Detection of condition-specific marker genes from RNA-seq data with MGFR

The identification of condition-specific genes is key to advancing our understanding of cell fate decisions and disease development. Differential gene expression analysis (DGEA) has been the standard tool for this task. However, the amount of samples that modern transcriptomic technologies allow us to study, makes DGEA a daunting task. On the other hand, experiments with low numbers of replicates lack the statistical power to detect differentially expressed genes. We have previously developed MGFM, a tool for marker gene detection from microarrays, that is particularly useful in the latter case. Here, we have adapted the algorithm behind MGFM to detect markers in RNA-seq data. MGFR groups samples with similar gene expression levels and flags potential markers of a sample type if their highest expression values represent all replicates of this type. We have benchmarked MGFR against other methods and found that its proposed markers accurately characterize the functional identity of different tissues and cell types in standard and single cell RNA-seq datasets. Then, we performed a more detailed analysis for three of these datasets, which profile the transcriptomes of different human tissues, immune and human blastocyst cell types, respectively. MGFR’s predicted markers were compared to gold-standard lists for these datasets and outperformed the other marker detectors. Finally, we suggest novel candidate marker genes for the examined tissues and cell types. MGFR is implemented as a freely available Bioconductor package (https://doi.org/doi:10.18129/B9.bioc.MGFR), which facilitates its use and integration with bioinformatics pipelines.

Therapeutic Complement Targeting in ANCA-Associated Vasculitides and Thrombotic Microangiopathy

Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitides (AAVs) are a group of systemic autoimmune disorders characterized by necrotizing inflammation of medium-to-small vessels, a relative paucity of immune deposits, and an association with detectable circulating ANCAs. AAVs include granulomatosis with polyangiitis (renamed from Wegener's granulomatosis), microscopic polyangiitis, and eosinophilic granulomatosis with polyangiitis (Churg-Strauss syndrome). Until recently, AAVs have not been viewed as complement-mediated disorders. However, recent findings predominantly from animal studies demonstrated a crucial role of the complement system in the pathogenesis of AAVs. Complement activation or defects in its regulation have been described in an increasing number of acquired or genetically driven forms of thrombotic microangiopathy. Coinciding with this expanding spectrum of complement-mediated diseases, the question arises as to which AAV patients might benefit from a complement-targeted therapy. Therapies directed against the complement system point to the necessity of a genetic workup of genes of complement components and regulators in patients with AAV. Genetic testing together with pluripotent stem cells and bioinformatics tools may broaden our approach to the treatment of patients with aggressive forms of AAV.

MGFM: a novel tool for detection of tissue and cell specific marker genes from microarray gene expression data

Background

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.

Results

We 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).

Conclusions

MGFM 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 material

The online version of this article (doi:10.1186/s12864-015-1785-9) contains supplementary material, which is available to authorized users.