scholarly journals Metagenome SNP calling via read-colored de Bruijn graphs

2020 ◽  
Author(s):  
Bahar Alipanahi ◽  
Martin D Muggli ◽  
Musa Jundi ◽  
Noelle R Noyes ◽  
Christina Boucher
Keyword(s):  
Pathogenic Bacteria ◽  
Read Length ◽  
Supplementary Information ◽  
De Bruijn Graph ◽  
Nucleotide Polymorphisms ◽  
Chromosomal Dna ◽  
Shotgun Metagenomics ◽  
De Bruijn Graphs ◽  
De Bruijn ◽  
Colored De Bruijn Graph

Abstract Motivation Metagenomics refers to the study of complex samples containing of genetic contents of multiple individual organisms and, thus, has been used to elucidate the microbiome and resistome of a complex sample. The microbiome refers to all microbial organisms in a sample, and the resistome refers to all of the antimicrobial resistance (AMR) genes in pathogenic and non-pathogenic bacteria. Single-nucleotide polymorphisms (SNPs) can be effectively used to ‘fingerprint’ specific organisms and genes within the microbiome and resistome and trace their movement across various samples. However, to effectively use these SNPs for this traceability, a scalable and accurate metagenomics SNP caller is needed. Moreover, such an SNP caller should not be reliant on reference genomes since 95% of microbial species is unculturable, making the determination of a reference genome extremely challenging. In this article, we address this need. Results We present LueVari, a reference-free SNP caller based on the read-colored de Bruijn graph, an extension of the traditional de Bruijn graph that allows repeated regions longer than the k-mer length and shorter than the read length to be identified unambiguously. LueVari is able to identify SNPs in both AMR genes and chromosomal DNA from shotgun metagenomics data with reliable sensitivity (between 91% and 99%) and precision (between 71% and 99%) as the performance of competing methods varies widely. Furthermore, we show that LueVari constructs sequences containing the variation, which span up to 97.8% of genes in datasets, which can be helpful in detecting distinct AMR genes in large metagenomic datasets. Availability and implementation Code and datasets are publicly available at https://github.com/baharpan/cosmo/tree/LueVari. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Resistome SNP Calling via Read Colored de Bruijn Graphs

2017 ◽  
Author(s):  
Bahar Alipanahi ◽  
Martin D. Muggli ◽  
Musa Jundi ◽  
Noelle Noyes ◽  
Christina Boucher
Keyword(s):  
Pathogenic Bacteria ◽  
Read Length ◽  
Metagenomic Data ◽  
De Bruijn Graph ◽  
Significant Advance ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
De Bruijn Graphs ◽  
De Bruijn ◽  
Colored De Bruijn Graph

AbstractMotivationThe resistome, which refers to all of the antimicrobial resistance (AMR) genes in pathogenic and non-pathogenic bacteria, is frequently studied using shotgun metagenomic data [14, 47]. Unfortunately, few existing methods are able to identify single nucleotide polymorphisms (SNPs) within metagenomic data, and to the best of our knowledge, no methods exist to detect SNPs within AMR genes within the resistome. The ability to identify SNPs in AMR genes across the resistome would represent a significant advance in understanding the dissemination and evolution of AMR, as SNP identification would enable “fingerprinting” of the resistome, which could then be used to track AMR dynamics across various settings and/or time periods.ResultsWe present LueVari, a reference-free SNP caller based on the read colored de Bruijn graph, an extension of the traditional de Bruijn graph that allows repeated regions longer than the k-mer length and shorter than the read length to be identified unambiguously. We demonstrate LueVari was the only method that had reliable sensitivity (between 73% and 98%) as the performance of competing methods varied widely. Furthermore, we show LueVari constructs sequences containing the variation which span 93% of the gene in datasets with lower coverage (15X), and 100% of the gene in datasets with higher coverage (30X).AvailabilityCode and datasets are publicly available at https://github.com/baharpan/cosmo/tree/LueVari.

scholarly journals Building large updatable colored de Bruijn graphs via merging

2019 ◽  
Vol 35 (14) ◽  
pp. i51-i60 ◽  
Author(s):  
Martin D Muggli ◽  
Bahar Alipanahi ◽  
Christina Boucher
Keyword(s):  
Bloom Filter ◽  
Supplementary Information ◽  
Metagenomic Data ◽  
De Bruijn Graph ◽  
De Bruijn Graphs ◽  
Working Space ◽  
Fold Reduction ◽  
De Bruijn ◽  
Colored De Bruijn Graph ◽  
Public Datasets

