SEQADAPT: an adaptable system for the tracking, storage and analysis of high throughput sequencing experiments

<p>Abstract</p> <p>Background</p> <p>High throughput sequencing has become an increasingly important tool for biological research. However, the existing software systems for managing and processing these data have not provided the flexible infrastructure that research requires.</p> <p>Results</p> <p>Existing software solutions provide static and well-established algorithms in a restrictive package. However as high throughput sequencing is a rapidly evolving field, such static approaches lack the ability to readily adopt the latest advances and techniques which are often required by researchers. We have used a loosely coupled, service-oriented infrastructure to develop SeqAdapt. This system streamlines data management and allows for rapid integration of novel algorithms. Our approach also allows computational biologists to focus on developing and applying new methods instead of writing boilerplate infrastructure code.</p> <p>Conclusion</p> <p>The system is based around the Addama service architecture and is available at our website as a demonstration web application, an installable single download and as a collection of individual customizable services.</p

Thinking like a consumer: Linking aquatic basal metabolism and consumer dynamics

The increasing availability of high-frequency freshwater ecosystem metabolism data provides an opportunity to identify links between metabolic regimes, as gross primary production and ecosystem respiration patterns, and consumer energetics with the potential to improve our current understanding of consumer dynamics (e.g., population dynamics, community structure, trophic interactions). We describe a conceptual framework linking metabolic regimes of flowing waters with consumer community dynamics. We use this framework to identify three emerging research needs: (1) quantifying the linkage of metabolism and consumer production data via food web theory and carbon use efficiencies, (2) evaluating the roles of metabolic dynamics and other environmental regimes (e.g., hydrology, light) in consumer dynamics, and (3) determining the degree to which metabolic regimes influence the evolution of consumer traits and phenology. Addressing these needs will improve the understanding of consumer biomass and production patterns as metabolic regimes can be viewed as an emergent property of food webs

The urologic epithelial stem cell database (UESC) – a web tool for cell type-specific gene expression and immunohistochemistry images of the prostate and bladder

Background: Public databases are crucial for analysis of high-dimensional gene and protein expression data. The Urologic Epithelial Stem Cells (UESC) database http://scgap.systemsbiology.net/ is a public database that contains gene and protein information for the major cell types of the prostate, prostate cancer cell lines, and a cancer cell type isolated from a primary tumor. Similarly, such information is available for urinary bladder cell types. Description: Two major data types were archived in the database, protein abundance localization data from immunohistochemistry images, and transcript abundance data principally from DNA microarray analysis. Data results were organized in modules that were made to operate independently but built upon a core functionality. Gene array data and immunostaining images for human and mouse prostate and bladder were made available for interrogation. Data analysis capabilities include: (1) CD (cluster designation) cell surface protein data. For each cluster designation molecule, a data summary allows easy retrieval of images (at multiple magnifications). (2) Microarray data. Single gene or batch search can be initiated with Affymetrix Probeset ID, Gene Name, or Accession Number together with options of coalescing probesets and/or replicates. Conclusion: Databases are invaluable for biomedical research, and their utility depends on data quality and user friendliness. UESC provides for database queries and tools to examine cell typespecific gene expression (normal vs. cancer), whereas most other databases contain only whole tissue expression datasets. The UESC database provides a valuable tool in the analysis of differential gene expression in prostate cancer genes in cancer progression.This work was supported by grant 1U01 DK63630 from NIDDK. Additional funding came from grants CA85859, CA98699 and CA111244 from NCI

Adaptable data management for systems biology investigations

<p>Abstract</p> <p>Background</p> <p>Within research each experiment is different, the focus changes and the data is generated from a continually evolving barrage of technologies. There is a continual introduction of new techniques whose usage ranges from in-house protocols through to high-throughput instrumentation. To support these requirements data management systems are needed that can be rapidly built and readily adapted for new usage.</p> <p>Results</p> <p>The adaptable data management system discussed is designed to support the seamless mining and analysis of biological experiment data that is commonly used in systems biology (e.g. ChIP-chip, gene expression, proteomics, imaging, flow cytometry). We use different content graphs to represent different views upon the data. These views are designed for different roles: equipment specific views are used to gather instrumentation information; data processing oriented views are provided to enable the rapid development of analysis applications; and research project specific views are used to organize information for individual research experiments. This management system allows for both the rapid introduction of new types of information and the evolution of the knowledge it represents.</p> <p>Conclusion</p> <p>Data management is an important aspect of any research enterprise. It is the foundation on which most applications are built, and must be easily extended to serve new functionality for new scientific areas. We have found that adopting a three-tier architecture for data management, built around distributed standardized content repositories, allows us to rapidly develop new applications to support a diverse user community.</p

Gene expression relationship between prostate cancer cells of Gleason 3, 4 and normal epithelial cells as revealed by cell type-specific transcriptomes

