Prefix-Free Parsing for Building Big BWTs

Boucher, C.; Gagie, T.; Kuhnle, A.; Langmead, B.; Manzini, G.; Mun, T.

doi:10.1101/472399

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bioRxiv · 10.1101/472399

Prefix-Free Parsing for Building Big BWTs

Boucher, C.·Gagie, T.·Kuhnle, A.·Langmead, B.·Manzini, G.·Mun, T.

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Abstract

High-throughput sequencing technologies have led to explosive growth of genomic databases; one of which will soon reach hundreds of terabytes. For many applications we want to build and store indexes of these databases but constructing such indexes is a challenge. Fortunately, many of these genomic databases are highly-repetitive--a characteristic that can be exploited to ease the computation of the Burrows-Wheeler Transform (BWT), which underlies many popular indexes. In this paper, we introduce a preprocessing algorithm, referred to as prefix-free parsing, that takes a text T as input, and in one-pass generates a dictionary D and a parse P of T with the property that the BWT of T can be constructed from D and P using workspace proportional to their total size and O(|T|)-time. Our experiments show that D and P are significantly smaller than T in practice, and thus, can fit in a reasonable internal memory even when T is very large. In particular, we show that with prefix-free parsing we can build an 131-megabyte run-length compressed FM-index (restricted to support only counting and not locating) for 1000 copies of human chromosome 19 in 2 hours using 21 gigabytes of memory suggesting that we can build a 6.73 gigabyte index for 1000 complete human-genome haplotypes in approximately 102 hours using about 1 terabyte of memory

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Boucher, C., Gagie, T., Kuhnle, A., Langmead, B., Manzini, G., Mun, T.. 2018-11-19. Prefix-Free Parsing for Building Big BWTs. https://doi.org/10.1101/472399

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