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.
Cite
@article{arxiv.1511.00898,
title = {Burrows-Wheeler transform for terabases},
author = {Jouni Sirén},
journal= {arXiv preprint arXiv:1511.00898},
year = {2016}
}
Comments
This is the full version of the paper that was accepted to DCC 2016. The implementation is available at https://github.com/jltsiren/bwt-merge