MCAT: Motif Combining and Association Tool

Abstract

Motivation: De novo motif discovery in biological sequences is always an important and computationally challenging problem. In the past 20 years, a myriad of algorithms have been proposed to solve this problem with varying success. Ensemble algorithms, which combine different individual algorithms, have been introduced in previous studies, and it has been proved that an ensemble strategy can improve the prediction accuracy. However, the performance of these tools has not yet met most people's expectation. One reason for the low performance is failure to adapt to complicated and large data sets. Another existing problem is that fewer motif finding tools are available, and many of them are not maintained. Results: I present a novel and fast tool MCAT (Motif Combining and Association Tool) for de novo motif discovery by combining six state-of-the-art motif discovery tools (MEME, BioProspector, DECOD, XXmotif, Weeder, and CMF). In addition, I developed an innovative motif combining algorithm, VoteRank, which is a position based algorithm that votes, ranks, and combines candidate motifs. By testing against DNA sequences from budding yeast, fission yeast, human, fruit fly, and mouse, I showed that MCAT is able to identify exact match motifs in DNA sequences efficiently and achieves at least 30% improvement in prediction accuracy. I am thankful to all of my group members and former colleagues, Jeff Robertson, Zhen Guo, Christy Coghlan, and Jake Martinez for helping with the MCAT project, Doaa Altarawy for her advice at the beginning of my research, Haitham Elmarakeby for the Beacon project, and Xiao Liang for her valuable ideas and encouragement during my research.

Implications of diffusion and time-varying morphogen gradients for the dynamic positioning and precision of bistable gene expression boundaries

“…Spatial patterning of gene expression, especially during embryonic development, has fascinated theoretical and experimental researchers throughout the past century. A central question involves how an initial pattern is interpreted by cells in a tissue to produce sharp boundaries between spatial domains expressing distinct sets of genes [ 1 , 2 ]. Significant evidence indicates that concentration gradients of chemical morphogens provide the necessary positional information for accurate boundary formation in various contexts ranging from axis orientation [ 3 – 6 ] to neurogenesis [ 7 , 8 ].…”