Open Access

Employing whole genome mapping for optimal de novo assembly of bacterial genomes

  • Basil Britto Xavier1,
  • Julia Sabirova1,
  • Moons Pieter1,
  • Jean-Pierre Hernalsteens2,
  • Henri de Greve3,
  • Herman Goossens1 and
  • Surbhi Malhotra-Kumar1Email author
BMC Research Notes20147:484

https://doi.org/10.1186/1756-0500-7-484

Received: 8 April 2014

Accepted: 21 July 2014

Published: 30 July 2014

Abstract

Background

De novo genome assembly can be challenging due to inherent properties of the reads, even when using current state-of-the-art assembly tools based on de Bruijn graphs. Often users are not bio-informaticians and, in a black box approach, utilise assembly parameters such as contig length and N50 to generate whole genome sequences, potentially resulting in mis-assemblies.

Findings

Utilising several assembly tools based on de Bruijn graphs like Velvet, SPAdes and IDBA, we demonstrate that at the optimal N50, mis-assemblies do occur, even when using the multi-k-mer approaches of SPAdes and IDBA. We demonstrate that whole genome mapping can be used to identify these mis-assemblies and can guide the selection of the best k-mer size which yields the highest N50 without mis-assemblies.

Conclusions

We demonstrate the utility of whole genome mapping (WGM) as a tool to identify mis-assemblies and to guide k-mer selection and higher quality de novo genome assembly of bacterial genomes.

Keywords

De-novo assembly Whole genome mapping k-mer Microbial genomes de bruijn graph SPAdes and Velvet

Findings

Genome assembly is often a primary step in the process of yielding results that lead to interpretation of biological data and hence sub-optimally assembled genomes might lead to faulty conclusions [1]. Factors causing such low quality genome assembly include sequence quality, presence of repetitive sequences, base composition, size and low genome coverage [2, 3], all of which complicate downstream data analysis using the available tools [4]. Currently, de novo assemblers based on de Bruijn graph are considered to yield the best results provided sufficient sequence quality and coverage are achieved. Such assembly tools based on de Bruijn graph algorithms, like Velvet [5] and SPAdes [6] use k-mers as building blocks, but as most users are not bio-informaticians, these tools are often considered as an encrypted black box with the quality of the assembly usually determined by statistics parameters such as the N50 and the size and number of contigs or scaffolds produced by the assemblers [7]. However, the choice of the k-mer size is crucial as too low or too high k-mer sizes lead to sub-optimal assemblies. Indeed, low quality reads might produce false positive vertices, repeats lead to branching, while an uneven distribution of the reads results in gaps. The use of smaller k-mers reduces the problem associated with low quality reads and their uneven distribution, while larger k-mer sizes help to bridge repeat regions decreasing the branching problem [8]. In a balancing exercise, various k-mer sizes are usually selected, evaluating optimization by aiming for high N50 values and long, but fewer contigs. Whole Genome Mapping (WGM; Opgen Inc, Gaithersburg, MD, USA) is a relatively novel technique that generates high-resolution restriction maps of a genome based on the alignment of single DNA molecules cut with restriction enzymes and ordered with high resolution and accuracy [9]. WGM was proven helpful in mapping de novo assembled contigs against previously sequenced related genomes [10].

In this paper, we evaluated the utility of WGM for proper k-mer size selection and for optimization of parameters for de novo genome assembly. The whole genome sequence of a methicillin-resistant Staphylococcus aureus (MRSA) strain (E-MRSA15-CC22-SCCmec IV) was generated on an Illumina HiSeq-2000 via 2X150b paired end sequencing [11]. Reads were de novo assembled using Velvet, SPAdes and IDBA-UD employing a range of k-mers. Velvet, using a single k-mer approach ranging from k-mer size 81 to 123 showed an initial increase in N50 (until k-mer size 115) and longest contig size and a concomitant decrease in the total number of contigs with increasing k-mer size with both these parameters positively influencing the assembly outcome (Table 1). Ensuing, using WGM, a whole genome map of S. aureus EMRSA-15 was generated which, using MapSolver, was aligned with the assembly files corresponding to different k-mer sizes. Although the percentage of the genome covered by contigs increased with increasing k-mer size, a mis-assembly (spanning 119 kb) was identified for the mapped contigs (>40 kb) for higher k-mer sizes (Table 1), revealing the best (without mis-assemblies) assembly was actually obtained using a k-mer size of 93 despite a higher N50 and fewer contigs as when for example utilizing a k-mer size of 115 (Table 1, Figure 1A, B). In contrast, SPAdes, which allows to combine a range of k-mer sizes in a multi-k-mer approach did not yield any mis-assemblies on this sequence for the N50 based best assemblies (Figure 1C). The same was true for IDBA, which similarly utilizes an iterative process including multiple k-mer sizes, while removing assembled sequences in subsequent rounds of analysis.
Table 1

