BackgroundA genomic catalogue of protein-protein interactions is a rich source of information, particularly for exploring the relationships between proteins. Numerous systems-wide and small-scale experiments have been conducted to identify interactions; however, our knowledge of all interactions for any one species is incomplete, and alternative means to expand these network maps is needed. We therefore took a comparative biology approach to predict protein-protein interactions across five species (human, mouse, fly, worm, and yeast) and developed InterologFinder for research biologists to easily navigate this data. We also developed a confidence score for interactions based on available experimental evidence and conservation across species.ResultsThe connectivity of the resultant networks was determined to have scale-free distribution, small-world properties, and increased local modularity, indicating that the added interactions do not disrupt our current understanding of protein network structures. We show examples of how these improved interactomes can be used to analyze a genome-scale dataset (RNAi screen) and to assign new function to proteins. Predicted interactions within this dataset were tested by co-immunoprecipitation, resulting in a high rate of validation, suggesting the high quality of networks produced.ConclusionsProtein-protein interactions were predicted in five species, based on orthology. An InteroScore, a score accounting for homology, number of orthologues with evidence of interactions, and number of unique observations of interactions, is given to each known and predicted interaction. Our website http://www.interologfinder.org provides research biologists intuitive access to this data.

Rapamycin (Rapa) and dietary restriction (DR) have consistently been shown to increase lifespan. To investigate whether Rapa and DR affect similar pathways in mice, we compared the effects of feeding mice ad libitum (AL), Rapa, DR, or a combination of Rapa and DR (Rapa + DR) on the transcriptome and metabolome of the liver. The principal component analysis shows that Rapa and DR are distinct groups. Over 2500 genes are significantly changed with either Rapa or DR when compared with mice fed AL; more than 80% are unique to DR or Rapa. A similar observation was made when genes were grouped into pathways; two-thirds of the pathways were uniquely changed by DR or Rapa. The metabolome shows an even greater difference between Rapa and DR; no metabolites in Rapa-treated mice were changed significantly from AL mice, whereas 173 metabolites were changed in the DR mice. Interestingly, the number of genes significantly changed by Rapa + DR when compared with AL is twice as large as the number of genes significantly altered by either DR or Rapa alone. In summary, the global effects of DR or Rapa on the liver are quite different and a combination of Rapa and DR results in alterations in a large number of genes and metabolites that are not significantly changed by either manipulation alone, suggesting that a combination of DR and Rapa would be more effective in extending longevity than either treatment alone.

BackgroundDNA methylation of promoter CpG islands is associated with gene suppression, and its unique genome-wide profiles have been linked to tumor progression. Coupled with high-throughput sequencing technologies, it can now efficiently determine genome-wide methylation profiles in cancer cells. Also, experimental and computational technologies make it possible to find the functional relationship between cancer-specific methylation patterns and their clinicopathological parameters.Methodology/Principal FindingsCancer methylome system (CMS) is a web-based database application designed for the visualization, comparison and statistical analysis of human cancer-specific DNA methylation. Methylation intensities were obtained from MBDCap-sequencing, pre-processed and stored in the database. 191 patient samples (169 tumor and 22 normal specimen) and 41 breast cancer cell-lines are deposited in the database, comprising about 6.6 billion uniquely mapped sequence reads. This provides comprehensive and genome-wide epigenetic portraits of human breast cancer and endometrial cancer to date. Two views are proposed for users to better understand methylation structure at the genomic level or systemic methylation alteration at the gene level. In addition, a variety of annotation tracks are provided to cover genomic information. CMS includes important analytic functions for interpretation of methylation data, such as the detection of differentially methylated regions, statistical calculation of global methylation intensities, multiple gene sets of biologically significant categories, interactivity with UCSC via custom-track data. We also present examples of discoveries utilizing the framework.Conclusions/SignificanceCMS provides visualization and analytic functions for cancer methylome datasets. A comprehensive collection of datasets, a variety of embedded analytic functions and extensive applications with biological and translational significance make this system powerful and unique in cancer methylation research. CMS is freely accessible at: http://cbbiweb.uthscsa.edu/KMethylomes/.

