My research area has been focusing on creating software for analysis of repetitive elements in the human genome and its application in cancer treatment. In particular, I have developed a program called PyMISA that can detect microsatellites from a given DNA sequence. Microsatellites are tandem repeats of length 1 – 6 base pairs (e. g., ATATATATAT contains 5 tandem copies of the repeat unit AT of length 2). Changes in microsatellites due to mutations have been associated with a variety of cancers such as Osteosarcoma. Osteosarcoma is a rare type of bone cancer that mainly occurs in young adolescents and its treatment has been hampered by lack of early detection. Thus, early detection of unusual microsatellites can help treat those with Osteosarcoma. Due to advances in sequencing technologies, computers can be used to extract microsatellites in sequenced DNA while being more economical and cheaper than traditional laboratory techniques. I have written PyMISA in the Python programming language with the purpose of finding all perfect and imperfect microsatellites from sequenced DNA. For the perfect microsatellite detection, PyMISA uses the ideas in another microsatellite finder software named MISA, which was implemented in Perl. Overall, PyMISA performs with the same accuracy and precision as MISA but in a shorter time. In addition, PyMISA can also detect imperfect microsatellites using a novel numerical approach. However, PyMISA’s imperfect microsatellites detection is still slow and requires additional development. Afterward, we are planning to use PyMISA in analysis of our dataset of 292 Osteosarcoma patients to find distinct early warning signs of Osteosarcoma. Through this research experience, I was able to explore the field of bioinformatics and learned much more about software engineering as a person from a biological science background.
Author: Linh Le
Faculty Advisor: Chun Liang, Department of Biology
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