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For instance, on an Escherichia coli data set GIIRA showed up to 15% improved identification accuracy in comparison to other prediction methods.
The second main project builds on the output of GIIRA and post-processes gene prediction results in order to improve prediction accuracy.
In this thesis, we present new proteogenomic approaches for the integration of next-generation sequencing and mass spectrometry data in form of DNA and RNA-Seq and tandem mass spectra.
These contributions can be divided into three main projects: First, we developed the method GIIRA (Gene Identification Incorporating RNA-Seq data and Ambiguous reads) for the construction of gene models and transcript prediction based on RNA-Seq data.
Hence, their results are accurate for standard gene structures, but they are not sample-specific.