External memory BWT and LCP computation for sequence collections with applications

• Main Authors: Lavinia Egidi, Felipe A. Louza, Giovanni Manzini, Guilherme P. Telles
• Format: Article
• Language: English
• Published: BMC 2019-03-01
• Series: Algorithms for Molecular Biology
• Subjects: Burrows–Wheeler Transform; Longest common prefix array; Maximal repeats; All pairs suffix–prefix overlaps; Succinct de Bruijn graph; External memory algorithms
• Online Access: http://link.springer.com/article/10.1186/s13015-019-0140-0

Abstract Background Sequencing technologies produce larger and larger collections of biosequences that have to be stored in compressed indices supporting fast search operations. Many compressed indices are based on the Burrows–Wheeler Transform (BWT) and the longest common prefix (LCP) array. Because of the sheer size of the input it is important to build these data structures in external memory and time using in the best possible way the available RAM. Results We propose a space-efficient algorithm to compute the BWT and LCP array for a collection of sequences in the external or semi-external memory setting. Our algorithm splits the input collection into subcollections sufficiently small that it can compute their BWT in RAM using an optimal linear time algorithm. Next, it merges the partial BWTs in external or semi-external memory and in the process it also computes the LCP values. Our algorithm can be modified to output two additional arrays that, combined with the BWT and LCP array, provide simple, scan-based, external memory algorithms for three well known problems in bioinformatics: the computation of maximal repeats, the all pairs suffix–prefix overlaps, and the construction of succinct de Bruijn graphs. Conclusions We prove that our algorithm performs $${\mathcal {O}}(n\, \mathsf {maxlcp})$$ O(nmaxlcp) sequential I/Os, where n is the total length of the collection and $$\mathsf {maxlcp}$$ maxlcp is the maximum LCP value. The experimental results show that our algorithm is only slightly slower than the state of the art for short sequences but it is up to 40 times faster for longer sequences or when the available RAM is at least equal to the size of the input.