Assembling short reads from jumping libraries with large insert sizes

Vasilinetc, Irina; Prjibelski, Andrey D.; Gurevich, Alexey; Korobeynikov, Anton; Pevzner, Pavel A.

doi:10.1093/bioinformatics/btv337

Cited by 52 publications

(39 citation statements)

References 26 publications

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“…The Velvet assembly from all runs was selected based on the largest continuous contig. SPAdes v3.1.1 was used for guided assembly, with Velvet assembly used as a guide for progressive k-mer assemblies (k ϭ 21, 33, 55, 77, 99, and 127; the defaults for SPAdes for this read length) (20,21). Genome quality control was performed as follows (22).…”

Section: Methodsmentioning

confidence: 99%

Comparison of Whole-Genome Sequencing Methods for Analysis of Three Methicillin-Resistant Staphylococcus aureus Outbreaks

et al. 2017

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Whole-genome sequencing (WGS) can provide excellent resolution in global and local epidemiological investigations of Staphylococcus aureus outbreaks. A variety of sequencing approaches and analytical tools have been used; it is not clear which is ideal. We compared two WGS strategies and two analytical approaches to the standard method of SmaI restriction digestion pulsed-field gel electrophoresis (PFGE) for typing S. aureus. Forty-two S. aureus isolates from three outbreaks and 12 reference isolates were studied. Near-complete genomes, assembled de novo with paired-end and long-mate-pair (8 kb) libraries were first assembled and analyzed utilizing an in-house assembly and analytical informatics pipeline. In addition, pairedend data were assembled and analyzed using a commercial software package. Single nucleotide variant (SNP) analysis was performed using the in-house pipeline. Two assembly strategies were used to generate core genome multilocus sequence typing (cgMLST) data. First, the near-complete genome data generated with the inhouse pipeline were imported into the commercial software and used to perform cgMLST analysis. Second, the commercial software was used to assemble paired-end data, and resolved assemblies were used to perform cgMLST. Similar isolate clustering was observed using SNP calling and cgMLST, regardless of data assembly strategy. All methods provided more discrimination between outbreaks than did PFGE. Overall, all of the evaluated WGS strategies yielded statistically similar results for S. aureus typing.KEYWORDS MRSA, PFGE, Staphylococcus aureus, molecular typing, whole-genome sequencing M ethicillin-resistant Staphylococcus aureus (MRSA) infections are associated with high morbidity and mortality. MRSA transmission poses a challenge to hospital infection prevention and control practitioners and public health professionals. The Centers for Disease Control and Prevention's Active Bacterial Surveillance Report estimated that there were 72,444 cases of invasive MRSA infection in 2014, the majority of which were health care associated (HCA) (1). Proactive screening strategies (molecular and culture based) are emphasized in many institutions and are mandatory in some states. Despite these measures, HCA-MRSA outbreaks continue to occur. Thorough investigation of outbreaks is essential for confirming that an outbreak is occurring, understanding transmission patterns and reservoirs, and intervening to interrupt out-

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Section: Methodsmentioning

confidence: 99%

Comparison of Whole-Genome Sequencing Methods for Analysis of Three Methicillin-Resistant Staphylococcus aureus Outbreaks

et al. 2017

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“…Hence, the A-Bruijn graph can function as an oracle, from which one can efficiently identify the overlaps of a given read with all other reads by considering all possible overlaps at once. The genome is assembled by repeatedly applying this procedure and borrowing the path extension paradigm from short read assemblers (37)(38)(39).…”

Section: For Details)mentioning

confidence: 99%

“…After constructing an A-Bruijn graph, one faces the problem of finding a path in this graph that corresponds to traversing the genome and then correcting errors in the sequence spelled by this path (this genomic path does not have to traverse all edges of the graph). Because the long reads are merely paths in the A-Bruijn graph, one can use the path extension paradigm (37)(38)(39) to derive the genomic path from these (shorter) read-paths. exSPAnder (38) is a module of the SPAdes assembler (24) that finds a genomic The histograms of the number of 15-mers with given frequencies for the ECOLI dataset from Escherichia coli.…”

Section: For Details)mentioning

confidence: 99%

“…Although it seems that the notion of overlapping paths allows us to implement the path extension paradigm for A-Bruijn graphs, there are two complications. First, the path extension algorithm becomes more complex when the growing path ends in a long repeat (39). Second, chimeric reads may end up in the set of overlapping read-paths extending the growing path in the ABruijn algorithm.…”

Section: For Details)mentioning

confidence: 99%

“…When exSPAnder extends the growing path, it takes into account the local repeat structure of the de Bruijn graph, resulting in a rather complex decision rule in the case when the growing path contains a repeat (38,39). Fig.…”

