Pattern finding in biomolecular data is at the core of Computational Molecular Biology research. Indeed, it makes a very important contribution in the analysis of these data. It can reveal information about shared biological functions of biological macromolecules, coming from several different organisms, by the identification of patterns that are shared by structures related to these macromolecules. These patterns, which have been conserved during evolution, often play an important structural and/or functional role, and consequently, shed light on the mechanisms and the biological processes in which these macromolecules participate. Pattern finding in biomolecular data is also used in evolutionary studies, in order to analyze relationships that exist between species and establish if two, or several, biological macromolecules are homologous and to reconstruct the phylogenetic tree that links them to their common biological ancestor. On the other hand, with the new sequencing technologies, the number of biological sequences in databases is increasing exponentially. In addition, the lengths of these sequences are large. Hence, the finding of patterns in such databases requires the development of fast, low memory requirement and highperformance techniques and approaches. This issue contains very interesting papers that deal with pattern finding in Computational Molecular Biology.