Faster algorithms for 1-mappability of a sequence

In the k-mappability problem, we are given a string x of length n and integers m and k, and we are asked to count, for each length-m factor y of x, the number of other factors of length m of x that are at Hamming distance at most k from y. We focus here on the version of the problem where k = 1. The fastest known algorithm for k = 1 requires time O(mn log n/ log log n) and space O(n). We present two algorithms that require worst-case time O(mn) and O(n log^2 n), respectively, and space O(n), thus greatly improving the state of the art. Moreover, we present an algorithm that requires average-case time and space O(n) for integer alphabets if m = {\Omega}(log n/ log {\sigma}), where {\sigma} is the alphabet size

Efficient Computation of Sequence Mappability

Sequence mappability is an important task in genome re-sequencing. In the $(k,m)$-mappability problem, for a given sequence $T$ of length $n$, our goal is to compute a table whose $i$th entry is the number of indices $j \ne i$ such that length-$m$ substrings of $T$ starting at positions $i$ and $j$ have at most $k$ mismatches. Previous works on this problem focused on heuristic approaches to compute 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 works in $\mathcal{O}(n \min\{m^k,\log^{k+1} n\})$ time and $\mathcal{O}(n)$ space for $k=\mathcal{O}(1)$. It requires a carefu l adaptation of the technique of Cole et al.~[STOC 2004] to avoid multiple counting of pairs of substrings. We also show $\mathcal{O}(n^2)$-time algorithms to compute all results for a fixed $m$ and all $k=0,\ldots,m$ or a fixed $k$ and all $m=k,\ldots,n-1$. Finally we show that the $(k,m)$-mappability problem cannot be solved in strongly subquadratic time for $k,m = \Theta(\log n)$ unless the Strong Exponential Time Hypothesis fails.Comment: Accepted to SPIRE 201

Longest Common Prefixes with kk-Errors and Applications

Although real-world text datasets, such as DNA sequences, are far from being uniformly random, average-case string searching algorithms perform significantly better than worst-case ones in most applications of interest. In this paper, we study the problem of computing the longest prefix of each suffix of a given string of length $n$ over a constant-sized alphabet that occurs elsewhere in the string with $k$-errors. This problem has already been studied under the Hamming distance model. Our first result is an improvement upon the state-of-the-art average-case time complexity for non-constant $k$ and using only linear space under the Hamming distance model. Notably, we show that our technique can be extended to the edit distance model with the same time and space complexities. Specifically, our algorithms run in $\mathcal{O}(n \log^k n \log \log n)$ time on average using $\mathcal{O}(n)$ space. We show that our technique is applicable to several algorithmic problems in computational biology and elsewhere

The k-Mappability Problem Revisited

The k-mappability problem has two integers parameters m and k. For every subword of size m in a text S, we wish to report the number of indices in S in which the word occurs with at most k mismatches. The problem was lately tackled by Alzamel et al. [Mai Alzamel et al., 2018]. For a text with constant alphabet ? and k ? O(1), they present an algorithm with linear space and O(nlog^{k+1}n) time. For the case in which k = 1 and a constant size alphabet, a faster algorithm with linear space and O(nlog(n)log log(n)) time was presented in [Mai Alzamel et al., 2020]. In this work, we enhance the techniques of [Mai Alzamel et al., 2020] to obtain an algorithm with linear space and O(n log(n)) time for k = 1. Our algorithm removes the constraint of the alphabet being of constant size. We also present linear algorithms for the case of k = 1, |?| ? O(1) and m = ?(?n)

Faster algorithms for 1-mappability of a sequence

In the k-mappability problem, we are given a string x of length n and integers m and k, and we are asked to count, for each length-m factor y of x, the number of other factors of length m of x that are at Hamming distance at most k from y. We focus here on the version of the problem where k=1. There exists an algorithm to solve this problem for k=1 requiring time O(mnlog⁡n/log⁡log⁡n) using space O(n). Here we present two new algorithms that require worst-case time O(mn) and O(nlog⁡nlog⁡log⁡n), respectively, and space O(n), thus greatly improving the previous result. Moreover, we present another algorithm that requires average-case time and space O(n) for integer alphabets of size σ if m=Ω(logσ⁡n). Notably, we show that this algorithm is generalizable for arbitrary k, requiring average-case time O(kn) and space O(n) if m=Ω(klogσ⁡n), assuming that the letters are independent and uniformly distributed random variables. Finally, we provide an experimental evaluation of our average-case algorithm demonstrating its competitiveness to the state-of-the-art implementation