Assembly statistics of Velvet applied on Staphylococcus aureus (MRSA) strain E-MRSA15-CC22-SCC mec IV showing an increase in contig size and N50 when using higher k-mer sizes, but revealing a mis-assembly starting from k-mer size 97 using whole genome mapping

K-mer size

N50

Total number of contigs

Longest contig size

Mis-assemblies on mapped contigs*

Approx. nts involved in mis-assemblies

Velvet

 

81

162295

40

340060

1 (10)

122303

83

170447

38

351373

1 (9)

122303

85

170449

37

351321

0 (10)

 

87

173763

33

351326

0 (10)

 

89

173765

33

351394

0 (10)

 

91

173767

33

351330

0 (10)

 

93

173769

35

340092

0 (10)

 

97

175770

33

365247

1 (9)

130273

99

175776

33

365260

1 (10)

130273

101

187438

32

365623

1 (9)

130273

103

187448

32

365625

1 (9)

130273

105

187458

32

365638

1 (9)

130273

107

187465

33

365647

1 (9)

130273

109

212189

32

365656

1 (9)

130273

111

212287

33

349286

2 (8)

93632 & 153207

113

212292

34

349288

1 (10)

93634

115

212294

34

349290

1 (10)

118928

117

174074

35

349419

1 (11)

93634

119

174076

35

349423

1 (11)

93638

121

170642

37

349435

0 (11)

 

123

170654

38

340456

0 (10)

 

*Number of mapped contigs indicated between brackets.

Figure 1

Alignment of contigs to the corresponding whole genome map: A) Velvet derived assembly using k-mer size 93, revealing no mis-assemblies; B) Velvet derived assembly using k-mer size 115, corresponding to the highest N50, but revealing mis-assemblies; C: SPAdes derived assembly using a multi- k-mer approach up to k-mer size 83, yielding the optimal N50 for this sequence and showing no mis-assemblies; D: SPAdes derived assembly using a multi- k-mer approach up to k-mer size 77, yielding the optimal N50 for this sequence, but showing mis-assemblies.

The general applicability of these results was investigated using two additional, similarly obtained, S. aureus sequences [UA-S391(accession # CP007690) and Mu50-CC5-SCCmec II (ATCC700699; previously sequenced and available under accession # NC_002758], again revealing mis-assemblies for Velvet at the highest N50 values, while error free assemblies could be obtained for lower k-mer sizes (data not shown). In addition, the sequence of Klebsiella pneumoniae ST258 was similarly generated using Illumina HiSeq-2000 via 2X150b paired end sequencing and was assembled using all three assembly tools. In this case, apart from mis-assemblies seen for Velvet, also SPAdes and IDBA were shown to produce mis-assemblies for certain k-mer sizes (Figure 1D), further demonstrating the potential of WGM to identify mis-assemblies, even for assemblers utilizing multi-k-mer approaches (Additional file 1: Table S2).

Conclusion

Genome assembly based on de Bruijn graphs potentially yields mis-assemblies when only considering standard parameters such as total number and length of the contigs and N50. However, Whole Genome Mapping provides a powerful tool to identify such mis-assemblies and to select the optimal k-mer sizes to produce optimally assembled genomes. Despite of its additional cost, the biological need for error-free and complete genomes makes WGM an indispensable technique during the process of genome assembly and its validation.

Declarations

Acknowledgements

Strains utilized in this study were obtained from the EU-FP6-MOSAR collection (MOZAR7-CC8-SCCmec III; http://www.mosar-sic.org), the European Staphylococcal Typing Network, HARMONY (E-MRSA15-CC22-SCCmec IV) and from the Network on Antimicrobial Resistance in Staphylococcus aureus Program, NARSA (Mu50-CC5-SCCmec II).

Funding

This work is supported by funding from Research Foundation Flanders (FWO-F, Research project no. G.0983.10N), and the European Community (PREPARE network contract FP7-HEALTH F3 2013- no. 602525). B.B.X. is supported by University of Antwerp Research funds (BOF-DOCPRO 2012–27450).

Authors’ Affiliations

(1)
Department of Medical Microbiology, Vaccine & Infectious Disease Institute, Universiteit Antwerpen
(2)
Viral Genetics Research Group, Vrije Universiteit Brussel
(3)
Structural Biology Brussels, Flanders Institute for Biotechnology (VIB), Vrije Universiteit Brussel

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Copyright

© Xavier et al.; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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