Teaching the Burrows-Wheeler Transform via the Positional Burrows-Wheeler Transform

TL;DR

This paper advocates teaching the Positional Burrows-Wheeler Transform (PBWT) before the classic BWT to improve understanding, demonstrating how PBWT relates to efficient string searches and how it can lead to the BWT and FM-index.

Contribution

It introduces the PBWT as a pedagogical tool for explaining the BWT and FM-index, highlighting its relation to radix sort and string cyclic shifts for efficient pattern matching.

Findings

PBWT can be used for fast positional search on string sets

Prefix search is a special case of positional search

Cyclic shifts of a string's PBWT yield the BWT of the string

Abstract

The Burrows-Wheeler Transform (BWT) is often taught in undergraduate courses on algorithmic bioinformatics, because it underlies the FM-index and thus important tools such as Bowtie and BWA. Its admirers consider the BWT a thing of beauty but, despite thousands of pages being written about it over nearly thirty years, to undergraduates seeing it for the first time it still often seems like magic. Some who persevere are later shown the Positional BWT (PBWT), which was published twenty years after the BWT. In this paper we argue that the PBWT should be taught {\em before} the BWT. We first use the PBWT's close relation to a right-to-left radix sort to explain how to use it as a fast and space-efficient index for {\em positional search} on a set of strings (that is, given a pattern and a position, quickly list the strings containing that pattern starting in that position). We then observe that {\em prefix search} (listing all the strings that start with the pattern) is an easy special case of positional search, and that prefix search on the suffixes of a single string is equivalent to {\em substring search} in that string (listing all the starting positions of occurrences of the pattern in the string). Storing na\"ively a PBWT of the suffixes of a string is space-{\em inefficient} but, in even reasonably small examples, most of its columns are nearly the same. It is not difficult to show that if we store a PBWT of the cyclic shifts of the string, instead of its suffixes, then all the columns are exactly the same -- and equal to the BWT of the string. Thus we can teach the BWT and the FM-index via the PBWT.