SIDECACHE: Information access, management and dissemination framework for web services

Doderer, Mark; Burkhardt, Cory; Robbins, Kay A.

doi:10.1186/1756-0500-4-182

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…The web interface was developed in Java using the SideCache [12] framework, supported by a publically available JavaScript graphics library (http://www.walterzorn.de/) for graphic and image rendering. CMB website is deployed in an Apache Tomcat web server (http://tomcat.apache.org/), and supported by a MySQL database of methylation data (Figure S1 in File S1).…”

Section: Resultsmentioning

confidence: 99%

CMS: A Web-Based System for Visualization and Analysis of Genome-Wide Methylation Data of Human Cancers

Doderer²,

Huang

et al. 2013

PLoS ONE

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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/.

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Section: Resultsmentioning

confidence: 99%

CMS: A Web-Based System for Visualization and Analysis of Genome-Wide Methylation Data of Human Cancers

Doderer²,

Huang

et al. 2013

PLoS ONE

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“…Pathway Distiller was built using Adobe Flex and Action Script 3 which produces a Adobe Flash movie that runs in a user's web browser. The back-end automatic data download, processing and query availability was created using the SideCache [ 24 ] framework that is implemented in Java and designed to run as a Java Servlet in a container such as Apache Tomcat [ 25 ]. Users can upload a set of genes (resultant gene set) to access all components of the Pathway Distiller functions or use a supplied sample gene set.…”

Section: Methodsmentioning

confidence: 99%

Pathway Distiller - multisource biological pathway consolidation

et al. 2012

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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.

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