# Lightweight merging of compressed indices based on BWT variants

## TL;DR

This paper introduces a flexible, lightweight method for merging compressed indices based on BWT variants, enabling efficient large-scale index construction with limited memory, and extends merging techniques to new data structures.

## Contribution

It adapts existing BWT merging algorithms to include LCP arrays and introduces merging algorithms for compressed tries and circular indices, filling notable gaps in the field.

## Key findings

- Enables incremental large index construction with limited memory.
- Provides merging algorithms for compressed tries and circular indices.
- Extends BWT merging techniques to new data structures.

## Abstract

In this paper we propose a flexible and lightweight technique for merging compressed indices based on variants of Burrows-Wheeler transform (BWT), thus addressing the need for algorithms that compute compressed indices over large collections using a limited amount of working memory. Merge procedures make it possible to use an incremental strategy for building large indices based on merging indices for progressively larger subcollections. Starting with a known lightweight algorithm for merging BWTs [Holt and McMillan, Bionformatics 2014], we show how to modify it in order to merge, or compute from scratch, also the Longest Common Prefix (LCP) array. We then expand our technique for merging compressed tries and circular/permuterm compressed indices, two compressed data structures for which there were hitherto no known merging algorithms.