Master's Thesis from the year 2014 in the subject Computer Science - Bioinformatics, grade: N, language: English, abstract: The data from next generation sequencing technologies has led to an explosion in genome sequence data available in public databases. This data provides unique opportunities to study the molecular mechanisms of gene evolution: how new genes and proteins originate and how they diversify. A major challenge is retracing origin of extant genes or proteins, by searching existing databases for related sequences and identifying evolutionary similarities. Therefore, enhanced and faster search algorithms are being developed, e.g. on accelerators such as GPU, in order to cope with the huge size of todays DNA or protein sequence databases. GeneTracer is a tool was developed to localize the common subsequences between two ancestors and its offspring. Besides, compute percentages of ancestors contributions in offspring. GeneTracer was developed to find the origin of unknown shuffling/offspring sequence. A database is scanned and the similarity between offspring sequence and each one in the database is computed using pairwise local sequence alignment algorithm. Based on similarity score, 100 sequences that have the highest score is realigned with shuffling sequence to determine length of common subsequences between them using local alignment algorithm. The two sequences that have longest subsequences with shuffling are the nearest origin to offspring. Swissport database contains around 400,000 proteins is used in the test. The execution time around hours. So, GPU is to accelerate the tool. Speedup is 84x using singleGPU Tesla C2075 versus Intel© Corei3 multiprocessor. Finally, the main contribution of work is developing fast tool that retrace origins of unknown gene/protein sequences.

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