Succinct colored de Bruijn graphs

Muggli, Martin D.; Bowe, Alexander; Noyes, Noelle; Morley, Paul S.; Belk, K. E.; Raymond, Robert; Gagie, Travis; Puglisi, Simon J.; Boucher, Christina

doi:10.1093/bioinformatics/btx067

Cited by 103 publications

(115 citation statements)

References 37 publications

Supporting

Mentioning

115

Contrasting

Order By: Relevance

“…The resulting structure is usually referred to as a colored de Bruijn graph [19] and its representations have been widely studied ( [50][51][52][53][54][55][56][57][58][59][60][61] ). Even though we touched this setting in the section Multiple pan-genomes, exploiting the similarity between individual de Bruijn graphs for further compression in simplitig-based approaches is to be addressed in future work.…”

Section: Discussionmentioning

confidence: 99%

Simplitigs as an efficient and scalable representation of de Bruijn graphs

Břinda

Baym

Kucherov

2020

Preprint

View full text Add to dashboard Cite

MotivationDe Bruijn graphs play an essential role in computational biology, facilitating rapid alignment-free comparison of genomic datasets as well as reconstruction of underlying genomic sequences. Subsequently, an important question is how to efficiently represent, compress, and transmit de Bruijn graphs of the most common types of genomic data sets, such as sequencing reads, genomes, and pan-genomes. ResultsWe introduce simplitigs, an effective representation of de Bruijn graphs for alignment-free applications. Simplitigs are a generalization of unitigs and correspond to spellings of vertex-disjoint paths in a de Bruijn graph. We present an easy-to-plug-in greedy heuristic for their computation and provide a reference implementation in a program called ProphAsm. We use ProphAsm to compare the scaling of simplitigs and unitigs on a range of genomic datasets. We demonstrate that simplitigs are superior to unitigs in terms of the cumulative sequence length as well as of the number of sequences, and that they are sufficiently close to the theoretical bounds for practical applications. Finally, we demonstrate that, when combined with standard full-text indexes, simplitigs provide a scalable solution for k-mer search in pan-genomes. AvailabilityProphAsm is written in C++ and is available under the MIT license from De Bruijn graphs belong to the most popular graph representations of genomic datasets. They are defined as directed graphs where V is the set of all k-mers (i.e., subwords of a fixed length k) occurring in the V , ) G = ( E dataset with edges connecting a vertex v to a vertex w if there is a long prefix-suffix overlap between these v k − 1 and w. As follows from the definition, we can associate a de Bruijn graph with the underlying k-mer set and edges can be defined implicitly (unlike the edge-centric definition where k-mer sets are associated with edges [5] ). In this paper, we consider only vertex-centric graphs.De Bruijn graphs feature remarkable properties. First, their computation from data is easy and deterministic.Algorithms for enumerating and counting k-mers have been extensively studied and many programs are available [6][7][8][9] . If the datasets contain sequencing errors, the computation may also involve graph cleaning. This aims at removing those k-mers that are the result of sequencing errors and are due to their supposed randomness expected to be rare. Second, if k is chosen appropriately, de Bruijn graphs can capture substantial information about the entire molecules under sequencing as these correspond to (some of the) walks in the graphs, provided that sequencing was sufficiently deep. Third, de Bruijn graphs can be handled easily, which simplifies software development as well as dataset analysis and interpretation. These properties have led to a large variety of applications of de Bruijn graphs.De Bruijn graphs have been widely studied in the context of sequence assembly [10][11][12] . Here, their construction is typically the first step to the reconstruction of the genomes and transcr...

show abstract

Section: Discussionmentioning

confidence: 99%

Simplitigs as an efficient and scalable representation of de Bruijn graphs

Břinda

Baym

Kucherov

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…As a second baseline method, this representation concatenates all rows of A into a joint vector that is subsequently compressed using Elias-Fano encoding. This approach, for instance, is used by VARI [13] and its extensions [3].…”

Section: Binary Relation Representation Schemesmentioning

confidence: 99%

“…To support relating queries to information such as source genomes, haplotypes, or functional annotations, additional labels must be associated with the index. To facilitate this, approaches for storing additional data on an indexed graph have been suggested, such as the gPBWT [16] for storing haplotype information as genome graphs or succinct representations of labeled de Bruijn graphs [13,11,4] for the representation of sets of sequences. In this context, dynamic representations of such data have also recently received attention [14,19].…”

Section: Introductionmentioning

confidence: 99%

Sparse Binary Relation Representations for Genome Graph Annotation

Karasikov

Mustafa

Joudaki

et al. 2018

Preprint

View full text Add to dashboard Cite

High-throughput DNA sequencing data is accumulating in public repositories, and efficient approaches for storing and indexing such data are in high demand. In recent research, several graph data structures have been proposed to represent large sets of sequencing data and to allow for efficient querying of sequences. In particular, the concept of labeled de Bruijn graphs has been explored by several groups. While there has been good progress towards representing the sequence graph in small space, methods for storing a set of labels on top of such graphs are still not sufficiently explored. It is also currently not clear how characteristics of the input data, such as the sparsity and correlations of labels, can help to inform the choice of method to compress the graph labeling. In this work, we present a new compression approach, Multi-BRWT, which is adaptive to different kinds of input data. We show an up to 29% improvement in compression performance over the basic BRWT method, and up to a 68% improvement over the current state-of-the-art for de Bruijn graph label compression. To put our results into perspective, we present a systematic analysis of five different state-of-the-art annotation compression schemes, evaluate key metrics on both artificial and real-world data and discuss how different data characteristics influence the compression performance. We show that the improvements of our new method can be robustly reproduced for different representative real-world datasets.

show abstract

“…A radically different approach is to ignore the existing reference entirely, instead jointly assembling read data from multiple genomes and tracking read origin (Iqabl et al ., , Muggli et al ., ). This computationally elegant method allows the nodes and/or edges of the graph to be tagged with information, indicating which read dataset(s) support them.…”

Section: Introductionmentioning

confidence: 97%

Computational aspects underlying genome to phenome analysis in plants

et al. 2019

View full text Add to dashboard Cite

Summary Recent advances in genomics technologies have greatly accelerated the progress in both fundamental plant science and applied breeding research. Concurrently, high‐throughput plant phenotyping is becoming widely adopted in the plant community, promising to alleviate the phenotypic bottleneck. While these technological breakthroughs are significantly accelerating quantitative trait locus (QTL) and causal gene identification, challenges to enable even more sophisticated analyses remain. In particular, care needs to be taken to standardize, describe and conduct experiments robustly while relying on plant physiology expertise. In this article, we review the state of the art regarding genome assembly and the future potential of pangenomics in plant research. We also describe the necessity of standardizing and describing phenotypic studies using the Minimum Information About a Plant Phenotyping Experiment (MIAPPE) standard to enable the reuse and integration of phenotypic data. In addition, we show how deep phenotypic data might yield novel trait−trait correlations and review how to link phenotypic data to genomic data. Finally, we provide perspectives on the golden future of machine learning and their potential in linking phenotypes to genomic features.

show abstract

Succinct colored de Bruijn graphs

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 103 publications

References 37 publications

Simplitigs as an efficient and scalable representation of de Bruijn graphs

Simplitigs as an efficient and scalable representation of de Bruijn graphs

Sparse Binary Relation Representations for Genome Graph Annotation

Computational aspects underlying genome to phenome analysis in plants

Contact Info

Product

Resources

About