0000000000497577

AUTHOR

Nicola Prezza

showing 5 related works from this author

Novel Results on the Number of Runs of the Burrows-Wheeler-Transform

2021

The Burrows-Wheeler-Transform (BWT), a reversible string transformation, is one of the fundamental components of many current data structures in string processing. It is central in data compression, as well as in efficient query algorithms for sequence data, such as webpages, genomic and other biological sequences, or indeed any textual data. The BWT lends itself well to compression because its number of equal-letter-runs (usually referred to as $r$) is often considerably lower than that of the original string; in particular, it is well suited for strings with many repeated factors. In fact, much attention has been paid to the $r$ parameter as measure of repetitiveness, especially to evalua…

FOS: Computer and information sciencesBurrows–Wheeler transformSettore INF/01 - InformaticaCombinatorics on wordsFormal Languages and Automata Theory (cs.FL)Computer scienceString (computer science)Search engine indexingCompressed data structuresComputer Science - Formal Languages and Automata TheoryString indexingData structureMeasure (mathematics)Burrows-Wheeler-TransformRepetitivenessCombinatorics on wordsBurrows-Wheeler-Transform Compressed data structures String indexing Repetitiveness Combinatorics on wordsTransformation (function)Computer Science - Data Structures and AlgorithmsData Structures and Algorithms (cs.DS)AlgorithmData compression
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Detecting mutations by eBWT

2018

In this paper we develop a theory describing how the extended Burrows-Wheeler Transform (eBWT) of a collection of DNA fragments tends to cluster together the copies of nucleotides sequenced from a genome G. Our theory accurately predicts how many copies of any nucleotide are expected inside each such cluster, and how an elegant and precise LCP array based procedure can locate these clusters in the eBWT. Our findings are very general and can be applied to a wide range of different problems. In this paper, we consider the case of alignment-free and reference-free SNPs discovery in multiple collections of reads. We note that, in accordance with our theoretical results, SNPs are clustered in th…

0301 basic medicineFOS: Computer and information sciences000 Computer science knowledge general worksBWT LCP Array SNPs Reference-free Assembly-freeLCP ArraySettore INF/01 - Informatica[SDV]Life Sciences [q-bio]Reference-freeAssembly-freeSNP03 medical and health sciences030104 developmental biologyBWTBWT; LCP Array; SNPs; Reference-free; Assembly-freeComputer ScienceComputer Science - Data Structures and AlgorithmsData Structures and Algorithms (cs.DS)[INFO]Computer Science [cs]SoftwareSNPs
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Adaptive learning of compressible strings

2020

Suppose an oracle knows a string $S$ that is unknown to us and that we want to determine. The oracle can answer queries of the form "Is $s$ a substring of $S$?". In 1995, Skiena and Sundaram showed that, in the worst case, any algorithm needs to ask the oracle $\sigma n/4 -O(n)$ queries in order to be able to reconstruct the hidden string, where $\sigma$ is the size of the alphabet of $S$ and $n$ its length, and gave an algorithm that spends $(\sigma-1)n+O(\sigma \sqrt{n})$ queries to reconstruct $S$. The main contribution of our paper is to improve the above upper-bound in the context where the string is compressible. We first present a universal algorithm that, given a (computable) compre…

FOS: Computer and information sciencesCentroid decompositionGeneral Computer ScienceString compressionAdaptive learningKolmogorov complexityContext (language use)Data_CODINGANDINFORMATIONTHEORYString reconstructionTheoretical Computer ScienceCombinatoricsString reconstruction; String learning; Adaptive learning; Kolmogorov complexity; String compression; Lempel-Ziv; Centroid decomposition; Suffix treeSuffix treeIntegerComputer Science - Data Structures and AlgorithmsOrder (group theory)Data Structures and Algorithms (cs.DS)Adaptive learning; Centroid decomposition; Kolmogorov complexity; Lempel-Ziv; String compression; String learning; String reconstruction; Suffix treeTime complexityComputer Science::DatabasesMathematicsLempel-ZivSettore INF/01 - InformaticaLinear spaceString (computer science)SubstringBounded functionString learningTheoretical Computer Science
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Variable-order reference-free variant discovery with the Burrows-Wheeler Transform

2020

Abstract Background In [Prezza et al., AMB 2019], a new reference-free and alignment-free framework for the detection of SNPs was suggested and tested. The framework, based on the Burrows-Wheeler Transform (BWT), significantly improves sensitivity and precision of previous de Bruijn graphs based tools by overcoming several of their limitations, namely: (i) the need to establish a fixed value, usually small, for the order k, (ii) the loss of important information such as k-mer coverage and adjacency of k-mers within the same read, and (iii) bad performance in repeated regions longer than k bases. The preliminary tool, however, was able to identify only SNPs and it was too slow and memory con…

Burrows–Wheeler transformComputer science[SDV]Life Sciences [q-bio]Value (computer science)SNPAssembly-free0102 computer and information scienceslcsh:Computer applications to medicine. Medical informatics01 natural sciencesBiochemistryPolymorphism Single Nucleotide03 medical and health sciencesBWTChromosome (genetic algorithm)Structural BiologyHumansSensitivity (control systems)Molecular Biologylcsh:QH301-705.5Alignment-free; Assembly-free; BWT; INDEL; SNP030304 developmental biologyAlignment-free; Assembly-free; BWT; INDEL; SNP;De Bruijn sequence0303 health sciencesSettore INF/01 - InformaticaAlignment-freeApplied MathematicsResearchGenomicsSequence Analysis DNAINDELData structureGraphComputer Science ApplicationsVariable (computer science)lcsh:Biology (General)010201 computation theory & mathematicsAdjacency listlcsh:R858-859.7Suffix[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]AlgorithmAlgorithmsBMC Bioinformatics
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SNPs detection by eBWT positional clustering

2019

Sequencing technologies keep on turning cheaper and faster, thus putting a growing pressure for data structures designed to efficiently store raw data, and possibly perform analysis therein. In this view, there is a growing interest in alignment-free and reference-free variants calling methods that only make use of (suitably indexed) raw reads data. We develop the positional clustering theory that (i) describes how the extended Burrows–Wheeler Transform (eBWT) of a collection of reads tends to cluster together bases that cover the same genome position (ii) predicts the size of such clusters, and (iii) exhibits an elegant and precise LCP array based procedure to locate such clusters in the e…

lcsh:QH426-470Computer scienceLCP arrayReference-free[SDV]Life Sciences [q-bio]0206 medical engineeringSequencing dataSNPAssembly-free02 engineering and technologyBWT LCP array SNPs Reference-free Assembly-freecomputer.software_genreSoftwareBWTStructural BiologyComputational Theory and MathematicCluster (physics)Cluster analysislcsh:QH301-705.5Molecular BiologyComputingMilieux_MISCELLANEOUSSettore INF/01 - Informaticabusiness.industryResearchApplied MathematicsLCP arrayData structurePipeline (software)lcsh:GeneticsComputational Theory and Mathematicslcsh:Biology (General)Data miningBWT; LCP array; SNPs; Reference-free; Assembly-free[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]businessRaw datacomputer020602 bioinformaticsSNPs
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