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.

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 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 deSPI: efficient classification of metagenomic reads with lightweight de Bruijn graph-based reference indexing

2016 ◽  
Author(s):  
Dengfeng Guan ◽  
Bo Liu ◽  
Yadong Wang
Keyword(s):  
Source Code ◽  
Classification Method ◽  
Supplementary Information ◽  
De Bruijn Graph ◽  
Supplementary Data ◽  
Link Type ◽  
Memory Footprint ◽  
Supplementary Material ◽  
De Bruijn

AbstractSummaryIn metagenomic studies, fast and effective tools are on wide demand to implement taxonomy classification for upto billions of reads. Herein, we propose deSPI, a novel read classification method that classifies reads by recognizing and analyzing the matches between reads and reference with de Bruijn graph-based lightweight reference indexing. deSPI has faster speed with relatively small memory footprint, meanwhile, it can also achieve higher or similar sensitivity and accuracy.Availabilitythe C++ source code of deSPI is available at https://github.com/hitbc/[email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals deGSM: memory scalable construction of large scale de Bruijn Graph

2018 ◽  
Author(s):  
Hongzhe Guo ◽  
Yilei Fu ◽  
Yan Gao ◽  
Junyi Li ◽  
Yadong Wang ◽  
...  
Keyword(s):  
Genome Sequence ◽  
Large Scale ◽  
High Throughput Sequencing ◽  
De Novo ◽  
Rapid Development ◽  
Main Idea ◽  
Supplementary Information ◽  
De Bruijn Graph ◽  
External Sorting ◽  
De Bruijn

AbstractMotivationDe Bruijn graph, a fundamental data structure to represent and organize genome sequence, plays important roles in various kinds of sequence analysis tasks such as de novo assembly, high-throughput sequencing (HTS) read alignment, pan-genome analysis, metagenomics analysis, HTS read correction, etc. With the rapid development of HTS data and ever-increasing number of assembled genomes, there is a high demand to construct de Bruijn graph for sequences up to Tera-base-pair level. It is non-trivial since the size of the graph to be constructed could be very large and each graph consists of hundreds of billions of vertices and edges. Current existing approaches may have unaffordable memory footprints to handle such a large de Bruijn graph. Moreover, it also requires the construction approach to handle very large dataset efficiently, even if in a relatively small RAM space.ResultsWe propose a lightweight parallel de Bruijn graph construction approach, de Bruijn Graph Constructor in Scalable Memory (deGSM). The main idea of deGSM is to efficiently construct the Bur-rows-Wheeler Transformation (BWT) of the unipaths of de Bruijn graph in constant RAM space and transform the BWT into the original unitigs. It is mainly implemented by a fast parallel external sorting of k-mers, which allows only a part of k-mers kept in RAM by a novel organization of the k-mers. The experimental results demonstrate that, just with a commonly used machine, deGSM is able to handle very large genome sequence(s), e.g., the contigs (305 Gbp) and scaffolds (1.1 Tbp) recorded in Gen-Bank database and Picea abies HTS dataset (9.7 Tbp). Moreover, deGSM also has faster or comparable construction speed compared with state-of-the-art approaches. With its high scalability and efficiency, deGSM has enormous potentials in many large scale genomics studies.Availabilityhttps://github.com/hitbc/[email protected] (YW) and [email protected] (BL)Supplementary informationSupplementary data are available online.

scholarly journals DeepMAsED: evaluating the quality of metagenomic assemblies

2020 ◽  
Vol 36 (10) ◽  
pp. 3011-3017 ◽  
Author(s):  
Olga Mineeva ◽  
Mateo Rojas-Carulla ◽  
Ruth E Ley ◽  
Bernhard Schölkopf ◽  
Nicholas D Youngblut
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Ground Truth ◽  
Supplementary Information ◽  
Learning Approach ◽  
Wide Range ◽  
Metagenome Assembly ◽  
Model Training ◽  
Reference Genomes

Abstract Motivation Methodological advances in metagenome assembly are rapidly increasing in the number of published metagenome assemblies. However, identifying misassemblies is challenging due to a lack of closely related reference genomes that can act as pseudo ground truth. Existing reference-free methods are no longer maintained, can make strong assumptions that may not hold across a diversity of research projects, and have not been validated on large-scale metagenome assemblies. Results We present DeepMAsED, a deep learning approach for identifying misassembled contigs without the need for reference genomes. Moreover, we provide an in silico pipeline for generating large-scale, realistic metagenome assemblies for comprehensive model training and testing. DeepMAsED accuracy substantially exceeds the state-of-the-art when applied to large and complex metagenome assemblies. Our model estimates a 1% contig misassembly rate in two recent large-scale metagenome assembly publications. Conclusions DeepMAsED accurately identifies misassemblies in metagenome-assembled contigs from a broad diversity of bacteria and archaea without the need for reference genomes or strong modeling assumptions. Running DeepMAsED is straight-forward, as well as is model re-training with our dataset generation pipeline. Therefore, DeepMAsED is a flexible misassembly classifier that can be applied to a wide range of metagenome assembly projects. Availability and implementation DeepMAsED is available from GitHub at https://github.com/leylabmpi/DeepMAsED. 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 Scalable Genome Assembly through Parallel de Bruijn Graph Construction for Multiple k-mers

