The ed-tree: an index for large DNA sequence databases

The growing interest in genomic research has caused an explosive growth in the size of DNA (deoxyribonucleic acid) databases making it increasingly challenging to perform searches on them. In this paper, we proposed an index structure called the ed-tree for supporting fast and effective homology searches on DNA databases. The ed-tree is developed to enable probe-based homology search algorithms like Blastn which generate short probe strings from the query sequence and then match them against the sequence database in order to identify potential regions of high similarity to the query sequence. Unlike Blastn however, the homology search algorithm we developed for ed-tree supports more flexible probe model with longer probes and more relaxed matching. As a consequence, the ed-tree is not only more effective and efficient than the latest Blastn (NCBI Blast2) when supporting homology search but also takes up moderate storage compared to existing data structures like the suffix tree. To index a DNA database of 2 giga base pairs (Gbps), ed-tree only takes less than 3Gb of secondary storage, which is easily handled by a desktop PC. Experiments will be shown in this paper to support our claim.