogens.

Computational prediction of domain interactions

Conserved domains carry many of the functional features found in the proteins of an organism. This includes not only catalytic activity, substrate binding, and structural features but also molecular adapters, which mediate the physical interactions between proteins or proteins with other molecules. In addition, two conserved domains can be linked not by physical contact but by a common function like forming a binding pocket. Although a wealth of experimental data has been collected and carefully curated for protein-protein interactions, as of today little useful data is available from major databases with respect to relations on the domain level. This lack of data makes computational prediction of domain-domain interactions a very important endeavor. In this chapter, we discuss the available experimental data (iPfam) and describe some important approaches to the problem of identifying interacting and/or functionally linked domain pairs from different kinds of input data. Specifically, we will discuss phylogenetic profiling on the level of conserved protein domains on one hand and inference of domain-interactions from observed or predicted protein-protein interactions datasets on the other. We explore the predictive power of these predictions and point out the importance of deploying as many different methods as possible for the best results.

eQED: an efficient method for interpreting eQTL associations using protein networks

Analysis of expression quantitative trait loci (eQTLs) is an emerging technique in which individuals are genotyped across a panel of genetic markers and, simultaneously, phenotyped using DNA microarrays. Because of the spacing of markers and linkage disequilibrium, each marker may be near many genes making it difficult to finely map which of these genes are the causal factors responsible for the observed changes in the downstream expression. To address this challenge, we present an efficient method for prioritizing candidate genes at a locus. This approach, called ‘eQTL electrical diagrams' (eQED), integrates eQTLs with protein interaction networks by modeling the two data sets as a wiring diagram of current sources and resistors. eQED achieved a 79% accuracy in recovering a reference set of regulator–target pairs in yeast, which is significantly higher than the performance of three competing methods. eQED also annotates 368 protein–protein interactions with their directionality of information flow with an accuracy of approximately 75%.

Novel metrics for evaluating the functional coherence of protein groups via protein semantic network

Metrics are presented for assessing overall functional coherence of a group of proteins based on the associated biomedical literature.

We present the metrics for assessing overall functional coherence of a group of proteins based on associated biomedical literature. A probabilistic topic model is applied to extract biologic concepts from a corpus of protein-related biomedical literature. Bipartite protein semantic networks are constructed, so that the functional coherence of a protein group can be evaluated with metrics that measure the closeness and strength of connectivity of the proteins in the network.

Reliability and applications of statistical methods based on oligonucleotide frequencies in bacterial and archaeal genomes

Background

The increasing number of sequenced prokaryotic genomes contains a wealth of genomic data that needs to be effectively analysed. A set of statistical tools exists for such analysis, but their strengths and weaknesses have not been fully explored. The statistical methods we are concerned with here are mainly used to examine similarities between archaeal and bacterial DNA from different genomes. These methods compare observed genomic frequencies of fixed-sized oligonucleotides with expected values, which can be determined by genomic nucleotide content, smaller oligonucleotide frequencies, or be based on specific statistical distributions. Advantages with these statistical methods include measurements of phylogenetic relationship with relatively small pieces of DNA sampled from almost anywhere within genomes, detection of foreign/conserved DNA, and homology searches. Our aim was to explore the reliability and best suited applications for some popular methods, which include relative oligonucleotide frequencies (ROF), di- to hexanucleotide zero'th order Markov methods (ZOM) and 2.order Markov chain Method (MCM). Tests were performed on distant homology searches with large DNA sequences, detection of foreign/conserved DNA, and plasmid-host similarity comparisons. Additionally, the reliability of the methods was tested by comparing both real and random genomic DNA.

Results

Our findings show that the optimal method is context dependent. ROFs were best suited for distant homology searches, whilst the hexanucleotide ZOM and MCM measures were more reliable measures in terms of phylogeny. The dinucleotide ZOM method produced high correlation values when used to compare real genomes to an artificially constructed random genome with similar %GC, and should therefore be used with care. The tetranucleotide ZOM measure was a good measure to detect horizontally transferred regions, and when used to compare the phylogenetic relationships between plasmids and hosts, significant correlation (R² = 0.4) was found with genomic GC content and intra-chromosomal homogeneity.

Conclusion

The statistical methods examined are fast, easy to implement, and powerful for a number of different applications involving genomic sequence comparisons. However, none of the measures examined were superior in all tests, and therefore the choice of the statistical method should depend on the task at hand.

