Optimal-Time Mapping in Run-Length Compressed PBWT

TL;DR

This paper introduces a new data structure for the multi-allelic PBWT that supports constant-time forward and backward steps within run-length encoded space, improving haplotype retrieval and prefix search efficiency.

Contribution

It presents the first $O( ewR)$-word data structure supporting both forward and backward steps in the run-length encoded PBWT, extending its applicability to general alphabets.

Findings

Supports constant-time forward and backward stepping in run-length encoded PBWT.

Enables haplotype retrieval in $O(rac{ ewR}{ ext{word}} imes ext{log log}_ ext{word} h + ext{width})$ time.

Provides efficient prefix search with $O( ext{height} + ewR)$ space and $O(m' imes ext{log log}_ ext{word} \sigma + ext{occ})$ query time.

Abstract

The Positional Burrows--Wheeler Transform (PBWT) is a data structure designed for efficiently representing and querying large collections of sequences, such as haplotype panels in genomics. Forward and backward stepping operations -- analogues to LF- and FL-mapping in the traditional BWT -- are fundamental to the PBWT, underpinning many algorithms based on the PBWT for haplotype matching and related analyses. Although the run-length encoded variant of the PBWT (also known as the $\mu$-PBWT) achieves $O(\newR)$-word space usage, where $\newR$ is the total number of runs, no data structure supporting both forward and backward stepping in constant time within this space bound was previously known. In this paper, we consider the multi-allelic PBWT that is extended from its original binary form to a general ordered alphabet $\{0, \dots, \sigma-1\}$. We first establish bounds on the size $\newR$ and then introduce a new $O(\newR)$-word data structure built over a list of haplotypes $\{S_1, \dots, S_\height\}$, each of length $\width$, that supports constant-time forward and backward stepping. We further revisit two key applications -- haplotype retrieval and prefix search -- leveraging our efficient forward stepping technique. Specifically, we design an $O(\newR)$-word space data structure that supports haplotype retrieval in $O(\log \log_{\word} h + \width)$ time. For prefix search, we present an $O(\height + \newR)$-word data structure that answers queries in $O(m' \log\log_{\word} \sigma + \occ)$ time, where $m'$ denotes the length of the longest common prefix returned and $\occ$ denotes the number of haplotypes prefixed the longest prefix.