  In order to avoid the reference bias introduced by mapping reads to a
reference genome, bioinformaticians are investigating reference-free methods
for analyzing sequenced genomes. With large projects sequencing thousands of
individuals, this raises the need for tools capable of handling terabases of
sequence data. A key method is the Burrows-Wheeler transform (BWT), which is
widely used for compressing and indexing reads. We propose a practical
algorithm for building the BWT of a large read collection by merging the BWTs
of subcollections. With our 2.4 Tbp datasets, the algorithm can merge 600
Gbp/day on a single system, using 30 gigabytes of memory overhead on top of the
run-length encoded BWTs.