Section: For Details)mentioning

confidence: 99%

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Assembly of long error-prone reads using de Bruijn graphs

Lin

Yuan

Kolmogorov

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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298

220

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The recent breakthroughs in assembling long error-prone reads were based on the overlap-layout-consensus (OLC) approach and did not utilize the strengths of the alternative de Bruijn graph approach to genome assembly. Moreover, these studies often assume that applications of the de Bruijn graph approach are limited to short and accurate reads and that the OLC approach is the only practical paradigm for assembling long error-prone reads. We show how to generalize de Bruijn graphs for assembling long error-prone reads and describe the ABruijn assembler, which combines the de Bruijn graph and the OLC approaches and results in accurate genome reconstructions.de Bruijn graph | genome assembly | single-molecule sequencing T he key challenge to the success of single-molecule sequencing (SMS) technologies lies in the development of algorithms for assembling genomes from long but inaccurate reads. The pioneer in long reads technologies, Pacific Biosciences, now produces accurate assemblies from long error-prone reads (1, 2). Goodwin et al. (3) and Loman et al. (4) demonstrated that high-quality assemblies can be obtained from even less-accurate Oxford Nanopore reads. Advances in assembly of long errorprone reads recently resulted in the accurate reconstructions of various genomes (5-10). However, as illustrated in Booher et al. (11), the problem of assembling long error-prone reads is far from being resolved even in the case of relatively small bacterial genomes.Previous studies of SMS assemblies were based on the overlaplayout-consensus (OLC) approach (12) or a similar string graph approach (13), which require an all-against-all comparison of reads (14) and remain computationally challenging (see refs. 15-17 for a discussion of the pros and cons of this approach). Moreover, there is an assumption that the de Bruijn graph approach, which has dominated genome assembly for the last decade, is inapplicable to long reads. This is a misunderstanding, because the de Bruijn graph approach, as well as its variation called the A-Bruijn graph approach, was developed to assemble rather long Sanger reads (18). There is also a misunderstanding that the de Bruijn graph approach can only assemble highly accurate reads and fails when assembling long error-prone reads. Although this is true for the original de Bruijn graph approach to assembly (15-17), the A-Bruijn graph approach was originally designed to assemble inaccurate reads as long as any similarities between reads can be reliably identified. Moreover, A-Bruijn graphs have proven to be useful even for assembling mass spectra, which represent highly inaccurate fingerprints of amino acid sequences of peptides (19,20). However, although A-Bruijn graphs have proven to be useful in assembling Sanger reads and mass spectra, the question of how to apply A-Bruijn graphs for assembling long error-prone reads remains open.de Bruijn graphs are a key algorithmic technique in genome assembly (15,(21)(22)(23)(24). In addition, de Bruijn graphs have been used for sequencing by hybridization (...

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Using SPAdes De Novo Assembler

Prjibelski

Antipov

Meleshko

et al. 2020

CP in Bioinformatics

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1,729

926

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SPAdes—St. Petersburg genome Assembler—was originally developed for de novo assembly of genome sequencing data produced for cultivated microbial isolates and for single‐cell genomic DNA sequencing. With time, the functionality of SPAdes was extended to enable assembly of IonTorrent data, as well as hybrid assembly from short and long reads (PacBio and Oxford Nanopore). In this article we present protocols for five different assembly pipelines that comprise the SPAdes package and that are used for assembly of metagenomes and transcriptomes as well as assembly of putative plasmids and biosynthetic gene clusters from whole‐genome sequencing and metagenomic datasets. In addition, we present guidelines for understanding results with use cases for each pipeline, and several additional support protocols that help in using SPAdes properly. © 2020 Wiley Periodicals LLC. Basic Protocol 1: Assembling isolate bacterial datasets Basic Protocol 2: Assembling metagenomic datasets Basic Protocol 3: Assembling sets of putative plasmids Basic Protocol 4: Assembling transcriptomes Basic Protocol 5: Assembling putative biosynthetic gene clusters Support Protocol 1: Installing SPAdes Support Protocol 2: Providing input via command line Support Protocol 3: Providing input data via YAML format Support Protocol 4: Restarting previous run Support Protocol 5: Determining strand‐specificity of RNA‐seq data

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Assembling short reads from jumping libraries with large insert sizes

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 52 publications

References 26 publications

Comparison of Whole-Genome Sequencing Methods for Analysis of Three Methicillin-Resistant Staphylococcus aureus Outbreaks

Comparison of Whole-Genome Sequencing Methods for Analysis of Three Methicillin-Resistant Staphylococcus aureus Outbreaks

Assembly of long error-prone reads using de Bruijn graphs

Using SPAdes De Novo Assembler

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