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Kanak Mahadik ◽  
Christopher Wright ◽  
Milind Kulkarni ◽  
Saurabh Bagchi ◽  
Somali Chaterji
Keyword(s):  
De Novo ◽  
De Bruijn Graph ◽  
High Quality ◽  
De Bruijn Graphs ◽  
Sequencing Technologies ◽  
De Bruijn ◽  
Similar Accuracy ◽  
Valued Graph ◽  
Assembly Algorithms ◽  
Level Parallelism

Abstract Remarkable advancements in high-throughput gene sequencing technologies have led to an exponential growth in the number of sequenced genomes. However, unavailability of highly parallel and scalable de novo assembly algorithms have hindered biologists attempting to swiftly assemble high-quality complex genomes. Popular de Bruijn graph assemblers, such as IDBA-UD, generate high-quality assemblies by iterating over a set of k-values used in the construction of de Bruijn graphs (DBG). However, this process of sequentially iterating from small to large k-values slows down the process of assembly. In this paper, we propose ScalaDBG, which metamorphoses this sequential process, building DBGs for each distinct k-value in parallel. We develop an innovative mechanism to “patch” a higher k-valued graph with contigs generated from a lower k-valued graph. Moreover, ScalaDBG leverages multi-level parallelism, by both scaling up on all cores of a node, and scaling out to multiple nodes simultaneously. We demonstrate that ScalaDBG completes assembling the genome faster than IDBA-UD, but with similar accuracy on a variety of datasets (6.8X faster for one of the most complex genome in our dataset).

A de novo genome assembler based on MapReduce and bi-directed de Bruijn graph

2016 ◽  
Author(s):  
Yuehua Zhang ◽  
Pengfei Xuan ◽  
Yunsheng Wang ◽  
Pradip K. Srimani ◽  
Feng Luo
Keyword(s):  
De Novo ◽  
De Bruijn Graph ◽  
De Bruijn ◽  
Genome Assembler

scholarly journals Clover: a clustering-oriented de novo assembler for Illumina sequences

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Ming-Feng Hsieh ◽  
Chin Lung Lu ◽  
Chuan Yi Tang
Keyword(s):  
De Novo Assembly ◽  
De Novo ◽  
Low Cost ◽  
De Bruijn Graph ◽  
Illumina Platform ◽  
Sequencing Errors ◽  
Sequencing Technologies ◽  
String Graph ◽  
Clustering Approach ◽  
De Bruijn

Abstract Background Next-generation sequencing technologies revolutionized genomics by producing high-throughput reads at low cost, and this progress has prompted the recent development of de novo assemblers. Multiple assembly methods based on de Bruijn graph have been shown to be efficient for Illumina reads. However, the sequencing errors generated by the sequencer complicate analysis of de novo assembly and influence the quality of downstream genomic researches. Results In this paper, we develop a de Bruijn assembler, called Clover (clustering-oriented de novo assembler), that utilizes a novel k-mer clustering approach from the overlap-layout-consensus concept to deal with the sequencing errors generated by the Illumina platform. We further evaluate Clover’s performance against several de Bruijn graph assemblers (ABySS, SOAPdenovo, SPAdes and Velvet), overlap-layout-consensus assemblers (Bambus2, CABOG and MSR-CA) and string graph assembler (SGA) on three datasets (Staphylococcus aureus, Rhodobacter sphaeroides and human chromosome 14). The results show that Clover achieves a superior assembly quality in terms of corrected N50 and E-size while remaining a significantly competitive in run time except SOAPdenovo. In addition, Clover was involved in the sequencing projects of bacterial genomes Acinetobacter baumannii TYTH-1 and Morganella morganii KT. Conclusions The marvel clustering-based approach of Clover that integrates the flexibility of the overlap-layout-consensus approach and the efficiency of the de Bruijn graph method has high potential on de novo assembly. Now, Clover is freely available as open source software from https://oz.nthu.edu.tw/~d9562563/src.html.

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