Optimal gap-affine alignment in O(s) space

Author:

Marco-Sola Santiago12ORCID,Eizenga Jordan M3ORCID,Guarracino Andrea45ORCID,Paten Benedict3ORCID,Garrison Erik5ORCID,Moreto Miquel16ORCID

Affiliation:

1. Computer Sciences Department, Barcelona Supercomputing Center , Barcelona 08034, Spain

2. Departament d’Arquitectura de Computadors i Sistemes Operatius, Universitat Autònoma de Barcelona , Barcelona 08193, Spain

3. Genomics Institute, University of California Santa Cruz , Santa Cruz, CA 95064, USA

4. Genomics Research Centre, Human Technopole , Milan 20157, Italy

5. Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center , Memphis, TN 38163, USA

6. Departament d’Arquitectura de Computadors, Universitat Politècnica de Catalunya , Barcelona 08034, Spain

Abstract

Abstract Motivation Pairwise sequence alignment remains a fundamental problem in computational biology and bioinformatics. Recent advances in genomics and sequencing technologies demand faster and scalable algorithms that can cope with the ever-increasing sequence lengths. Classical pairwise alignment algorithms based on dynamic programming are strongly limited by quadratic requirements in time and memory. The recently proposed wavefront alignment algorithm (WFA) introduced an efficient algorithm to perform exact gap-affine alignment in O(ns) time, where s is the optimal score and n is the sequence length. Notwithstanding these bounds, WFA’s O(s2) memory requirements become computationally impractical for genome-scale alignments, leading to a need for further improvement. Results In this article, we present the bidirectional WFA algorithm, the first gap-affine algorithm capable of computing optimal alignments in O(s) memory while retaining WFA’s time complexity of O(ns). As a result, this work improves the lowest known memory bound O(n) to compute gap-affine alignments. In practice, our implementation never requires more than a few hundred MBs aligning noisy Oxford Nanopore Technologies reads up to 1 Mbp long while maintaining competitive execution times. Availability and implementation All code is publicly available at https://github.com/smarco/BiWFA-paper. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

European Union

Ministerio de Ciencia e Innovacion

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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