Identification and characterization of the 2-phospho-L-lactate guanylyltransferase involved in coenzyme F420 biosynthesis

Coenzyme F 420 is a hydride carrier cofactor functioning in methanogenesis. One step in the biosynthesis of coenzyme F 420 involves the coupling of 2-phospho- l-lactate (LP) to 7,8-didemethyl-8-hydroxy-5-deazaflavin, the F 420 chromophore. This condensation requires an initial activation of 2-phospho- l-lactate through a pyrophosphate linkage to GMP. Bioinformatic analysis identified an uncharacterized archaeal protein in the Methanocaldococcus jannaschii genome, MJ0887, which could be involved in this transformation. The predicted MJ0887-derived protein has domain similarity with other known nucleotidyl transferases. The MJ0887 gene was cloned and overexpressed, and the purified protein was found to catalyze the formation of lactyl-2-diphospho-5'-guanosine from LP and GTP. Kinetic constants were determined for the MJ0887-derived protein with both LP and GTP substrates and are as follows: V max = 3 micromol min (-1) mg (-1), GTP K M (app) = 56 microM, and k cat/ K M (app) = 2 x 10 (4) M (-1) s (-1) and LP K M (app) = 36 microM, and k cat/ K M (app) = 4 x 10 (4) M (-1) s (-1). The MJ0887 gene product has been designated CofC to indicate its involvement in the third step of coenzyme F 420 biosynthesis.

A predictive model for transcriptional control of physiology in a free living cell

The environment significantly influences the dynamic expression and assembly of all components encoded in the genome of an organism into functional biological networks. We have constructed a model for this process in Halobacterium salinarum NRC-1 through the data-driven discovery of regulatory and functional interrelationships among approximately 80% of its genes and key abiotic factors in its hypersaline environment. Using relative changes in 72 transcription factors and 9 environmental factors (EFs) this model accurately predicts dynamic transcriptional responses of all these genes in 147 newly collected experiments representing completely novel genetic backgrounds and environments-suggesting a remarkable degree of network completeness. Using this model we have constructed and tested hypotheses critical to this organism's interaction with its changing hypersaline environment. This study supports the claim that the high degree of connectivity within biological and EF networks will enable the construction of similar models for any organism from relatively modest numbers of experiments.

New paradigm in protein function prediction for large scale omics analysis

Biological interpretation of large scale omics data, such as protein-protein interaction data and microarray gene expression data, requires that the function of many genes in a data set is annotated or predicted. Here the predicted function for a gene does not necessarily have to be a detailed biochemical function; a broad class of function, or low-resolution function, may be sufficient to understand why a set of genes shows the observed expression pattern or interaction pattern. In this Highlight, we focus on two recent approaches for function prediction which aim to provide large coverage in function prediction, namely omics data driven approaches and a thorough data mining approach on homology search results.

Extracting interactions between proteins from the literature

During the last decade, biomedicine has witnessed a tremendous development. Large amounts of experimental and computational biomedical data have been generated along with new discoveries, which are accompanied by an exponential increase in the number of biomedical publications describing these discoveries. In the meantime, there has been a great interest with scientific communities in text mining tools to find knowledge such as protein-protein interactions, which is most relevant and useful for specific analysis tasks. This paper provides a outline of the various information extraction methods in biomedical domain, especially for discovery of protein-protein interactions. It surveys methodologies involved in plain texts analyzing and processing, categorizes current work in biomedical information extraction, and provides examples of these methods. Challenges in the field are also presented and possible solutions are discussed.

STITCH: interaction networks of chemicals and proteins

The knowledge about interactions between proteins and small molecules is essential for the understanding of molecular and cellular functions. However, information on such interactions is widely dispersed across numerous databases and the literature. To facilitate access to this data, STITCH (‘search tool for interactions of chemicals’) integrates information about interactions from metabolic pathways, crystal structures, binding experiments and drug–target relationships. Inferred information from phenotypic effects, text mining and chemical structure similarity is used to predict relations between chemicals. STITCH further allows exploring the network of chemical relations, also in the context of associated binding proteins. Each proposed interaction can be traced back to the original data sources. Our database contains interaction information for over 68 000 different chemicals, including 2200 drugs, and connects them to 1.5 million genes across 373 genomes and their interactions contained in the STRING database. STITCH is available at http://stitch.embl.de/

Germinal center B cell-like (GCB) and activated B cell-like (ABC) type of diffuse large B cell lymphoma (DLBCL): analysis of molecular predictors, signatures, cell cycle state and patient survival

