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Mobile genetic elements (MGEs) are fragments of DNA that can move around within the genome through retrotransposition. These are responsible for various important events such as gene inactivation, transduction, regulation of gene expression and genome expansion. The present work involves the identification and study of the distribution of Alu and L1 retrotransposons in the genome of Macaca mulatta, an extensively used organism in biomedical studies. We also make comparisons with MGE distributions in other primate genomes and study the physicochemical properties of the local DNA structure around the transposon insertion site using ELAN. The present work also includes computational testing of the pre-insertion loci in order to detect unique features based on DNA structure, thermodynamic considerations and protein interaction measures. Although there is significant sequence divergence between the elements of M. mulatta and H. sapiens, their genome wide distribution is very similar; comparing the distribution of L1's in all available X chromosome sequences suggests a common mechanism behind the spread of MGE's in primate genomes.
Recently published gorilla genome has offered an opportunity to study human evolution through variety of approaches. Mobile genetic elements (MGEs) insert non randomly in genome through mechanisms such as retrotransposition and may cause gene inactivation, transduction, regulation of gene expression and genome expansion. Here we report that majority of gorilla genome is occupied with MGEs (> 36%) with presence of LTRs and Non-LTRs such as Alus and L1s. Other types of MGEs such as MIRs, retrovirus like elements ERVs and DNA transposons are also found using repeatmasker and ELAN pipeline. The distribution is similar to Humans and Macaca genome. Using DNA Scanner we also scanned preinsertion loci for number of different properties such as DNA denaturation, energy measures, potential for protein interactions and sequence based features. We also predicted preinsertion loci with > 70% accuracy using a machine learning tool called insertion site finder (ISF) based upon support vector machines.
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