Fig 1.
DIDA workflow with four partitions as an example.
(a) First, we partition the targets into four parts using a heuristic balanced cut. (b) Next, we create an index for each partition. (c) The reads are then flowed through Bloom filters to dispatch the alignment task to the corresponding node(s). (d) Finally, the reads are aligned on all four partitions and the results are combined together to create the final output.
Table 1.
Dataset specification.
Fig 2.
Scalability of different aligners using DIDA for C. elegant data.
Y-axis indicates the runtime/memory scalability in the in the [0.1] interval for different alignment tools. The scalability of each tool is shown in the standalone case and within DIDA framework on 2, 4, 8, and 12 nodes.
Table 2.
Alignment time/indexing memory for all aligners on different datasets.
Fig 3.
Scalability of different aligners using DIDA for human draft assembly.