Detecting and visualizing gene fusions.

In recent years, gene fusions have gained significant recognition as biomarkers. They can assist treatment decisions, are seldom found in normal tissue and are detectable through Next-generation sequencing (NGS) of the transcriptome (RNA-seq). To transform the data provided by the sequencer into robust gene fusion detection several analysis steps are needed. Usually the first step is to map the sequenced transcript fragments (RNA-seq) to a reference genome. One standard application of this approach is to estimate expression and detect variants within known genes, e.g. SNPs and indels. In case of gene fusions, however, completely novel gene structures have to be detected. Here, we describe the detection of such gene fusion events based on our comprehensive transcript annotation (ElDorado). To demonstrate the utility of our approach, we extract gene fusion candidates from eight breast cancer cell lines, which we compare to experimentally verified gene fusions. We discuss several gene fusion events, like BCAS3-BCAS4 that was only detected in the breast cancer cell line MCF7. As supporting evidence we show that gene fusions occur more frequently in copy number enriched regions (CNV analysis). In addition, we present the Transcriptome Viewer (TViewer) a tool that allows to interactively visualize gene fusions. Finally, we support detected gene fusions through literature mining based annotations and network analyses. In conclusion, we present a platform that allows detecting gene fusions and supporting them through literature knowledge as well as rich visualization capabilities. This enables scientists to better understand molecular processes, biological functions and disease associations, which will ultimately lead to better biomedical knowledge for the development of biomarkers for diagnostics and therapies.

In silico prediction of scaffold/matrix attachment regions in large genomic sequences.

Scaffold/matrix attachment regions (S/MARs) are essential regulatory DNA elements of eukaryotic cells. They are major determinants of locus control of gene expression and can shield gene expression from position effects. Experimental detection of S/MARs requires substantial effort and is not suitable for large-scale screening of genomic sequences. In silico prediction of S/MARs can provide a crucial first selection step to reduce the number of candidates. We used experimentally defined S/MAR sequences as the training set and generated a library of new S/MAR-associated, AT-rich patterns described as weight matrices. A new tool called SMARTest was developed that identifies potential S/MARs by performing a density analysis based on the S/MAR matrix library (http://www.genomatix.de/cgi-bin/smartest_pd/smartest.pl). S/MAR predictions were evaluated by using six genomic sequences from animal and plant for which S/MARs and non-S/MARs were experimentally mapped. SMARTest reached a sensitivity of 38% and a specificity of 68%. In contrast to previous algorithms, the SMARTest approach does not depend on the sequence context and is suitable to analyze long genomic sequences up to the size of whole chromosomes. To demonstrate the feasibility of large-scale S/MAR prediction, we analyzed the recently published chromosome 22 sequence and found 1198 S/MAR candidates.