Dataset Information

Array data extractor (ADE): a LabVIEW program to extract and merge gene array data.

ABSTRACT:

Background

Large data sets from gene expression array studies are publicly available offering information highly valuable for research across many disciplines ranging from fundamental to clinical research. Highly advanced bioinformatics tools have been made available to researchers, but a demand for user-friendly software allowing researchers to quickly extract expression information for multiple genes from multiple studies persists.

Findings

Here, we present a user-friendly LabVIEW program to automatically extract gene expression data for a list of genes from multiple normalized microarray datasets. Functionality was tested for 288 class A G protein-coupled receptors (GPCRs) and expression data from 12 studies comparing normal and diseased human hearts. Results confirmed known regulation of a beta 1 adrenergic receptor and further indicate novel research targets.

Conclusions

Although existing software allows for complex data analyses, the LabVIEW based program presented here, "Array Data Extractor (ADE)", provides users with a tool to retrieve meaningful information from multiple normalized gene expression datasets in a fast and easy way. Further, the graphical programming language used in LabVIEW allows applying changes to the program without the need of advanced programming knowledge.

SUBMITTER: Kurtenbach S

PROVIDER: S-EPMC4222097 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Similar Datasets

Project description:BACKGROUND: Microarrays have become a routine tool to address diverse biological questions. Therefore, different types and generations of microarrays have been produced by several manufacturers over time. Likewise, the diversity of raw data deposited in public databases such as NCBI GEO or EBI ArrayExpress has grown enormously.This has resulted in databases currently containing several hundred thousand microarray samples clustered by different species, manufacturers and chip generations. While one of the original goals of these databases was to make the data available to other researchers for independent analysis and, where appropriate, integration with their own data, current software implementations could not provide that feature.Only those data sets generated on the same chip platform can be readily combined and even here there are batch effects to be taken care of. A straightforward approach to deal with multiple chip types and batch effects has been missing.The software presented here was designed to solve both of these problems in a convenient and user friendly way. RESULTS: The virtualArray software package can combine raw data sets using almost any chip types based on current annotations from NCBI GEO or Bioconductor. After establishing congruent annotations for the raw data, virtualArray can then directly employ one of seven implemented methods to adjust for batch effects in the data resulting from differences between the chip types used. Both steps can be tuned to the preferences of the user. When the run is finished, the whole dataset is presented as a conventional Bioconductor "ExpressionSet" object, which can be used as input to other Bioconductor packages. CONCLUSIONS: Using this software package, researchers can easily integrate their own microarray data with data from public repositories or other sources that are based on different microarray chip types. Using the default approach a robust and up-to-date batch effect correction technique is applied to the data.