Safe and Complete Contig Assembly Via Omnitigs

Tomescu, Alexandru I.; Medvedev, Paul

doi:10.1007/978-3-319-31957-5_11

Cited by 12 publications

(13 citation statements)

References 35 publications

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“…With this definition, a set of maximal non-branched paths can be derived from

. Here, a maximal non-branch path indicates a path that meets the following conditions: (i) for the first vertex, the in-degree is 0 or >1, and the out-degree is 1; (ii) for the last vertex, the out-degree is 0 or >1, and the in-degree 1; (iii) for all the other internal vertices, the in- and out-degrees are exactly 1 ( Tomescu and Medvedev, 2016 ). Such paths are usually termed as ‘unipaths’ or ‘unitigs’, which are commonly used in the de novo assembly of genome ( Gnerre et al , 2011 ; Zimin et al , 2013 ; Tomescu and Medvedev, 2016 ).…”

Section: Methodsmentioning

confidence: 99%

deBWT: parallel construction of Burrows–Wheeler Transform for large collection of genomes with de Bruijn-branch encoding

2016

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Motivation: With the development of high-throughput sequencing, the number of assembled genomes continues to rise. It is critical to well organize and index many assembled genomes to promote future genomics studies. Burrows–Wheeler Transform (BWT) is an important data structure of genome indexing, which has many fundamental applications; however, it is still non-trivial to construct BWT for large collection of genomes, especially for highly similar or repetitive genomes. Moreover, the state-of-the-art approaches cannot well support scalable parallel computing owing to their incremental nature, which is a bottleneck to use modern computers to accelerate BWT construction.Results: We propose de Bruijn branch-based BWT constructor (deBWT), a novel parallel BWT construction approach. DeBWT innovatively represents and organizes the suffixes of input sequence with a novel data structure, de Bruijn branch encoding. This data structure takes the advantage of de Bruijn graph to facilitate the comparison between the suffixes with long common prefix, which breaks the bottleneck of the BWT construction of repetitive genomic sequences. Meanwhile, deBWT also uses the structure of de Bruijn graph for reducing unnecessary comparisons between suffixes. The benchmarking suggests that, deBWT is efficient and scalable to construct BWT for large dataset by parallel computing. It is well-suited to index many genomes, such as a collection of individual human genomes, with multiple-core servers or clusters.Availability and implementation: deBWT is implemented in C language, the source code is available at https://github.com/hitbc/deBWT or https://github.com/DixianZhu/deBWTContact: ydwang@hit.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online.

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“…With this definition, a set of maximal non-branched paths can be derived from

Section: Methodsmentioning

confidence: 99%

deBWT: parallel construction of Burrows–Wheeler Transform for large collection of genomes with de Bruijn-branch encoding

2016

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“…A set of maximal non-branched paths (unipaths) can be derived from D. Each of the unipaths meets the following conditions: i) for the first vertex, the indegree is 0 or >1, and the out-degree is 1; ii) for the last vertex, the outdegree is 0 or >1, and the in-degree 1; iii) for all the other vertices along the path, the in-and out-degrees are exactly 1. This definition follows previous studies (Tomescu and Medvedev, 2016), and the string implied by compacting a certain unipath is called a "unitig" (Gnerre et al, 2011;Zimin et al, 2013).…”

Section: Preliminarymentioning

confidence: 97%

deGSM: Memory Scalable Construction Of Large Scale de Bruijn Graph

Guo

Gao

et al. 2021

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Motivation: De Bruijn graph, a fundamental data structure to represent and organize genome sequence, plays important roles in various kinds of sequence analysis tasks such as de novo assembly, high-throughput sequencing (HTS) read alignment, pan-genome analysis, metagenomics analysis, HTS read correction, etc. With the rapid development of HTS data and ever-increasing number of assembled genomes, there is a high demand to construct de Bruijn graph for sequences up to Tera-basepair level. It is non-trivial since the size of the graph to be constructed could be very large and each graph consists of hundreds of billions of vertices and edges. Current existing approaches may have unaffordable memory footprints to handle such a large de Bruijn graph. Moreover, it also requires the construction approach to handle very large dataset efficiently, even if in a relatively small RAM space. Results: We propose a lightweight parallel de Bruijn graph construction approach, de Bruijn Graph Constructor in Scalable Memory (deGSM). The main idea of deGSM is to efficiently construct the Burrows-Wheeler Transformation (BWT) of the unipaths of de Bruijn graph in constant RAM space and transform the BWT into the original unitigs. It is mainly implemented by a fast parallel external sorting of k-mers, which allows only a part of k-mers kept in RAM by a novel organization of the k-mers. The experimental results demonstrate that, just with a commonly used machine, deGSM is able to handle very large genome sequence(s), e.g., the contigs (305 Gbp) and scaffolds (1.1 Tbp) recorded in Gen-Bank database and Picea abies HTS dataset (9.7 Tbp). Moreover, deGSM also has faster or comparable construction speed compared with state-of-the-art approaches. With its high scalability and efficiency, deGSM has enormous potentials in many large scale genomics studies.

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“…The idea of this paper originates from a more general research question in bioinformatics about partial solutions common to all solutions. The typical example is the contig assembly problem, where contigs are those strings that are part of all possible genomic reconstructions from an assembly graph (be they Eulerian, Hamiltonian, or just node/ edge covering paths)-see e.g., [33]. Another example is the sequence alignment problem, where some studies consider reliable partial alignments [12], [16], [34], or base-pairings common to all optimal or almost-optimal alignments [4], [38].…”

Section: Previous Work On Safe Solutionsmentioning

confidence: 99%

“…In this paper we address the gap filling problem with the "safe and complete" framework proposed in [33] for the contig assembly problem. This framework defines an algorithm for a problem to be: (i) safe, if it returns only partial solutions that are common to all solutions to the problem (these common partial solutions are also called safe), and (ii) complete, if it returns all safe solutions.…”

Section: Introductionmentioning

confidence: 99%

Safely Filling Gaps with Partial Solutions Common to All Solutions

Salmela

Tomescu

2019

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Gap filling has emerged as a natural sub-problem of many de novo genome assembly projects. The gap filling problem generally asks for an path in an assembly graph whose length matches the gap length estimate. Several methods have addressed it, but only few have focused on strategies for dealing with multiple gap filling solutions and for guaranteeing reliable results. Such strategies include reporting only unique solutions, or exhaustively enumerating all filling solutions and heuristically creating their consensus. Our main contribution is a new method for reliable gap filling: filling gaps with those sub-paths common to all gap filling solutions. We call these partial solutions safe, following the framework of (Tomescu and Medvedev, RECOMB 2016). We give an efficient safe algorithm running in time and space, where is the gap length estimate and is the number of edges of the assembly graph. To show the benefits of this method, we implemented this algorithm for the problem of filling gaps in scaffolds. Our experimental results on bacterial and on conservative human assemblies show that, on average, our method can retrieve over 73% more safe and correct bases as compared to previous methods, with a similar precision.

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Safe and Complete Contig Assembly Via Omnitigs

Cited by 12 publications

References 35 publications

deBWT: parallel construction of Burrows–Wheeler Transform for large collection of genomes with de Bruijn-branch encoding

deBWT: parallel construction of Burrows–Wheeler Transform for large collection of genomes with de Bruijn-branch encoding

deGSM: Memory Scalable Construction Of Large Scale de Bruijn Graph

Safely Filling Gaps with Partial Solutions Common to All Solutions

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