  We present an algorithm for building the extended BWT (eBWT) of a string
collection from its grammar-compressed representation. Our technique exploits
the string repetitions captured by the grammar to boost the computation of the
eBWT. Thus, the more repetitive the collection is, the lower are the resources
we use per input symbol. We rely on a new grammar recently proposed at DCC'21
whose nonterminals serve as building blocks for inducing the eBWT. A relevant
application for this idea is the construction of self-indexes for analyzing
sequencing reads -- massive and repetitive string collections of raw genomic
data. Self-indexes have become increasingly popular in Bioinformatics as they
can encode more information in less space. Our efficient eBWT construction
opens the door to perform accurate bioinformatic analyses on more massive
sequence datasets, which are not tractable with current eBWT construction
techniques.
