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.

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 Disentangled Long-Read De Bruijn Graphs via Optical Maps

2016 ◽  
Author(s):  
Bahar Alipanahi ◽  
Leena Salmela ◽  
Simon J. Puglisi ◽  
Martin Muggli ◽  
Christina Boucher
Keyword(s):  
Auxiliary Information ◽  
Positional Information ◽  
De Bruijn Graph ◽  
De Bruijn Graphs ◽  
Assembly Algorithm ◽  
Third Generation Sequencing ◽  
Long Reads ◽  
Succinct Data Structure ◽  
Long Read ◽  
De Bruijn

AbstractPacific Biosciences (PacBio), the main third generation sequencing technology can produce scalable, high-throughput, unprecedented sequencing results through long reads with uniform coverage. Although these long reads have been shown to increase the quality of draft genomes in repetitive regions, fundamental computational challenges remain in overcoming their high error rate and assembling them efficiently. In this paper we show that the de Bruijn graph built on the long reads can be efficiently and substantially disentangled using optical mapping data as auxiliary information. Fundamental to our approach is the use of the positional de Bruijn graph and a succinct data structure for constructing and traversing this graph. Our experimental results show that over 97.7% of directed cycles have been removed from the resulting positional de Bruijn graph as compared to its non-positional counterpart. Our results thus indicate that disentangling the de Bruijn graph using positional information is a promising direction for developing a simple and efficient assembly algorithm for long reads.

scholarly journals Faucet: streaming de novo assembly graph construction

2017 ◽  
Author(s):  
Roye Rozov ◽  
Gil Goldshlager ◽  
Eran Halperin ◽  
Ron Shamir
Keyword(s):  
Resource Use ◽  
De Novo ◽  
State Of The Art ◽  
Supplementary Information ◽  
De Bruijn Graph ◽  
Assembly Quality ◽  
Metagenome Assembly ◽  
Streaming Algorithm ◽  
Supplementary Material ◽  
De Bruijn

AbstractMotivationWe present Faucet, a 2-pass streaming algorithm for assembly graph construction. Faucet builds an assembly graph incrementally as each read is processed. Thus, reads need not be stored locally, as they can be processed while downloading data and then discarded. We demonstrate this functionality by performing streaming graph assembly of publicly available data, and observe that the ratio of disk use to raw data size decreases as coverage is increased.ResultsFaucet pairs the de Bruijn graph obtained from the reads with additional meta-data derived from them. We show these metadata - coverage counts collected at junction k-mers and connections bridging between junction pairs - contain most salient information needed for assembly, and demonstrate they enable cleaning of metagenome assembly graphs, greatly improving contiguity while maintaining accuracy. We compared Faucet’s resource use and assembly quality to state of the art metagenome assemblers, as well as leading resource-efficient genome assemblers. Faucet used orders of magnitude less time and disk space than the specialized metagenome assemblers MetaSPAdes and Megahit, while also improving on their memory use; this broadly matched performance of other assemblers optimizing resource efficiency - namely, Minia and LightAssembler. However, on metagenomes tested, Faucet’s outputs had 14-110% higher mean NGA50 lengths compared to Minia, and 2-11-fold higher mean NGA50 lengths compared to LightAssembler, the only other streaming assembler available.AvailabilityFaucet is available at https://github.com/Shamir-Lab/[email protected],[email protected] 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 A hybrid and scalable error correction algorithm for indel and substitution errors of long reads

BMC Genomics ◽  
2019 ◽  
Vol 20 (S11) ◽  
Author(s):  
Arghya Kusum Das ◽  
Sayan Goswami ◽  
Kisung Lee ◽  
Seung-Jong Park
Keyword(s):  
Error Correction ◽  
Error Rates ◽  
De Bruijn Graph ◽  
Correction Algorithm ◽  
Short Read ◽  
Short Reads ◽  
Long Reads ◽  
Long Read ◽  
De Bruijn ◽  
Error Correction Algorithm

Abstract Background Long-read sequencing has shown the promises to overcome the short length limitations of second-generation sequencing by providing more complete assembly. However, the computation of the long sequencing reads is challenged by their higher error rates (e.g., 13% vs. 1%) and higher cost ($0.3 vs. $0.03 per Mbp) compared to the short reads. Methods In this paper, we present a new hybrid error correction tool, called ParLECH (Parallel Long-read Error Correction using Hybrid methodology). The error correction algorithm of ParLECH is distributed in nature and efficiently utilizes the k-mer coverage information of high throughput Illumina short-read sequences to rectify the PacBio long-read sequences.ParLECH first constructs a de Bruijn graph from the short reads, and then replaces the indel error regions of the long reads with their corresponding widest path (or maximum min-coverage path) in the short read-based de Bruijn graph. ParLECH then utilizes the k-mer coverage information of the short reads to divide each long read into a sequence of low and high coverage regions, followed by a majority voting to rectify each substituted error base. Results ParLECH outperforms latest state-of-the-art hybrid error correction methods on real PacBio datasets. Our experimental evaluation results demonstrate that ParLECH can correct large-scale real-world datasets in an accurate and scalable manner. ParLECH can correct the indel errors of human genome PacBio long reads (312 GB) with Illumina short reads (452 GB) in less than 29 h using 128 compute nodes. ParLECH can align more than 92% bases of an E. coli PacBio dataset with the reference genome, proving its accuracy. Conclusion ParLECH can scale to over terabytes of sequencing data using hundreds of computing nodes. The proposed hybrid error correction methodology is novel and rectifies both indel and substitution errors present in the original long reads or newly introduced by the short reads.

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 TALC: Transcript-level Aware Long Read Correction

2020 ◽  
Author(s):  
Lucile Broseus ◽  
Aubin Thomas ◽  
Andrew J. Oldfield ◽  
Dany Severac ◽  
Emeric Dubois ◽  
...  
Keyword(s):  
Transcriptome Sequencing ◽  
Transcript Level ◽  
De Bruijn Graph ◽  
Rna Seq ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Long Read ◽  
De Bruijn ◽  
Rna Transcript

ABSTRACTMotivationLong-read sequencing technologies are invaluable for determining complex RNA transcript architectures but are error-prone. Numerous “hybrid correction” algorithms have been developed for genomic data that correct long reads by exploiting the accuracy and depth of short reads sequenced from the same sample. These algorithms are not suited for correcting more complex transcriptome sequencing data.ResultsWe have created a novel reference-free algorithm called TALC (Transcription Aware Long Read Correction) which models changes in RNA expression and isoform representation in a weighted De-Bruijn graph to correct long reads from transcriptome studies. We show that transcription aware correction by TALC improves the accuracy of the whole spectrum of downstream RNA-seq applications and is thus necessary for transcriptome analyses that use long read technology.Availability and ImplementationTALC is implemented in C++ and available at https://gitlab.igh.cnrs.fr/lbroseus/[email protected]

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 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.

Sign in / Sign up

Export Citation Format

Share Document