Dr. Al Seesi’s work focuses on analysis of DNA and RNA sequences, with special focus on next generation transcriptome sequencing. Her research addresses various problems including RNA structure analysis, transcriptome reconstruction, allele specific isoform expression estimation, and cancer specific mutation identification. Tackling each of these problems requires employing a combination of known computational approaches such as machine learning, grammatical modeling, statistical analysis, parallel algorithms, dynamic programming, and expectation maximization algorithms.
Working in an interdisciplinary field, Dr. Al Seesi is engaged in active collaborations with other biology and computer science labs at the University of Connecticut, University of Connecticut Health Center, and Georgia State University. As part of a collaboration with Life Technologies, Dr. Al Seesi worked on the development of ION-Torrent plugins for isoform expression estimation and calling expressed SNVs from RNA-Seq reads. These plugins were ranked as first and second top ranked plugins on the ION Community.