Compacting de Bruijn graphs from sequencing data quickly and in low memory

Chikhi, Rayan; Limasset, Antoine; Medvedev, Paul

doi:10.1093/bioinformatics/btw279

Cited by 209 publications

(262 citation statements)

References 40 publications

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“…Figure 1 shows the pipeline. We first self-correct the Illumina reads using Lighter [35], then build the de Bruijn graph using BCalm2 [36], align the long reads using GraphAligner with default parameters and finally extract the path as the corrected read. Due to fluctuations and biases of Illumina coverage, some genomic areas are impossible to correct with short reads even in principle.…”

Section: Error Correctionmentioning

confidence: 99%

GraphAligner: Rapid and Versatile Sequence-to-Graph Alignment

Rautiainen

Marschall

2019

Preprint

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Genome graphs can represent genetic variation and sequence uncertainty. Aligning sequences to genome graphs is key to many applications, including error correction, genome assembly, and genotyping of variants in a pan-genome graph. Yet, so far this step is often prohibitively slow. We present GraphAligner, a tool for aligning long reads to genome graphs. Compared to state-of-the-art tools, GraphAligner is 12x faster and uses 5x less memory, making it as efficient as aligning reads to linear reference genomes. When employing GraphAligner for error correction, we find it to be almost 3x more accurate and over 15x faster than extant tools.Availability: Package manager: https://anaconda.org/bioconda/graphaligner and source code: https://github.com/maickrau/GraphAligner

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Section: Error Correctionmentioning

confidence: 99%

GraphAligner: Rapid and Versatile Sequence-to-Graph Alignment

Rautiainen

Marschall

2019

Preprint

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“…Ntcard [24] is used to select the best-suited k-mer size. A compacted DBG is then constructed using Bcalm2 [25]. The Btrim [26] module cleans the graph, and the reads are finally mapped back on the de Bruijn graph using Bgreat2 [27].…”

Section: Dbg-based Reads Correctionmentioning

confidence: 99%

Toward perfect reads: short reads correction via mapping on compacted de Bruijn graphs

Limasset

Flot

Peterlongo³

2019

Preprint

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Motivations Short-read accuracy is important for downstream analyses such as genome assembly and hybrid long-read correction. Despite much work on short-read correction, present-day correctors either do not scale well on large data sets or consider reads as mere suites of k-mers, without taking into account their full-length read information. Results We propose a new method to correct short reads using de Bruijn graphs, and implement it as a tool called Bcool. As a first step, Bcool constructs a compacted de Bruijn graph from the reads. This graph is filtered on the basis of k-mer abundance then of unitig abundance, thereby removing most sequencing errors. The cleaned graph is then used as a reference on which the reads are mapped to correct them. We show that this approach yields more accurate reads than k-mer-spectrum correctors while being scalable to human-size genomic datasets and beyond. Availability and ImplementationThe implementation is open source and available at http: //github.com/Malfoy/BCOOL under the Affero GPL license and as a Bioconda package.

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“…Here we only consider local schemes, and exclude global schemes, such as the counting-based orderings used in Chikhi et al (2015, 2016). …”

Section: Introductionmentioning

confidence: 99%

Improving the performance of minimizers and winnowing schemes

et al. 2017

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MotivationThe minimizers scheme is a method for selecting k-mers from sequences. It is used in many bioinformatics software tools to bin comparable sequences or to sample a sequence in a deterministic fashion at approximately regular intervals, in order to reduce memory consumption and processing time. Although very useful, the minimizers selection procedure has undesirable behaviors (e.g. too many k-mers are selected when processing certain sequences). Some of these problems were already known to the authors of the minimizers technique, and the natural lexicographic ordering of k-mers used by minimizers was recognized as their origin. Many software tools using minimizers employ ad hoc variations of the lexicographic order to alleviate those issues.ResultsWe provide an in-depth analysis of the effect of k-mer ordering on the performance of the minimizers technique. By using small universal hitting sets (a recently defined concept), we show how to significantly improve the performance of minimizers and avoid some of its worse behaviors. Based on these results, we encourage bioinformatics software developers to use an ordering based on a universal hitting set or, if not possible, a randomized ordering, rather than the lexicographic order. This analysis also settles negatively a conjecture (by Schleimer et al.) on the expected density of minimizers in a random sequence.Availability and ImplementationThe software used for this analysis is available on GitHub: https://github.com/gmarcais/minimizers.git.

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Compacting de Bruijn graphs from sequencing data quickly and in low memory

Cited by 209 publications

References 40 publications

GraphAligner: Rapid and Versatile Sequence-to-Graph Alignment

GraphAligner: Rapid and Versatile Sequence-to-Graph Alignment

Toward perfect reads: short reads correction via mapping on compacted de Bruijn graphs

Improving the performance of minimizers and winnowing schemes

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