Detecting Superbubbles in Assembly Graphs

We introduce a new concept of a subgraph class called a superbubble for analyzing assembly graphs, and propose an efficient algorithm for detecting it. Most assembly algorithms utilize assembly graphs like the de Bruijn graph or the overlap graph constructed from reads. From these graphs, many assembly algorithms first detect simple local graph structures (motifs), such as tips and bubbles, mainly to find sequencing errors. These motifs are easy to detect, but they are sometimes too simple to deal with more complex errors. The superbubble is an extension of the bubble, which is also important for analyzing assembly graphs. Though superbubbles are much more complex than ordinary bubbles, we show that they can be efficiently enumerated. We propose an average-case linear time algorithm (i.e., O(n+m) for a graph with n vertices and m edges) for graphs with a reasonable model, though the worst-case time complexity of our algorithm is quadratic (i.e., O(n(n+m))). Moreover, the algorithm is practically very fast: Our experiments show that our algorithm runs in reasonable time with a single CPU core even against a very large graph of a whole human genome.Comment: Peer-reviewed and presented as part of the 13th Workshop on Algorithms in Bioinformatics (WABI2013

Succinct Oblivious RAM

As online storage services become increasingly common, it is important that users\u27 private information is protected from database access pattern analyses. Oblivious RAM (ORAM) is a cryptographic primitive that enables users to perform arbitrary database accesses without revealing any information about the access pattern to the server. Previous ORAM studies focused mostly on reducing the access overhead. Consequently, the access overhead of the state-of-the-art ORAM constructions are almost at practical levels in certain application scenarios such as secure processors. However, we assume that the server space usage could become a new important issue in the coming big-data era. To enable large-scale computation in security-aware settings, it is necessary to rethink the ORAM server space cost using big-data standards. In this paper, we introduce "succinctness" as a theoretically tractable and practically relevant criterion of the ORAM server space efficiency in the big-data era. We, then, propose two succinct ORAM constructions that also exhibit state-of-the-art performance in terms of the bandwidth blowup and the user space. We also give non-asymptotic analyses and simulation results which indicate that the proposed ORAM constructions are practically effective