BackgroundOne method to understand and evaluate an experiment that produces a large set of genes, such as a gene expression microarray analysis, is to identify overrepresentation or enrichment for biological pathways. Because pathways are able to functionally describe the set of genes, much effort has been made to collect curated biological pathways into publicly accessible databases. When combining disparate databases, highly related or redundant pathways exist, making their consolidation into pathway concepts essential. This will facilitate unbiased, comprehensive yet streamlined analysis of experiments that result in large gene sets.MethodsAfter gene set enrichment finds representative pathways for large gene sets, pathways are consolidated into representative pathway concepts. Three complementary, but different methods of pathway consolidation are explored. Enrichment Consolidation combines the set of the pathways enriched for the signature gene list through iterative combining of enriched pathways with other pathways with similar signature gene sets; Weighted Consolidation utilizes a Protein-Protein Interaction network based gene-weighting approach that finds clusters of both enriched and non-enriched pathways limited to the experiments' resultant gene list; and finally the de novo Consolidation method uses several measurements of pathway similarity, that finds static pathway clusters independent of any given experiment.ResultsWe demonstrate that the three consolidation methods provide unified yet different functional insights of a resultant gene set derived from a genome-wide profiling experiment. Results from the methods are presented, demonstrating their applications in biological studies and comparing with a pathway web-based framework that also combines several pathway databases. Additionally a web-based consolidation framework that encompasses all three methods discussed in this paper, Pathway Distiller (http://cbbiweb.uthscsa.edu/PathwayDistiller), is established to allow researchers access to the methods and example microarray data described in this manuscript, and the ability to analyze their own gene list by using our unique consolidation methods.ConclusionsBy combining several pathway systems, implementing different, but complementary pathway consolidation methods, and providing a user-friendly web-accessible tool, we have enabled users the ability to extract functional explanations of their genome wide experiments.

Eukaryotic gene expression must be coordinated for the proper functioning of biological processes. This coordination can be achieved both at the transcriptional and post-transcriptional levels. In both cases, regulatory sequences placed at either promoter regions or on UTRs function as markers recognized by regulators that can then activate or repress different groups of genes according to necessity. While regulatory sequences involved in transcription are quite well documented, there is a lack of information on sequence elements involved in post-transcriptional regulation. We used a statistical over-representation method to identify novel regulatory elements located on UTRs. An exhaustive search approach was used to calculate the frequency of all possible n-mers (short nucleotide sequences) in 16,160 human genes of NCBI RefSeq sequences and to identify any peculiar usage of n-mers on UTRs. After a stringent filtering process, we identified 2,772 highly over-represented n-mers on 3' UTRs. We provide evidence that these n-mers are potentially involved in regulatory functions. Identified n-mers overlap with previously identified binding sites for HuR and TIA-1 and, ARE and GRE sequences. We determine also that n-mers overlap with predicted miRNA target sites. Finally, a method to cluster n-mer groups allowed the identification of putative gene networks.

BackgroundScientists striving to unlock mysteries within complex biological systems face myriad barriers in effectively integrating available information to enhance their understanding. While experimental techniques and available data sources are rapidly evolving, useful information is dispersed across a variety of sources, and sources of the same information often do not use the same format or nomenclature. To harness these expanding resources, scientists need tools that bridge nomenclature differences and allow them to integrate, organize, and evaluate the quality of information without extensive computation.ResultsSidekick, a genomic data driven analysis and decision making framework, is a web-based tool that provides a user-friendly intuitive solution to the problem of information inaccessibility. Sidekick enables scientists without training in computation and data management to pursue answers to research questions like "What are the mechanisms for disease X" or "Does the set of genes associated with disease X also influence other diseases." Sidekick enables the process of combining heterogeneous data, finding and maintaining the most up-to-date data, evaluating data sources, quantifying confidence in results based on evidence, and managing the multi-step research tasks needed to answer these questions. We demonstrate Sidekick's effectiveness by showing how to accomplish a complex published analysis in a fraction of the original time with no computational effort using Sidekick.ConclusionsSidekick is an easy-to-use web-based tool that organizes and facilitates complex genomic research, allowing scientists to explore genomic relationships and formulate hypotheses without computational effort. Possible analysis steps include gene list discovery, gene-pair list discovery, various enrichments for both types of lists, and convenient list manipulation. Further, Sidekick's ability to characterize pairs of genes offers new ways to approach genomic analysis that traditional single gene lists do not, particularly in areas such as interaction discovery.

BackgroundMany bioinformatics algorithms and data sets are deployed using web services so that the results can be explored via the Internet and easily integrated into other tools and services. These services often include data from other sites that is accessed either dynamically or through file downloads. Developers of these services face several problems because of the dynamic nature of the information from the upstream services. Many publicly available repositories of bioinformatics data frequently update their information. When such an update occurs, the developers of the downstream service may also need to update. For file downloads, this process is typically performed manually followed by web service restart. Requests for information obtained by dynamic access of upstream sources is sometimes subject to rate restrictions.FindingsSideCache provides a framework for deploying web services that integrate information extracted from other databases and from web sources that are periodically updated. This situation occurs frequently in biotechnology where new information is being continuously generated and the latest information is important. SideCache provides several types of services including proxy access and rate control, local caching, and automatic web service updating.ConclusionsWe have used the SideCache framework to automate the deployment and updating of a number of bioinformatics web services and tools that extract information from remote primary sources such as NCBI, NCIBI, and Ensembl. The SideCache framework also has been used to share research results through the use of a SideCache derived web service.

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