Constructing burrows-wheeler transforms of large string collections via merging

The throughput of biological sequencing technologies has led to the necessity for compressed and accessible sequencing formats. Recently, the Multi-String Burrows-Wheeler Transform (MSBWT) has risen in prevalence as a method for transforming sequence data to improve compression while providing access to the reads through an auxiliary FM-index. While there are many algorithms for building the MSBWT for a collection of strings, they do not scale well as the length of those strings increases. We propose a new method for constructing the MSBWT for a collection of strings based on previous work for merging two or more MSBWTs. It requires O(N * LCPavg * log(m)) time and O(N) bits of memory for a collection of m strings composed of N symbols where the average longest common prefix of all suffixes is LCPavg. We evaluate the speed of the algorithm on multiple datasets that vary in both quantity of strings and string length. Availability: https://code.google.com/p/msbwt/source/browse/MUSCython/MultimergeCython.pyx

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