Elastic Founder Graphs Improved and Enhanced
Nicola Rizzo, Massimo Equi, Tuukka Norri, Veli M\"akinen

TL;DR
This paper advances the Elastic Founder Graph framework for pangenomic pattern matching by improving index space efficiency, developing faster construction algorithms, and extending its capabilities with BWT-based indexing and document listing functionalities.
Contribution
It introduces space-efficient indexing, linear-time construction algorithms, and versatile extensions of the Elastic Founder Graph framework for enhanced pattern matching in pangenomics.
Findings
Improved the EFG index to answer queries in linear time relative to edge labels.
Developed $O(mn)$-time algorithms for constructing EFGs with optimized metrics.
Extended EFG framework with BWT-based index and document listing capabilities.
Abstract
Indexing labeled graphs for pattern matching is a central challenge of pangenomics. Equi et al. (Algorithmica, 2022) developed the Elastic Founder Graph () representing an alignment of sequences of length , drawn from alphabet plus the special gap character: the paths spell the original sequences or their recombination. By enforcing the semi-repeat-free property, the admits a polynomial-space index for linear-time pattern matching, breaking through the conditional lower bounds on indexing labeled graphs (Equi et al., SOFSEM 2021). In this work we improve the space of the index answering pattern matching queries in linear time, from linear in the length of all strings spelled by three consecutive node labels, to linear in the size of the edge labels. Then, we develop linear-time construction algorithms optimizing for different…
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Taxonomy
TopicsAlgorithms and Data Compression · Natural Language Processing Techniques · Genomics and Phylogenetic Studies