Aiming to find key genes and events, we analyze a large data set on diffuse large B-cell lymphoma (DLBCL) gene-expression (248 patients, 12196 spots). Applying the loess normalization method on these raw data yields improved survival predictions, in particular for the clinical important group of patients with medium survival time. Furthermore, we identify a simplified prognosis predictor, which stratifies different risk groups similarly well as complex signatures. We identify specific, activated B cell-like (ABC) and germinal center B cell-like (GCB) distinguishing genes. These include early (e.g. CDKN3) and late (e.g. CDKN2C) cell cycle genes. Independently from previous classification by marker genes we confirm a clear binary class distinction between the ABC and GCB subgroups. An earlier suggested third entity is not supported. A key regulatory network, distinguishing marked over-expression in ABC from that in GCB, is built by: ASB13, BCL2, BCL6, BCL7A, CCND2, COL3A1, CTGF, FN1, FOXP1, IGHM, IRF4, LMO2, LRMP, MAPK10, MME, MYBL1, NEIL1 and SH3BP5. It predicts and supports the aggressive behaviour of the ABC subgroup. These results help to understand target interactions, improve subgroup diagnosis, risk prognosis as well as therapy in the ABC and GCB DLBCL subgroups.

Integrating information from existing databases for enhanced function annotation of genes, genomes and networks

Uncovering functional associations for genes and gene products remains one of the most significant challenges in biology. The classical approaches, such as homology detection, are mainly suited for predicting approximate molecular function of a protein and should be used in context with other methods. Several studies have emerged that employ knowledge-based procedures to extract functional data for genes from a variety of biological sources. However, data derived from a single biological resource often provides only a limited perspective on their functional associations largely due to systematic bias in the underlying data. The post-genomic era has witnessed the emergence of knowledge-based studies that aim to decipher functional associations by combining several biological evidence types. These are expected to provide better insights into the functional aspects of diverse genes, genomes and networks.

Genomic signatures of breast cancer metastasis

Despite significant advances in the treatment of primary cancer, the ability to predict the metastatic behavior of a patient's cancer, as well as to detect and eradicate such recurrences, remain major clinical challenges in oncology. While many potential molecular biomarkers have been identified and tested previously, none have greatly improved the accuracy of specimen evaluation over routine histopathological criteria and they predict individual outcomes poorly. However, the recent introduction of high-throughput microarray technology has opened new avenues in genomic investigation of cancer, and through application in tissue-based studies and appropriate animal models, has facilitated the identification of gene expression signatures that are associated with the lethal progression of breast cancer. The use of these approaches has the potential to greatly impact our knowledge of tumor biology, to provide efficient biomarkers, and enable development towards customized prognostication and therapies for the individual.

An assessment of the uses of homologous interactions

MOTIVATION

Protein-protein interactions have proved to be a valuable starting point for understanding the inner workings of the cell. Computational methodologies have been built which both predict interactions and use interaction datasets in order to predict other protein features. Such methods require gold standard positive (GSP) and negative (GSN) interaction sets. Here we examine and demonstrate the usefulness of homologous interactions in predicting good quality positive and negative interaction datasets.

RESULTS

We generate GSP interaction sets as subsets from experimental data using only interaction and sequence information. We can therefore produce sets for several species (many of which at present have no identified GSPs). Comprehensive error rate testing demonstrates the power of the method. We also show how the use of our datasets significantly improves the predictive power of algorithms for interaction prediction and function prediction. Furthermore, we generate GSN interaction sets for yeast and examine the use of homology along with other protein properties such as localization, expression and function. Using a novel method to assess the accuracy of a negative interaction set, we find that the best single selector for negative interactions is a lack of co-function. However, an integrated method using all the characteristics shows significant improvement over any current method for identifying GSN interactions. The nature of homologous interactions is also examined and we demonstrate that interologs are found more commonly within species than across species.

CONCLUSION

GSP sets built using our homologous verification method are demonstrably better than standard sets in terms of predictive ability. We can build such GSP sets for several species. When generating GSNs we show a combination of protein features and lack of homologous interactions gives the highest quality interaction sets.

AVAILABILITY

GSP and GSN datasets for all the studied species can be downloaded from http://www.stats.ox.ac.uk/~deane/HPIV.

