Indexing weighted sequences: Neat and efficient

In a weighted sequence, for every position of the sequence and every letter of the alphabet a probability of occurrence of this letter at this position is specified. Weighted sequences are commonly used to represent imprecise or uncertain data, for example in molecular biology, where they are known under the name of Position Weight Matrices. Given a probability threshold 1/z , we say that a string P of length m occurs in a weighted sequence X at position i if the product of probabilities of the letters of P at positions i, . . . , i+m−1 in X is at least 1/z . In this article, we consider an indexing variant of the problem, in which we are to pre-process a weighted sequence to answer multiple pattern matching queries. We present an O(nz)-time construction of an O(nz)-sized index for a weighted sequence of length n that answers pattern matching queries in the optimal O(m+Occ) time, where Occ is the number of occurrences reported. The cornerstone of our data structure is a novel construction of a family of [z] strings that carries the information about all the strings that occur in the weighted sequence with a sufficient probability. We thus improve the most efficient previously known index by Amir et al. (Theor. Comput. Sci., 2008) with size and construction time O(nz2 log z), preserving optimal query time. On the way we develop a new, more straightforward index for the so-called property matching problem. We provide an open-source implementation of our data structure and present experimental results using both synthetic and real data. Our construction allows us also to obtain a significant improvement over the complexities of the approximate variant of the weighted index presented by Biswas et al. at EDBT 2016 and an improvement of the space complexity of their general index. We also present applications of our index

Faster algorithms for longest common substring

In the classic longest common substring (LCS) problem, we are given two strings S and T, each of length at most n, over an alphabet of size σ, and we are asked to find a longest string occurring as a fragment of both S and T. Weiner, in his seminal paper that introduced the suffix tree, presented an (n log σ)-time algorithm for this problem [SWAT 1973]. For polynomially-bounded integer alphabets, the linear-time construction of suffix trees by Farach yielded an (n)-time algorithm for the LCS problem [FOCS 1997]. However, for small alphabets, this is not necessarily optimal for the LCS problem in the word RAM model of computation, in which the strings can be stored in (n log σ/log n) space and read in (n log σ/log n) time. We show that, in this model, we can compute an LCS in time (n log σ / √{log n}), which is sublinear in n if σ = 2^{o(√{log n})} (in particular, if σ = (1)), using optimal space (n log σ/log n). We then lift our ideas to the problem of computing a k-mismatch LCS, which has received considerable attention in recent years. In this problem, the aim is to compute a longest substring of S that occurs in T with at most k mismatches. Flouri et al. showed how to compute a 1-mismatch LCS in (n log n) time [IPL 2015]. Thankachan et al. extended this result to computing a k-mismatch LCS in (n log^k n) time for k = (1) [J. Comput. Biol. 2016]. We show an (n log^{k-1/2} n)-time algorithm, for any constant integer k > 0 and irrespective of the alphabet size, using (n) space as the previous approaches. We thus notably break through the well-known n log^k n barrier, which stems from a recursive heavy-path decomposition technique that was first introduced in the seminal paper of Cole et al. [STOC 2004] for string indexing with k errors. </p

Linear-time computation of cyclic roots and cyclic covers of a string

Cyclic versions of covers and roots of a string are considered in this paper. A prefix V of a string S is a cyclic root of S if S is a concatenation of cyclic rotations of V . A prefix V of S is a cyclic cover of S if the occurrences of the cyclic rotations of V cover all positions of S. We present O(n)-time algorithms computing all cyclic roots (using number-theoretic tools) and all cyclic covers (using tools related to seeds) of a length-n string over an integer alphabet. Our results improve upon O(n log log n) and O(n log n) time complexities of recent algorithms of Grossi et al. (WALCOM 2023) for the respective problems and provide novel approaches to the problems. As a by-product, we obtain an optimal data structure for Internal Circular Pattern Matching queries that generalize Internal Pattern Matching and Cyclic Equivalence queries of Kociumaka et al. (SODA 2015)