Abstract Motivation There exist several large genomic and metagenomic data collection efforts, including GenomeTrakr and MetaSub, which are routinely updated with new data. To analyze such datasets, memory-efficient methods to construct and store the colored de Bruijn graph were developed. Yet, a problem that has not been considered is constructing the colored de Bruijn graph in a scalable manner that allows new data to be added without reconstruction. This problem is important for large public datasets as scalability is needed but also the ability to update the construction is also needed. Results We create a method for constructing the colored de Bruijn graph for large datasets that is based on partitioning the data into smaller datasets, building the colored de Bruijn graph using a FM-index based representation, and succinctly merging these representations to build a single graph. The last step, merging succinctly, is the algorithmic challenge which we solve in this article. We refer to the resulting method as VariMerge. This construction method also allows the graph to be updated with new data. We validate our approach and show it produces a three-fold reduction in working space when constructing a colored de Bruijn graph for 8000 strains. Lastly, we compare VariMerge to other competing methods—including Vari, Rainbowfish, Mantis, Bloom Filter Trie, the method of Almodaresi et al. and Multi-BRWT—and illustrate that VariMerge is the only method that is capable of building the colored de Bruijn graph for 16 000 strains in a manner that allows it to be updated. Competing methods either did not scale to this large of a dataset or do not allow for additions without reconstruction. Availability and implementation VariMerge is available at https://github.com/cosmo-team/cosmo/tree/VARI-merge under GPLv3 license. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Somatic variant analysis of linked-reads sequencing data with Lancet

2020 ◽  
Author(s):  
Rajeeva Musunuri ◽  
Kanika Arora ◽  
André Corvelo ◽  
Minita Shah ◽  
Jennifer Shelton ◽  
...  
Keyword(s):  
Supplementary Information ◽  
De Bruijn Graph ◽  
Haplotype Structure ◽  
Sequencing Data ◽  
Somatic Variant ◽  
Local Assembly ◽  
De Bruijn ◽  
Variant Analysis ◽  
Colored De Bruijn Graph ◽  
Commercial Research

Abstract Summary We present a new version of the popular somatic variant caller, Lancet, that supports the analysis of linked-reads sequencing data. By seamlessly integrating barcodes and haplotype read assignments within the colored De Bruijn graph local-assembly framework, Lancet computes a barcode-aware coverage and identifies variants that disagree with the local haplotype structure. Availability and implementation Lancet is implemented in C++ and available for academic and non-commercial research purposes as an open-source package at https://github.com/nygenome/lancet. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals cloudSPAdes: assembly of synthetic long reads using de Bruijn graphs

2019 ◽  
Vol 35 (14) ◽  
pp. i61-i70 ◽  
Author(s):  
Ivan Tolstoganov ◽  
Anton Bankevich ◽  
Zhoutao Chen ◽  
Pavel A Pevzner
Keyword(s):  
Narrow Range ◽  
State Of The Art ◽  
Supplementary Information ◽  
De Bruijn Graph ◽  
Hybrid Assembly ◽  
De Bruijn Graphs ◽  
Long Reads ◽  
Long Read ◽  
De Bruijn ◽  
New Applications

Abstract Motivation The recently developed barcoding-based synthetic long read (SLR) technologies have already found many applications in genome assembly and analysis. However, although some new barcoding protocols are emerging and the range of SLR applications is being expanded, the existing SLR assemblers are optimized for a narrow range of parameters and are not easily extendable to new barcoding technologies and new applications such as metagenomics or hybrid assembly. Results We describe the algorithmic challenge of the SLR assembly and present a cloudSPAdes algorithm for SLR assembly that is based on analyzing the de Bruijn graph of SLRs. We benchmarked cloudSPAdes across various barcoding technologies/applications and demonstrated that it improves on the state-of-the-art SLR assemblers in accuracy and speed. Availability and implementation Source code and installation manual for cloudSPAdes are available at https://github.com/ablab/spades/releases/tag/cloudspades-paper. Supplementary Information Supplementary data are available at Bioinformatics online.