Background: Prostate cancer cells in primary tumors have been typed CD10(-)/CD13(-)/CD24(hi)/CD26(+)/CD38(lo)/CD44(-)/CD104(-). This CD phenotype suggests a lineage relationship between cancer cells and luminal cells. The Gleason grade of tumors is a descriptive of tumor glandular differentiation. Higher Gleason scores are associated with treatment failure. Methods: CD26(+) cancer cells were isolated from Gleason 3+3 (G3) and Gleason 4+4 (G4) tumors by cell sorting, and their gene expression or transcriptome was determined by Affymetrix DNA array analysis. Dataset analysis was used to determine gene expression similarities and differences between G3 and G4 as well as to prostate cancer cell lines and histologically normal prostate luminal cells. Results: The G3 and G4 transcriptomes were compared to those of prostatic cell types of non-cancer, which included luminal, basal, stromal fibromuscular, and endothelial. A principal components analysis of the various transcriptome datasets indicated a closer relationship between luminal and G3 than luminal and G4. Dataset comparison also showed that the cancer transcriptomes differed substantially from those of prostate cancer cell lines. Conclusions: Genes differentially expressed in cancer are potential biomarkers for cancer detection, and those differentially expressed between G3 and G4 are potential biomarkers for disease stratification given that G4 cancer is associated with poor outcomes. Differentially expressed genes likely contribute to the prostate cancer phenotype and constitute the signatures of these particular cancer cell types.National Institutes of Health (NIH)[CA111244]National Institutes of Health (NIH)[CA98699]National Institutes of Health (NIH)[CA85859]National Institutes of Health (NIH)[DK63630][P50-GMO-76547

Differential Inductive Signaling of CD90+ Prostate Cancer-Associated Fibroblasts Compared to Normal Tissue Stromal Mesenchyme Cells

Prostate carcinomas are surrounded by a layer of stromal fibroblastic cells that are characterized by increased expression of CD90. These CD90+ cancer-associated stromal fibroblastic cells differ in gene expression from their normal counterpart, CD49a+CD90lo stromal smooth muscle cells; and were postulated to represent a less differentiated cell type with altered inductive properties. CD90+ stromal cells were isolated from tumor tissue specimens and co-cultured with the pluripotent embryonal carcinoma cell line NCCIT in order to elucidate the impact of tumor-associated stroma on stem cells, and the ‘cancer stem cell.’ Transcriptome analysis identified a notable decreased induction of smooth muscle and prostate stromal genes such as PENK, BMP2 and ChGn compared to previously determined NCCIT response to normal prostate stromal cell induction. CD90+ stromal cell secreted factors induced an increased expression of CD90 and differential induction of genes involved in extracellular matrix remodeling and the RECK pathway in NCCIT. These results suggest that, compared to normal tissue stromal cells, signaling from cancer-associated stromal cells has a markedly different effect on stem cells as represented by NCCIT. Given that stromal cells are important in directing organ-specific differentiation, stromal cells in tumors appear to be defective in this function, which may contribute to abnormal differentiation found in diseases such as cancer

LabKey Server: An open source platform for scientific data integration, analysis and collaboration

<p>Abstract</p> <p>Background</p> <p>Broad-based collaborations are becoming increasingly common among disease researchers. For example, the Global HIV Enterprise has united cross-disciplinary consortia to speed progress towards HIV vaccines through coordinated research across the boundaries of institutions, continents and specialties. New, end-to-end software tools for data and specimen management are necessary to achieve the ambitious goals of such alliances. These tools must enable researchers to organize and integrate heterogeneous data early in the discovery process, standardize processes, gain new insights into pooled data and collaborate securely.</p> <p>Results</p> <p>To meet these needs, we enhanced the LabKey Server platform, formerly known as CPAS. This freely available, open source software is maintained by professional engineers who use commercially proven practices for software development and maintenance. Recent enhancements support: (i) Submitting specimens requests across collaborating organizations (ii) Graphically defining new experimental data types, metadata and wizards for data collection (iii) Transitioning experimental results from a multiplicity of spreadsheets to custom tables in a shared database (iv) Securely organizing, integrating, analyzing, visualizing and sharing diverse data types, from clinical records to specimens to complex assays (v) Interacting dynamically with external data sources (vi) Tracking study participants and cohorts over time (vii) Developing custom interfaces using client libraries (viii) Authoring custom visualizations in a built-in R scripting environment.</p> <p>Diverse research organizations have adopted and adapted LabKey Server, including consortia within the Global HIV Enterprise. Atlas is an installation of LabKey Server that has been tailored to serve these consortia. It is in production use and demonstrates the core capabilities of LabKey Server. Atlas now has over 2,800 active user accounts originating from approximately 36 countries and 350 organizations. It tracks roughly 27,000 assay runs, 860,000 specimen vials and 1,300,000 vial transfers.</p> <p>Conclusions</p> <p>Sharing data, analysis tools and infrastructure can speed the efforts of large research consortia by enhancing efficiency and enabling new insights. The Atlas installation of LabKey Server demonstrates the utility of the LabKey platform for collaborative research. Stable, supported builds of LabKey Server are freely available for download at <url>http://www.labkey.org</url>. Documentation and source code are available under the Apache License 2.0.</p

Gene expression down-regulation in CD90+ prostate tumor-associated stromal cells involves potential organ-specific genes

<p>Abstract</p> <p>Background</p> <p>The prostate stroma is a key mediator of epithelial differentiation and development, and potentially plays a role in the initiation and progression of prostate cancer. The tumor-associated stroma is marked by increased expression of CD90/THY1. Isolation and characterization of these stromal cells could provide valuable insight into the biology of the tumor microenvironment.</p> <p>Methods</p> <p>Prostate CD90⁺stromal fibromuscular cells from tumor specimens were isolated by cell-sorting and analyzed by DNA microarray. Dataset analysis was used to compare gene expression between histologically normal and tumor-associated stromal cells. For comparison, stromal cells were also isolated and analyzed from the urinary bladder.</p> <p>Results</p> <p>The tumor-associated stromal cells were found to have decreased expression of genes involved in smooth muscle differentiation, and those detected in prostate but not bladder. Other differential expression between the stromal cell types included that of the CXC-chemokine genes.</p> <p>Conclusion</p> <p>CD90⁺prostate tumor-associated stromal cells differed from their normal counterpart in expression of multiple genes, some of which are potentially involved in organ development.</p