TwoPaCo: an efficient algorithm to build the compacted de Bruijn graph from many complete genomes

Minkin, Ilya; Pham, Son; Medvedev, Paul

doi:10.1093/bioinformatics/btw609

Cited by 79 publications

(99 citation statements)

References 54 publications

(53 reference statements)

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“…, v n−1 → v n }. We efficiently construct the compacted graph using our previously published algorithm TwoPaCo (Minkin et al, 2017).…”

Section: Other Considerationsmentioning

confidence: 99%

“…If s is a string, then lets be its reverse complement. We handle double strandedness in the natural way by using the comprehensive de Bruijn graph, which is defined as G comp (s, k) = G(s, k) ∪ G(s, k) (Minkin et al, 2017). Our algorithm and corresponding data structures can be modified to work with the comprehensive graph with a few minor changes which we omit here.…”

Section: Other Considerationsmentioning

confidence: 99%

“…For example, Sibelia can handle repeats and works for many bacterial genomes; unfortunately, it does not scale to larger genomes. However, the last three years has seen a breakthrough in the efficiency of de Bruijn graph construction algorithms Chikhi et al, 2016;Baier et al, 2016;Minkin et al, 2017). The latest methods can construct the graph for tens of mammalian genomes in minutes rather than weeks.…”

Section: Introductionmentioning

confidence: 99%

“…Further, we extend SibeliaZ-LCB into a multiple whole-genome aligner called SibeliaZ. SibeliaZ works by first constructing the compacted de Bruijn graph using our previoulsy published TwoPaCo tool (Minkin et al, 2017), then finding locally collinear blocks using using SibeliaZ-LCB, and finally, running a multiple-sequence aligner (spoa, Lee et al (2002)) on each of the found blocks. To demonstrate the scalability and accuracy of our method, we compute the multiple whole-genome alignment for a collection of recently assembled strains of mice.…”

Section: Introductionmentioning

confidence: 99%

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Scalable multiple whole-genome alignment and locally collinear block construction with SibeliaZ

Minkin

Medvedev

2019

Preprint

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Multiple whole-genome alignment is a fundamental and challenging problems in bioinformatics. Despite many ongoing successes, today's methods are not able to keep up with the growing number, length, and complexity of assembled genomes. Approaches based on using compacted de Bruijn graphs to identify and extend anchors into locally collinear blocks hold the potential for scalability, but current algorithms still do not scale to mammalian genomes. We present a novel algorithm SibeliaZ-LCB for identifying collinear blocks in closely related genomes based on the analysis of the de Bruijn graph. We further incorporate it into a multiple whole-genome alignment pipeline called SibeliaZ. SibeliaZ shows drastic run-time improvements over other methods on both simulated and real data, with only a limited decrease in accuracy. On sixteen recently assembled strains of mice, SibeliaZ runs in under 12 hours, while other tools could not run to completion for even eight mice, given a week. SibeliaZ makes a significant step towards improving scalability of multiple whole-genome alignment and collinear block reconstruction algorithms and will enable many comparative genomics studies in the near future.

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“…, v n−1 → v n }. We efficiently construct the compacted graph using our previously published algorithm TwoPaCo (Minkin et al, 2017).…”

Section: Other Considerationsmentioning

confidence: 99%

Section: Other Considerationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Scalable multiple whole-genome alignment and locally collinear block construction with SibeliaZ

Minkin

Medvedev

2019

Preprint

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“…Despite serving as a building block for many methods in computational biology, the de Bruijn graph adoption is hindered by two factors. First, the memory usage and computational requirements for building de Bruijn graphs from raw sequencing reads are considerable compared to alignment to a reference genome while only a handful of tools have focused on de Bruijn graph compaction (Minkin et al, 2016;Chikhi et al, 2016;Marcus et al, 2014;Baier et al, 2016;Minkin et al, 2013). Second, de Bruijn graph construction usually requires tight integration with the code.…”

Section: Introductionmentioning

confidence: 99%

Bifrost – Highly parallel construction and indexing of colored and compacted de Bruijn graphs

Holley

Melsted

2019

Preprint

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Motivation: De Bruijn graphs are the core data structure for a wide range of assemblers and genome analysis software processing High Throughput Sequencing datasets. For population genomic analysis, the colored de Bruijn graph is often used in order to take advantage of the massive sets of sequenced genomes available for each species. However, memory consumption of tools based on the de Bruijn graph is often prohibitive, due to the high number of vertices, edges or colors in the graph. In order to process large and complex genomes, most short-read assemblers based on the de Bruijn graph paradigm reduce the assembly complexity and memory usage by compacting first all maximal non-branching paths of the graph into single vertices. Yet, de Bruijn graph compaction is challenging as it requires the uncompacted de Bruijn graph to be available in memory. Results:We present a new parallel and memory efficient algorithm enabling the direct construction of the compacted de Bruijn graph without producing the intermediate uncompacted de Bruijn graph. Bifrost features a broad range of functions such as sequence querying, storage of user data alongside vertices and graph editing that automatically preserve the compaction property. Bifrost makes full use of the dynamic index efficiency and proposes a graph coloring method efficiently mapping each k-mer of the graph to the set of genomes in which it occurs. Experimental results show that our algorithm is competitive with state-of-the-art de Bruijn graph compaction and coloring tools. Bifrost was able to build the colored and compacted de Bruijn graph of about 118,000 Salmonella genomes on a mid-class server in about 4 days using 103 GB of main memory.Availability: https://github.com

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Applications of de Bruijn graphs in microbiome research

Dufault‐Thompson

2022

iMeta

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High‐throughput sequencing has become an increasingly central component of microbiome research. The development of de Bruijn graph‐based methods for assembling high‐throughput sequencing data has been an important part of the broader adoption of sequencing as part of biological studies. Recent advances in the construction and representation of de Bruijn graphs have led to new approaches that utilize the de Bruijn graph data structure to aid in different biological analyses. One type of application of these methods has been in alternative approaches to the assembly of sequencing data like gene‐targeted assembly, where only gene sequences are assembled out of larger metagenomes, and differential assembly, where sequences that are differentially present between two samples are assembled. de Bruijn graphs have also been applied for comparative genomics where they can be used to represent large sets of multiple genomes or metagenomes where structural features in the graphs can be used to identify variants, indels, and homologous regions in sequences. These de Bruijn graph‐based representations of sequencing data have even begun to be applied to whole sequencing databases for large‐scale searches and experiment discovery. de Bruijn graphs have played a central role in how high‐throughput sequencing data is worked with, and the rapid development of new tools that rely on these data structures suggests that they will continue to play an important role in biology in the future.

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TwoPaCo: an efficient algorithm to build the compacted de Bruijn graph from many complete genomes

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 79 publications

References 54 publications

Scalable multiple whole-genome alignment and locally collinear block construction with SibeliaZ

Scalable multiple whole-genome alignment and locally collinear block construction with SibeliaZ

Bifrost – Highly parallel construction and indexing of colored and compacted de Bruijn graphs

Applications of de Bruijn graphs in microbiome research

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