Approximate circular pattern matching

We investigate the complexity of approximate circular pattern matching (CPM, in short) under the Hamming and edit distance. Under each of these two basic metrics, we are given a length-n text T, a length-m pattern P, and a positive integer threshold k, and we are to report all starting positions (called occurrences) of fragments of T that are at distance at most k from some cyclic rotation of P. In the decision version of the problem, we are to check if there is any such occurrence. All previous results for approximate CPM were either average-case upper bounds or heuristics, with the exception of the work of Charalampopoulos et al. [CKP+, JCSS'21], who considered only the Hamming distance. For the reporting version of the approximate CPM problem, under the Hamming distance we improve upon the main algorithm of [CKP+, JCSS'21] from O(n+(n/m) k4) to O(n+(n/m) k3 log log k) time; for the edit distance, we give an O(nk2)-time algorithm. Notably, for the decision versions and wide parameter-ranges, we give algorithms whose complexities are almost identical to the state-of-the-art for standard (i.e., non-circular) approximate pattern matching: For the decision version of the approximate CPM problem under the Hamming distance, we obtain an O(n + (n/m) k2 log k/ log log k)-time algorithm, which works in O(n) time whenever k = O( p mlog log m/logm). In comparison, the fastest algorithm for the standard counterpart of the problem, by Chan et al. [CGKKP, STOC'20], runs in O(n) time only for k = O(√ m). We achieve this result via a reduction to a geometric problem by building on ideas from [CKP+, JCSS'21] and Charalampopoulos et al. [CKW, FOCS'20]. For the decision version of the approximate CPM problem under the edit distance, the O(nk log3 k) runtime of our algorithm near matches the O(nk) runtime of the Landau-Vishkin algorithm [LV, J. Algorithms'89] for approximate pattern matching under edit distance; the latter algorithm remains the fastest known for k = Ω(m2/5). As a stepping stone, we propose an O(nk log3 k)-time algorithm for solving the Longest Prefix k-Approximate Match problem, proposed by Landau et al. [LMS, SICOMP'98], for all k ∈ {1, , k}. Our algorithm is based on Tiskin's theory of seaweeds [Tiskin, Math. Comput. Sci.'08], with recent advancements (see Charalampopoulos et al. [CKW, FOCS'22]), and on exploiting the seaweeds' relation to Monge matrices. In contrast, we obtain a conditional lower bound that suggests a polynomial separation between approximate CPM under the Hamming distance over the binary alphabet and its non-circular counterpart. We also show that a strongly subquadratic-time algorithm for the decision version of approximate CPM under edit distance would refute the Strong Exponential Time Hypothesis

Circular pattern matching with k mismatches

We consider the circular pattern matching with k mismatches (k-CPM) problem in which one is to compute the minimal Hamming distance of every length-m substring of T and any cyclic rotation of P, if this distance is no more than k. It is a variation of the well-studied k-mismatch problem. A multitude of papers has been devoted

Efficient computation of sequence mappability

Sequence mappability is an important task in genome resequencing. In the (k, m)-mappability problem, for a given sequence T of length n, the goal is to compute a table whose ith entry is the number of indices j≠ i such that the length-m substrings of T starting at positions i and j have at most k mismatches. Previous works on this problem focused on heuristics computing a rough approximation of the result or on the case of k= 1. We present several efficient algorithms for the general case of the problem. Our main result is an algorithm that, for k= O(1) , works in O(n) space and, with high probability, in O(n· min { mk, log kn}) time. Our algorithm requires a careful adaptation of the k-errata trees of Cole et al. [STOC 2004] to avoid multiple counting of pairs of substrings. Our technique can also be applied to solve the all-pairs Hamming distance problem introduced by Crochemore et al. [WABI 2017]. We further develop O(n2) -time algorithms to compute all (k, m)-mappability tables for a fixed m and all k∈ { 0 , … , m} or a fixed k and all m∈ { k, … , n}. Finally, we show that, for k, m= Θ (log n) , the (k, m)-mappability problem cannot be solved in strongly subquadratic time unless the Strong Exponential Time Hypothesis fails. This is an improved and extended version of a paper presented at SPIRE 2018