Fast BWT in Small Space by Blockwise Suffix Sorting (Juha Kärkkäinen

Fast BWT in Small Space by Blockwise Suffix Sorting (Juha Kärkkäinen

This is an old but very useful paper by Juha Krkkinen that we are looking into to understand the BGI paper “GPU-Accelerated BWT Construction for Large Collection of Short Reads” posted a few months back.

Fast BWT in small space by blockwise suffix sorting

We present a new space- and time-efficient algorithm for computing the BurrowWheeler transform (BWT). For any choice of a parameter v?[3,n2/3], the computation of BWT for a text of length n takes O(nlogn+vn) worst-case time and View the MathML source average-case time using View the MathML source bits of space in addition to the text and the BWT. For example, if v=log2n, the time is O(nlog2n) in the worst case and O(nlogn) on an average with the additional space requirement of O(n) bits. The algorithm is alphabet- independent: it uses only character comparisons, and the complexities do not depend on the alphabet size unless v does. A practical implementation is 23 times slower than one of the fastest and most space-efficient previous algorithms while needing only one-third of the main memory. The algorithm is based on suffix arrays, but unlike any other algorithm, it can construct the suffix array a small block at a time without storing the rest of the suffix array anywhere.

If you are more interested, the following code may come handy.


This project is an implementation of the blockwise Burrows-Wheeler transform algorithm described in “Fast BWT in small space by blockwise suffix sorting,” Karkkainen, (2007). This implementation is specialized for DNA sequences, meaning an alphabet consisting only of {A, C, G, T}. The advantage of the blockwise BWT over other algorithms is the space efficiency of the procedure, which is controlled by a user-specified parameter v. The complexity is O(nlogn

  • vn) time and O(nlogn/sqrt(v)) space in addition to storing the original string and the resulting BWT. Space efficiency is important in the analysis of DNA sequences, which can be long enough such that storing all of the sequences in memory is infeasible.

Also, Ben Langmead’s website has a set of slides with simple introduction.

Written by M. //