  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.
