In-silico Approach to Map Transcription Factor Binding Motifs onto Drosophila Cardiac Genes

The development of multi cellular organisms requires a consortium of different types of cells that interact to form a functional organism. Each cell has a unique genetic profile that is regulated in a specific manner according to different developmental stages of an organism. This control is orchestrated by regulatory sequences called enhancers which are gene regulatory sequences that dictate the spatio-temporal patterns of gene expression by controlling transcriptional activities. A common feature of the regulatory enhancers is the presence of multiple binding sites known as Transcription Factor Binding Sites (TFBS), which binds to multiple transcription factors. A molecular understanding of enhancers and various transcription factors that bind to these is necessary for determining complex biological networks. The binding sites within the enhancers are conserved in nature, thus finding out those sites can help in uncovering various interaction mechanisms. In the present study, we try to address this issue by computational approach to predict TFBS within the set of enhancers in Drosophila melanogaster heart organ. We collected all the known enhancers that are active in cardiac mesoderm. The motifs were identified and functionally characterized by comparing with the database of known motifs. Putative motifs were mapped onto our dataset of enhancer sequences. We believe that these mapped enhancer sequences can be used to predict various de novo enhancers in the entire Drosophila melanogaster genome using machine learning techniques. Thus these findings helps to discover mechanisms currently unknown and may be important in gene regulation.