Inference of viral quasispecies with a paired de Bruijn graph

2020 ◽  
Author(s):  
Borja Freire ◽  
Susana Ladra ◽  
Jose R Paramá ◽  
Leena Salmela
Keyword(s):  
High Throughput Sequencing ◽  
De Novo ◽  
Supplementary Information ◽  
De Bruijn Graph ◽  
Viral Quasispecies ◽  
Sequencing Data ◽  
De Bruijn Graphs ◽  
Sequencing Errors ◽  
High Throughput Sequencing Data ◽  
De Bruijn

Abstract Motivation RNA viruses exhibit a high mutation rate and thus they exist in infected cells as a population of closely related strains called viral quasispecies. The viral quasispecies assembly problem asks to characterize the quasispecies present in a sample from high-throughput sequencing data. We study the de novo version of the problem, where reference sequences of the quasispecies are not available. Current methods for assembling viral quasispecies are either based on overlap graphs or on de Bruijn graphs. Overlap graph-based methods tend to be accurate but slow, whereas de Bruijn graph-based methods are fast but less accurate. Results We present viaDBG, which is a fast and accurate de Bruijn graph-based tool for de novo assembly of viral quasispecies. We first iteratively correct sequencing errors in the reads, which allows us to use large k-mers in the de Bruijn graph. To incorporate the paired-end information in the graph, we also adapt the paired de Bruijn graph for viral quasispecies assembly. These features enable the use of long-range information in contig construction without compromising the speed of de Bruijn graph-based approaches. Our experimental results show that viaDBG is both accurate and fast, whereas previous methods are either fast or accurate but not both. In particular, viaDBG has comparable or better accuracy than SAVAGE, while being at least nine times faster. Furthermore, the speed of viaDBG is comparable to PEHaplo but viaDBG is able to retrieve also low abundance quasispecies, which are often missed by PEHaplo. Availability and implementation viaDBG is implemented in C++ and it is publicly available at https://bitbucket.org/bfreirec1/viadbg. All datasets used in this article are publicly available at https://bitbucket.org/bfreirec1/data-viadbg/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Toward perfect reads: self-correction of short reads via mapping on de Bruijn graphs

2019 ◽  
Vol 36 (5) ◽  
pp. 1374-1381 ◽  
Author(s):  
Antoine Limasset ◽  
Jean-François Flot ◽  
Pierre Peterlongo
Keyword(s):  
Supplementary Information ◽  
De Bruijn Graph ◽  
Sequence Information ◽  
Short Read ◽  
De Bruijn Graphs ◽  
Short Reads ◽  
Sequencing Errors ◽  
Long Read ◽  
De Bruijn ◽  
Read Accuracy

Abstract Motivation 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 datasets or consider reads as mere suites of k-mers, without taking into account their full-length sequence 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 implementation The implementation is open source, available at http://github.com/Malfoy/BCOOL under the Affero GPL license and as a Bioconda package. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Detecting High Scoring Local Alignments in Pangenome Graphs

2021 ◽  
Author(s):  
Tizian Schulz ◽  
Roland Wittler ◽  
Sven Rahmann ◽  
Faraz Hach ◽  
Jens Stoye
Keyword(s):  
Sequence Similarity ◽  
Query Sequence ◽  
Heuristic Method ◽  
Supplementary Information ◽  
De Bruijn Graph ◽  
Local Alignment ◽  
Memory Usage ◽  
Sequence Comparisons ◽  
De Bruijn Graphs ◽  
De Bruijn

Abstract Motivation Increasing amounts of individual genomes sequenced per species motivate the usage of pangenomic approaches. Pangenomes may be represented as graphical structures, e.g. compacted colored de Bruijn graphs, which offer a low memory usage and facilitate reference-free sequence comparisons. While sequence-to-graph mapping to graphical pangenomes has been studied for some time, no local alignment search tool in the vein of BLAST has been proposed yet. Results We present a new heuristic method to find maximum scoring local alignments of a DNA query sequence to a pangenome represented as a compacted colored de Bruijn graph. Our approach additionally allows a comparison of similarity among sequences within the pangenome. We show that local alignment scores follow an exponential-tail distribution similar to BLAST scores, and we discuss how to estimate its parameters to separate local alignments representing sequence homology from spurious findings. An implementation of our method is presented, and its performance and usability are shown. Our approach scales sublinearly in running time and memory usage with respect to the number of genomes under consideration. This is an advantage over classical methods that do not make use of sequence similarity within the pangenome. Availability Source code and test data are available from https://gitlab.ub.uni-bielefeld.de/gi/plast. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals BubbleGun: Enumerating Bubbles and Superbubbles in Genome Graphs

