An External-Memory Algorithm for String Graph Construction

Bonizzoni, Paola; Vedova, Gianluca Della; Pirola, Yuri; Previtali, Marco; Rizzi, Raffaella

doi:10.1007/s00453-016-0165-4

Cited by 10 publications

(4 citation statements)

References 34 publications

(65 reference statements)

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“…Furthermore, fingerprints are numerical sequences shorter than the represented character sequences and we also expect that they are resilient to errors occurring in the reads (especially in long reads). The first application is related to genome assembly based on the use of an overlap graph which is constructed by detecting the overlaps between genomic reads [11,12]. When dealing with long reads this task is further complicated by the length of the reads and the high sequencing error rate.…”

Section: Some Applications: Representing and Querying Read Sequencesmentioning

confidence: 99%

Can Formal Languages Help Pangenomics to Represent and Analyze Multiple Genomes?

Bonizzoni

Felice

Pirola

et al. 2022

Developments in Language Theory

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Graph pangenomics is a new emerging field in computational biology that is changing the traditional view of a reference genome from a linear sequence to a new paradigm: a sequence graph (pangenome graph or simply pangenome) that represents the main similarities and differences in multiple evolutionary related genomes. The speed in producing large amounts of genome data, driven by advances in sequencing technologies, is far from the slow progress in developing new methods for constructing and analyzing a pangenome. Most recent advances in the field are still based on notions rooted in established and quite old literature on combinatorics on words, formal languages and space efficient data structures. In this paper we discuss two novel notions that may help in managing and analyzing multiple genomes by addressing a relevant question: how can we summarize sequence similarities and dissimilarities in large sequence data? The first notion is related to variants of the Lyndon factorization and allows to represent sequence similarities for a sample of reads, while the second one is that of sample specific string as a tool to detect differences in a sample of reads. New perspectives opened by these two notions are discussed.

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Section: Some Applications: Representing and Querying Read Sequencesmentioning

confidence: 99%

Can Formal Languages Help Pangenomics to Represent and Analyze Multiple Genomes?

Bonizzoni

Felice

Pirola

et al. 2022

Developments in Language Theory

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“…Their algorithm for transitive reduction is similar to that of Myers'. Bonizzoni et al [15] propose a similar approach using only the FM-index of the input sequence to create a string graph and perform a transitive reduction with a different but equivalent formulation than Myers' [19].…”

Section: Related Workmentioning

confidence: 99%

Parallel String Graph Construction and Transitive Reduction for De Novo Genome Assembly

Guidi¹,

Selvitopi²,

Ellis³

et al. 2020

Preprint

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One of the most computationally intensive tasks in computational biology is de novo genome assembly, the decoding of the sequence of an unknown genome from redundant and erroneous short sequences. A common assembly paradigm identifies overlapping sequences, simplifies their layout, and creates consensus. Despite many algorithms developed in the literature, the efficient assembly of large genomes is still an open problem.In this work, we introduce new distributed-memory parallel algorithms for overlap detection and layout simplification steps of de novo genome assembly, and implement them in the diBELLA 2D pipeline. Our distributed memory algorithms for both overlap detection and layout simplification are based on linear-algebra operations over semirings using 2D distributed sparse matrices. Our layout step consists of performing a transitive reduction from the overlap graph to a string graph. We provide a detailed communication analysis of the main stages of our new algorithms.diBELLA 2D achieves near linear scaling with over 80% parallel efficiency for the human genome, reducing the runtime for overlap detection by 1.2-1.3× × × for the human genome and 1.5-1.9× × × for C.elegans compared to the state-of-the-art. Our transitive reduction algorithm outperforms an existing distributed-memory implementation by 10.5-13.3× × × for the human genome and 18-29× × × for the C. elegans. Our work paves the way for efficient de novo assembly of large genomes using long reads in distributed memory.

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“…In (Myers, 2005) redundant arcs are those arcs (r, s) labeled by γ, for γ containing as prefix the label of an arc (r, t). In (Bonizzoni et al, 2016b) we state an equivalent characterization of string graphs (given below) which is a direct consequence of the fact that an arc (r i , r j ) is labeled by the left extension α and its assembly is αr j . An arc e 1 = (r i , r j ) of G O labeled by α is transitive (or reducible) if there exists another arc e 2 = (r k , r j ) labeled by δ where δ is a suffix of α.…”

Section: Preliminariesmentioning

confidence: 99%

FSG: Fast String Graph Construction for De Novo Assembly

Bonizzoni

Vedova

Pirola

et al. 2017

Journal of Computational Biology

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The string graph for a collection of next-generation reads is a lossless data representation that is fundamental for de novo assemblers based on the overlap-layout-consensus paradigm. In this article, we explore a novel approach to compute the string graph, based on the FM-index and Burrows and Wheeler Transform. We describe a simple algorithm that uses only the FM-index representation of the collection of reads to construct the string graph, without accessing the input reads. Our algorithm has been integrated into the string graph assembler (SGA) as a standalone module to construct the string graph. The new integrated assembler has been assessed on a standard benchmark, showing that fast string graph (FSG) is significantly faster than SGA while maintaining a moderate use of main memory, and showing practical advantages in running FSG on multiple threads. Moreover, we have studied the effect of coverage rates on the running times.

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An External-Memory Algorithm for String Graph Construction

Cited by 10 publications

References 34 publications

Can Formal Languages Help Pangenomics to Represent and Analyze Multiple Genomes?

Can Formal Languages Help Pangenomics to Represent and Analyze Multiple Genomes?

Parallel String Graph Construction and Transitive Reduction for De Novo Genome Assembly

FSG: Fast String Graph Construction for De Novo Assembly

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