Microbial communities play an important role in the whole life of planet earth. The fields metagenomics and metatranscriptomics have developed to reveal the taxo-

nomic composition and functional diversity of heterogeneous microbial communities. With the development of new sequencing methods studies in those fields are

accelerated. At the same time the new sequencing methods provide a challenge for bioinformatics to process and store a high amount of data.

In the scope of this thesis, methods for the analysis of metagenome and metatranscriptome data were developed. At first, the taxonomic classifier metaBEETL was

developed and implemented. metaBEETL is based on the Burrows-Wheeler transformation and analyses metagenome sequences to gain a taxonomic profile of microbial communities. Using several bias controls, it provides accurate taxonomic profiles while being memory efficient. In this thesis the accuracy of the classifier is

shown by the analysis of an artificial metagenome dataset.

Secondly, the rich client software platform Metrans was developed for the analysis and comparison of metatranscriptome datasets. Metrans consists of a pipeline

designed for the analysis of metatranscriptomes. It also includes storage and visualization of the analysis results. The software is currently used in a number of

projects. The analysis of a metatranscriptome gained from the infected ear of a man and a time series from tidal flat are presented as analysis examples in this thesis.