Insight into the haem d1 biosynthesis pathway in heliobacteria through bioinformatics analysis

Haem d(1) is a unique tetrapyrrole molecule that serves as a prosthetic group of cytochrome cd(1), which reduces nitrite to nitric oxide during the process of denitrification. Very little information is available regarding the biosynthesis of haem d(1). The extreme difficulty in studying the haem d(1) biosynthetic pathway can be partly attributed to the lack of a theoretical basis for experimental investigation. We report here a gene cluster encoding enzymes involved in the biosynthesis of haem d(1) in two heliobacterial species, Heliobacillus mobilis and Heliophilum fasciatum. The gene organization of the cluster is conserved between the two species, and contains a complete set of genes that lead to the biosynthesis of uroporphyrinogen III and genes thought to be involved in the late steps of haem d(1) biosynthesis. Detailed bioinformatics analysis of some of the proteins encoded in the gene cluster revealed important clues to the precise biochemical roles of the proteins in the biosynthesis of haem d(1), as well as the membrane transport and insertion of haem d(1) into an apocytochrome during the maturation of cytochrome cd(1).

The Firegoose: two-way integration of diverse data from different bioinformatics web resources with desktop applications

BACKGROUND

Information resources on the World Wide Web play an indispensable role in modern biology. But integrating data from multiple sources is often encumbered by the need to reformat data files, convert between naming systems, or perform ongoing maintenance of local copies of public databases. Opportunities for new ways of combining and re-using data are arising as a result of the increasing use of web protocols to transmit structured data.

RESULTS

The Firegoose, an extension to the Mozilla Firefox web browser, enables data transfer between web sites and desktop tools. As a component of the Gaggle integration framework, Firegoose can also exchange data with Cytoscape, the R statistical package, Multiexperiment Viewer (MeV), and several other popular desktop software tools. Firegoose adds the capability to easily use local data to query KEGG, EMBL STRING, DAVID, and other widely-used bioinformatics web sites. Query results from these web sites can be transferred to desktop tools for further analysis with a few clicks. Firegoose acquires data from the web by screen scraping, microformats, embedded XML, or web services. We define a microformat, which allows structured information compatible with the Gaggle to be embedded in HTML documents. We demonstrate the capabilities of this software by performing an analysis of the genes activated in the microbe Halobacterium salinarum NRC-1 in response to anaerobic environments. Starting with microarray data, we explore functions of differentially expressed genes by combining data from several public web resources and construct an integrated view of the cellular processes involved.

CONCLUSION

The Firegoose incorporates Mozilla Firefox into the Gaggle environment and enables interactive sharing of data between diverse web resources and desktop software tools without maintaining local copies. Additional web sites can be incorporated easily into the framework using the scripting platform of the Firefox browser. Performing data integration in the browser allows the excellent search and navigation capabilities of the browser to be used in combination with powerful desktop tools.

InPrePPI: an integrated evaluation method based on genomic context for predicting protein-protein interactions in prokaryotic genomes

Background

Although many genomic features have been used in the prediction of protein-protein interactions (PPIs), frequently only one is used in a computational method. After realizing the limited power in the prediction using only one genomic feature, investigators are now moving toward integration. So far, there have been few integration studies for PPI prediction; one failed to yield appreciable improvement of prediction and the others did not conduct performance comparison. It remains unclear whether an integration of multiple genomic features can improve the PPI prediction and, if it can, how to integrate these features.

Results

In this study, we first performed a systematic evaluation on the PPI prediction in Escherichia coli (E. coli) by four genomic context based methods: the phylogenetic profile method, the gene cluster method, the gene fusion method, and the gene neighbor method. The number of predicted PPIs and the average degree in the predicted PPI networks varied greatly among the four methods. Further, no method outperformed the others when we tested using three well-defined positive datasets from the KEGG, EcoCyc, and DIP databases. Based on these comparisons, we developed a novel integrated method, named InPrePPI. InPrePPI first normalizes the AC value (an integrated value of the accuracy and coverage) of each method using three positive datasets, then calculates a weight for each method, and finally uses the weight to calculate an integrated score for each protein pair predicted by the four genomic context based methods. We demonstrate that InPrePPI outperforms each of the four individual methods and, in general, the other two existing integrated methods: the joint observation method and the integrated prediction method in STRING. These four methods and InPrePPI are implemented in a user-friendly web interface.

Conclusion

This study evaluated the PPI prediction by four genomic context based methods, and presents an integrated evaluation method that shows better performance in E. coli.