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Compressed Indexes for Regular Languages with Applications to Computational Pan-genomics

Project description

Advancing computational pangenomics via graph indexing

In the past, analysis of the human genome required a plethora of algorithmic tools which allowed for example the identification of genetic variations. However, using a single reference genome does not comply with the genetic variability of a population, and computational scientists are shifting towards pangenome graphs to represent variation. Pangenomes were originally proposed in the context of microbial analysis and describe commonly present and frequently absent genes and open reading frames in a population. The EU-funded REGINDEX project aims to advance the current knowledge of indexing data structures for graphs and extend the applicability of graph-based pangenome representations.

Objective

Sorting is, arguably, the most powerful algorithmic primitive when it comes to indexing data. At the same time, the regularities exposed by sorting are precisely those enabling data compression. In the last two decades, this fascinating duality has led researchers to the design of compressed full-text indexes: data structures supporting fast pattern matching queries over compressed text. In this project, we revisit the natural generalization of the problem to labeled graphs from a new perspective: we interpret graphs as finite-state automata and investigate the connections existing between their propensity to be sorted and the languages they recognize. Our novel language-theoretic approach makes it possible to transfer fundamental results between the mature fields of regular language theory and compressed text indexing. We aim at building this bridge by developing a new theory of compressed regular language indexing. This project finds fundamental applications to the rapidly-expanding field of computational pan-genomics, where the goal is to study the variations contained in the genomes of an entire population. Recent research has shown that representing pan-genomes as labeled graphs is an important step to reduce reference allele bias. Existing approaches, however, can index only restricted classes of graphs, thereby limiting the practical applicability of such powerful pan-genome representations. Our innovative approach, based on sorting regular languages by partial co-lexicographic orders, changes the perspective from which the compressed indexing problem has been tackled in the literature. This project aims at developing a theory of graph indexing and compression based on the natural interplay between sorting and regular language theory. We will apply these findings inside practical tools for aligning arbitrarily-long DNA fragments against compressed pan-genome graphs.

Fields of science (EuroSciVoc)

Keywords

Programme(s)

Topic(s)

Funding Scheme

HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

(opens in new window) ERC-2021-STG

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Host institution

UNIVERSITA CA' FOSCARI VENEZIA
Net EU contribution
€ 1 385 743,00
Address
DORSODURO 3246
30123 Venezia
Italy
Region
Nord-Est Veneto Venezia
Activity type
Higher or Secondary Education Establishments
Links
Total cost
€ 1 385 743,75

Beneficiaries (1)