Project description:Agilent 44K whole genome studies were used to correlate gene expression values with mutations found in massively parallel sequencing experiments.
Project description:BackgroundThe ability to generate transcriptional data on the scale of entire genomes has been a boon both in the improvement of biological understanding and in the amount of data generated. The latter, the amount of data generated, has implications when it comes to effective storage, analysis and sharing of these data. A number of software tools have been developed to store, analyze, and share microarray data. However, a majority of these tools do not offer all of these features nor do they specifically target the commonly used two color Agilent DNA microarray platform. Thus, the motivating factor for the development of EDGE(3) was to incorporate the storage, analysis and sharing of microarray data in a manner that would provide a means for research groups to collaborate on Agilent-based microarray experiments without a large investment in software-related expenditures or extensive training of end-users.ResultsEDGE(3) has been developed with two major functions in mind. The first function is to provide a workflow process for the generation of microarray data by a research laboratory or a microarray facility. The second is to store, analyze, and share microarray data in a manner that doesn't require complicated software. To satisfy the first function, EDGE3 has been developed as a means to establish a well defined experimental workflow and information system for microarray generation. To satisfy the second function, the software application utilized as the user interface of EDGE(3) is a web browser. Within the web browser, a user is able to access the entire functionality, including, but not limited to, the ability to perform a number of bioinformatics based analyses, collaborate between research groups through a user-based security model, and access to the raw data files and quality control files generated by the software used to extract the signals from an array image.ConclusionHere, we present EDGE(3), an open-source, web-based application that allows for the storage, analysis, and controlled sharing of transcription-based microarray data generated on the Agilent DNA platform. In addition, EDGE(3) provides a means for managing RNA samples and arrays during the hybridization process. EDGE(3) is freely available for download at http://edge.oncology.wisc.edu/.
Project description:For decades, formalin-fixing and paraffin embedding (FFPE) has been routinely used to preserve tissue samples for histological analysis. Global gene expression analysis of these archival tissues has the potential to greatly advance research attempting to link perturbations in molecular pathways to disease outcome. We investigated 16-year-old FFPE mouse liver samples treated with phenobarbital and created a protocol for their analysis using Agilent gene expression arrays. Despite low quality RNA, archival phenobarbital samples exhibited strong induction of the positive control genes CYP2b9 and CYP2b10 by RT-PCR. We hybridized Universal Linkage System–labelled cDNA libraries to 8x60K Agilent gene expression arrays. We compared one- and two-colour microarray experiments and tested the effects of increasing the amount of cDNA loaded. Canonical gene responders to phenobarbital treatment were measurably induced under each experimental condition (however increasing cDNA input also increased the array background signal). Individual genes were validated by RT-PCR and literature searches, and pathway analysis demonstrated that 9/10 top canonical pathways were consistent across experiments. These analyses suggested that future experiments should be done in duplicate to identify and eliminate false positive genes. We conclude that FFPE samples can be used for meaningful and reproducible gene expression and pathway analyses using microarrays.
Project description:Gene expression microarrays have made a profound impact in biomedical research. The diversity of platforms and analytical methods has made comparison of data from multiple platforms very challenging. In this study, we describe a framework for comparisons across platforms and laboratories. We have attempted to include nearly all the available commercial and “in-house” platforms. Using probe sequences matched at the exon level improved consistency of measurements across the different microarray platforms compared to annotation-based matches. Generally, consistency was good for highly expressed genes, and variable for genes with lower expression values as confirmed by QRT-PCR. Concordance of measurements was higher between laboratories on the same platform than across platforms. We demonstrate that, after stringent pre-processing, commercial arrays were more consistent than “in-house” arrays, and by most measures, one-dye platforms were more consistent than two-dye platforms. Keywords: cross platform microarrays