2021 ◽  
Author(s):  
Fawaz Dabbaghie ◽  
Jana Ebler ◽  
Tobias Marschall
Keyword(s):  
De Novo ◽  
General Purpose ◽  
Supplementary Information ◽  
De Bruijn Graph ◽  
De Bruijn Graphs ◽  
Third Generation Sequencing ◽  
Human Sample ◽  
Fast Development ◽  
De Bruijn ◽  
Generation Sequencing

AbstractMotivationWith the fast development of third generation sequencing machines, de novo genome assembly is becoming a routine even for larger genomes. Graph-based representations of genomes arise both as part of the assembly process, but also in the context of pangenomes representing a population. In both cases, polymorphic loci lead to bubble structures in such graphs. Detecting bubbles is hence an important task when working with genomic variants in the context of genome graphs.ResultsHere, we present a fast general-purpose tool, called BubbleGun, for detecting bubbles and superbubbles in genome graphs. Furthermore, BubbleGun detects and outputs runs of linearly connected bubbles and superbubbles, which we call bubble chains. We showcase its utility on de Bruijn graphs and compare our results to vg’s snarl detection. We show that BubbleGun is considerably faster than vg especially in bigger graphs, where it reports all bubbles in less than 30 minutes on a human sample de Bruijn graph of around 2 million nodes.AvailabilityBubbleGun is available and documented at https://github.com/fawaz-dabbaghieh/bubble_gun under MIT [email protected] or [email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals Cuttlefish: Fast, parallel, and low-memory compaction of de Bruijn graphs from large-scale genome collections

2020 ◽  
Author(s):  
Jamshed Khan ◽  
Rob Patro
Keyword(s):  
Large Scale ◽  
De Bruijn Graph ◽  
Comparative Genomic ◽  
De Bruijn Graphs ◽  
Long Reads ◽  
Genomic Analyses ◽  
Finite State ◽  
De Bruijn ◽  
Colored De Bruijn Graph ◽  
Memory Compaction

AbstractMotivationThe construction of the compacted de Bruijn graph from a large collection of reference genomes is a task of increasing interest in genomic analyses. For example, compacted colored reference de Bruijn graphs are increasingly used as sequence indices for the purposes of alignment of short and long reads. Also, as we sequence and assemble a greater diversity of individual genomes, the compacted colored de Bruijn graph can be used as the basis for methods aiming to perform comparative genomic analyses on these genomes. While algorithms have been developed to construct the compacted colored de Bruijn graph from reference sequences, there is still room for improvement, especially in the memory and the runtime performance as the number and the scale of the genomes over which the de Bruijn graph is built grow.ResultsWe introduce a new algorithm, implemented in the tool Cuttlefish, to construct the colored compacted de Bruijn graph from a collection of one or more genome references. Cuttlefish introduces a novel modeling scheme of the de Bruijn graph vertices as finite-state automata, and constrains the state-space for the automata to enable tracking of their transitioning states with very low memory usage. Cuttlefish is also fast and highly parallelizable. Experimental results demonstrate that the algorithm scales much better than existing approaches, especially as the number and scale of the input references grow. For example, on a typical shared-memory machine, Cuttlefish constructed the compacted graph for 100 human genomes in less than 7 hours, using ~29 GB of memory; no other tested tool successfully completed this task on the testing hardware. We also applied Cuttlefish on 11 diverse conifer plant genomes, and the compacted graph was constructed in under 11 hours, using ~84 GB of memory, while the only other tested tool able to complete this compaction on our hardware took more than 16 hours and ~289 GB of memory.AvailabilityCuttlefish is written in C++14, and is available under an open source license at https://github.com/COMBINE-lab/[email protected]

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

2019 ◽  
Author(s):  
Guillaume Holley ◽  
Páll Melsted
Keyword(s):  
High Throughput Sequencing ◽  
Genomic Analysis ◽  
Main Memory ◽  
De Bruijn Graph ◽  
Direct Construction ◽  
De Bruijn Graphs ◽  
Wide Range ◽  
User Data ◽  
De Bruijn ◽  
Colored De Bruijn Graph

AbstractMotivationDe 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.ResultsWe 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 eachk-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.Availabilityhttps://github.com/pmelsted/bifrostavailable with a BSD-2 [email protected]

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