Abstract. The need for data management and processing approaches in life sciences is be- coming more intense due to the continuous technological advances in the machines that produce data from biological samples. In today’s era, these machines produce vast amount of data that need to be processed. Most of these data are represented as sequences and their processing consists, mainly, of applying sequence alignment algorithms on them. State-of-the-art sequence alignment algorithms fail to perform efficiently for such big data, thus, the introduction of novel approaches is apparent. To make the condition worse, novel findings sometimes raise novel processing needs that cannot be fulfilled by adapting already existent approaches. Again, new methods are required. Finally, new rapidly evolving fields in life sciences, like that of miRNA research, lack centralised information resources. The knowledge in such fields is scattered in a multitude of scientific publications slowing down the work of researchers.