The work is based on the algorithm SETTER (Secondary Structure Tertiary Structure-based Similarity Algorithm), which is designed to compare the 3D structures of RNA. SETTER in the original version can only compare pairs of RNA, however many applications require real similarity comparison between a set of RNA structures. The main idea used in MultiSETTER is a well-known approach used for multiple sequence alignment, which is based on the Neigbour-Joining method - a method for calculation of the taxonomic tree from distances between taxa - and on pairwise alignment. At each step the closest pair is aligned according to the taxonomic tree. To achieve good results, it was necessary to invent a method that creates a fictive average RNA structure by merging two RNAs that shares the structural characteristics of both RNA.

Automatically classifying malware variants source code is the most important research issue in the field of digital forensics. By means of malware classification, we can get complete behavior of malware which can simplify the forensics task. In previous researches, researchers use malware binary to perform dynamic analysis or static analysis after reverse engineering. In the other hand, malware developers even use anti-VM and obfuscation techniques try to cheating malware classifiers. With honeypots are increasingly used, researchers could get more and more malware source code. Analyzing these source codes could be the best way for malware classification. In this paper, a novel classification approach is proposed which based on logic and directory structure similarity of malwares. All collected source code will be classified correctly by hierarchical clustering algorithm. The proposed system not only helps us classify known malwares correctly but also find new type of malware. Furthermore, it avoids forensics staffs spending too much time to reanalyze known malware. And the system could also help realize attacker's behavior and purpose. The experimental results demonstrate the system can classify the malware correctly and be applied to other source code classification aspect.

The advent of high-throughput sequencing and structure determination techniques has had a tremendous impact on our quest in cracking the language of life. The genomic and protein data is now being accumulated at a phenomenal rate, with the motivation of deriving insights into the function, mechanism, and evolution of the biomolecules, through analysis of their similarities, differences, and interactions. The rapid increase in the size of the biomolecular databases, however, calls for development of new computational methods for sensitive and efficient management and analysis of this information. In this thesis, we propose and implement several approaches for accurate and highly efficient comparison and retrieval of protein sequences and structures. The observation that corresponding residues in related proteins share similar inter-residue contacts is exploited in derivation of a new set of biologically sensitive metric amino acid substitution matrices, yielding accurate alignment and comparison of proteins. The metricity of these matrices has allowed efficient indexing and retrieval of both protein sequences and structures. A landmark-guided embedding of protein sequences is developed to represent subsequences in a vector space for approximate, but extremely fast spatial indexing and similarity search. Whereas protein structure comparison and search tasks were hitherto handled separately, we propose an integrated approach that serves both of these tasks and performs comparable to or better than other available methods. Our approach hinges on identification of similar residue contacts using distance-based indexing and provides the best of the both worlds: the accuracy of detailed structure alignment algorithms, at a speed comparable to that of the structure retrieval algorithms. We expect that the methods and tools developed in this study will find use in a wide range of application areas including annotation of new proteins, discovery of functional motifs, discerning evolutionary relationships among genes and species, and drug design and targeting.