A memory-efficient algorithm for multiple sequence alignment with constraints

Motivation: Recently, the concept of the constrained sequence alignment was proposed to incorporate the knowledge of biologists about structures/functionalities/consensuses of their datasets into sequence alignment such that the user-specified residues/nucleotides are aligned together in the computed alignment. The currently developed programs use the so-called progressive approach to efficiently obtain a constrained alignment of several sequences. However, the kernels of these programs, the dynamic programming algorithms for computing an optimal constrained alignment between two sequences, run in O(γn 2) memory, where γ is the number of the constraints and n is the maximum of the lengths of sequences. As a result, such a high memory requirement limits the overall programs to align short sequences~only. Results: We adopt the divide-and-conquer approach to design a memory-efficient algorithm for computing an optimal constrained alignment between two sequences, which greatly reduces the memory requirement of the dynamic programming approaches at the expense of a small constant factor in CPU time. This new algorithm consumes only O(αn) space, where α is the sum of the lengths of constraints and usually α ≪ n in practical applications. Based on this algorithm, we have developed a memory-efficient tool for multiple sequence alignment with constraints. Availability: http://genome.life.nctu.edu.tw/MUSICME Contact: cllu@mail.nctu.edu.tw