Abstract
BackgroundIdentification of marker genes associated with a specific tissue/cell type is a fundamental challenge in genetic and cell research. Marker genes are of great importance for determining cell identity, and for understanding tissue specific gene function and the molecular mechanisms underlying complex diseases.ResultsWe have developed a new bioinformatics tool called MGFM (Marker Gene Finder in Microarray data) to predict marker genes from microarray gene expression data. Marker genes are identified through the grouping of samples of the same type with similar marker gene expression levels. We verified our approach using two microarray data sets from the NCBI’s Gene Expression Omnibus public repository encompassing samples for similar sets of five human tissues (brain, heart, kidney, liver, and lung). Comparison with another tool for tissue-specific gene identification and validation with literature-derived established tissue markers established functionality, accuracy and simplicity of our tool. Furthermore, top ranked marker genes were experimentally validated by reverse transcriptase-polymerase chain reaction (RT-PCR). The sets of predicted marker genes associated with the five selected tissues comprised well-known genes of particular importance in these tissues. The tool is freely available from the Bioconductor web site, and it is also provided as an online application integrated into the CellFinder platform (http://cellfinder.org/analysis/marker).ConclusionsMGFM is a useful tool to predict tissue/cell type marker genes using microarray gene expression data. The implementation of the tool as an R-package as well as an application within CellFinder facilitates its use.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1785-9) contains supplementary material, which is available to authorized users.
Highlights
Identification of marker genes associated with a specific tissue/cell type is a fundamental challenge in genetic and cell research
The microarray data set is publicly available from Gene Expression Omnibus (GEO) with the series number GSE3526 [7]
Marker genes are identified when sample grouping of the same type exist with similar expression of the marker gene
Summary
Identification of marker genes associated with a specific tissue/cell type is a fundamental challenge in genetic and cell research. Marker genes are of great importance for determining cell identity, and for understanding tissue specific gene function and the molecular mechanisms underlying complex diseases. Large amounts of microarray experimental data are available in public repositories. A variety of programs have been developed to make use of these data, the number of tools that identify marker genes is limited. Since disease-associated genes are more likely to show tissue specific expression [2], marker genes of healthy tissues could be used to understand the molecular mechanisms underlying complex diseases. A large amount of microarray expression data has accumulated in public repositories. The aim of the current study was to develop a tool to detect marker genes associated
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