The advent of high throughput DNA sequencing methods has made the sequencing and assembly of new genomes feasible affordable. New computational methods have jointly been developed to control, preprocess, assemble and verify the large amounts of data produced by high throughput DNA sequencing. We find however that few practical solutions exist to control for sample contamination by exogeneous genetic material, a control step that appears to be much less frequently conducted than the other common quality controls. As the effects of contamination can be highly detrimental to the assembly process and subsequent analyses, we propose that a readily usable and comprehensive method to identify and filter contamination in genome sequencing experiments would be of great interest. In this study, we develop a method to automatically: identify possible contaminants in a genome sequencing reads dataset ; find and compile relevant databases for sequences comparisons ; filter contamination in raw reads and/or assembled contigs. The method is implemented as a readily usable command line application (Rscript). Validation of the method is then performed using several artificially contaminated datasets. The execution of the application is profiled under different levels of parallelization. We then go on to apply our method on a particularly challenging archaean genome assembly experiment, yielding supportive results. Potential caveats are discussed, as well as possible areas and blueprints for further improvements.