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PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
A memory-efficient data structure representing exact-matc...
Hieu Dinh, Sanguthevar Rajasekaran · 2010-09-21 · via cs.DS updates on arXiv.org

An exact-match overlap graph of $n$ given strings of length $\ell$ is an edge-weighted graph in which each vertex is associated with a string and there is an edge $(x,y)$ of weight $ω= \ell - |ov_{max}(x,y)|$ if and only if $ω\leq λ$, where $|ov_{max}(x,y)|$ is the length of $ov_{max}(x,y)$ and $λ$ is a given threshold. In this paper, we show that the exact-match overlap graphs can be represented by a compact data structure that can be stored using at most $(2λ-1 )(2\lceil\log n\rceil + \lceil\logλ\rceil)n$ bits with a guarantee that the basic operation of accessing an edge takes $O(\log λ)$ time. Exact-match overlap graphs have been broadly used in the context of DNA assembly and the \emph{shortest super string problem} where the number of strings $n$ ranges from a couple of thousands to a couple of billions, the length $\ell$ of the strings is from 25 to 1000, depending on DNA sequencing technologies. However, many DNA assemblers using overlap graphs are facing a major problem of constructing and storing them. Especially, it is impossible for these DNA assemblers to handle the huge amount of data produced by the next generation sequencing technologies where the number of strings $n$ is usually very large ranging from hundred million to a couple of billions. In fact, to our best knowledge there is no DNA assemblers that can handle such a large number of strings. Fortunately, with our compact data structure, the major problem of constructing and storing overlap graphs is practically solved since it only requires linear time and and linear memory. As a result, it opens the door of possibilities to build a DNA assembler that can handle large-scale datasets efficiently.