Agrobacterium tumefaciens is a gram-negative bacterium of the family Rhizobiaceae and is known for its pathogenic ability to induce tumor formation in over 100 different species of plants, often leading to significant decline in individual plant health. The mechanism by which tumors are induced includes a segment of DNA contained within the bacterium’s T i plasmid (known as T-DNA), which is integrated in the host genome. The T-DNA is oncogenic, encoding enzymes which increase the production of certain plant hormones whose actions lead to tumor formation. The impressive ability of T-DNA to insert into plant genomes has led to its use as a common method of genetic transformation in plants. In this study, we assessed the T-DNA pointof insertion sequences using machine learning and data mining. While, it has been documented that the T-DNA insertion occurs at double strand breaks, the mechanism of insertion remains elusive. Currently, the point at which the T-DNA is inserted in the host genome has been thought to be somewhat random with respect to the surrounding sequences, and uncontrolled multiple insertions appear to be a common phenomenon. Machine learning is a series of statistical/computational techniques which can be applied in order to find relationships in large, complex data sets. In this study, we utilized machine learning algorithms as well as data mining techniques in efforts to probe the relationship between points of insertion of the T-DNA and the host genomic sequences. Our preliminary data suggest that in fact there is a relationship between host sequence and probability of insertion. Additionally, using neural networks, we have successfully generated a model that can accurately predict whether a certain genomic sequence will lead to T-DNA insertion.

Understanding the nature of plasmid integration in agrobacterium tumefaciens using computational methods

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