Abstract
BACKGROUND: Extraordinary size variation of higher plant nuclear genomes is in large part caused by differences in accumulation of repetitive DNA. This makes repetitive DNA of great interest for studying the molecular mechanisms shaping architecture and function of complex plant genomes. However, due to methodological constraints of conventional cloning and sequencing, a global description of repeat composition is available for only a very limited number of higher plants. In order to provide further data required for investigating evolutionary patterns of repeated DNA within and between species, we used a novel approach based on massive parallel sequencing which allowed a comprehensive repeat characterization in our model species, garden pea (Pisum sativum). RESULTS: Analysis of 33.3 Mb sequence data resulted in quantification and partial sequence reconstruction of major repeat families occurring in the pea genome with at least thousands of copies. Our results showed that the pea genome is dominated by LTR-retrotransposons, estimated at 140,000 copies/1C. Ty3/gypsy elements are less diverse and accumulated to higher copy numbers than Ty1/copia. This is in part due to a large population of Ogre-like retrotransposons which alone make up over 20% of the genome. In addition to numerous types of mobile elements, we have discovered a set of novel satellite repeats and two additional variants of telomeric sequences. Comparative genome analysis revealed that there are only a few repeat sequences conserved between pea and soybean genomes. On the other hand, all major families of pea mobile elements are well represented in M. truncatula. CONCLUSION: We have demonstrated that even in a species with a relatively large genome like pea, where a single 454-sequencing run provided only 0.77% coverage, the generated sequences were sufficient to reconstruct and analyze major repeat families corresponding to a total of 35-48% of the genome. These data provide a starting point for further investigations of legume plant genomes based on their global comparative analysis and for the development of more sophisticated approaches for data mining.