A Computational Analysis of the Impact of Transcript Diversity on Protein Domains Coded by Human, Mouse and Rat Transcription Factor Genes

In this study, three different mammalian genomes are investigated with respect to their transcript diversity. The main focus of the thesis is investigation of how this transcript diversity reflects on the protein structures. Within the three genomes, specifically Transcription Factor genes are analyzed. The methodologies employed include biological data retrieval from contemporary biomedical resources, storage of data in a relational database and further computational analyses. Our results revealed that both in human and in mouse more than half of the TF genes analyzed have unique transcripts which code for proteins with unique domains. That is they have at least 2 unique transcripts coding for differential protein domain structures. Importantly, the unique domain coded by one of the TF transcripts and not the other conveys DNA-binding ability. This is the case for 51% of TF human genes and 52% of TF mouse genes. Given the lesser number of transcripts sequenced per rat TF genes in general, this percentage stays at 37%, as expected. The overall conclusion from this thesis is that the majority of TF genes have transcript diversity and that this transcript diversity brings diversity in protein structures and thus in functions. Keywords: Transcription factor, genomes, transcripts, protein structure, domain function, DNA-binding, biological databases, data retrieval and storage.