Open Access

LASER: Large genome ASsembly EvaluatoR

BMC Research Notes20158:709

DOI: 10.1186/s13104-015-1682-y

Received: 16 September 2015

Accepted: 9 November 2015

Published: 24 November 2015

Abstract

Background

Genome assembly is a fundamental problem with multiple applications. Current technological limitations do not allow assembling of entire genomes and many programs have been designed to produce longer and more reliable contigs. Assessing the quality of these assemblies and comparing those produced by different tools is essential in choosing the best ones. The QUAST program has become the current state-of-the-art in quality assessment of genome assemblies. The only drawback of QUAST is high time and memory usage for large genomes, e.g., over 4 days and 120 GB of RAM for a single human genome assembly.

Results

We introduce LASER, a new tool for assembly evaluation that improves greatly the speed and memory requirements of QUAST. For a human genome assembly, LASER is 5.6 times faster than QUAST while using only half the memory; one human genome assembly is evaluated in 17 hours instead of 4 days. The code of LASER is based on that of QUAST and therefore inherits all its features.

Conclusions

Genome assembly evaluation is an essential step in assessing the quality of an assembly that is too often done improperly, in part due to significant resource consumption. With the introduction of LASER, proper evaluation can be performed efficiently.

Keywords

Bioinformatics DNA sequencing Genome assembly Assembly evaluation

Background

The current sequencing technologies produce short pieces of DNA, called reads, that need to be assembled together to reconstruct the original genome. Usually, whole genomes cannot be produced and instead the assembling programs produce longer DNA pieces, called contigs. High quality assemblies require longer and more accurate contigs. Genome assembly is a difficult problem that is far from being solved. A multitude of assemblers have been designed, see, e.g., [111].

Comparing the quality of two assemblies is already nontrivial; one may have longer contigs while the other may have fewer misassembles. Given the large number of tools available, choosing the best one for, say, building a new pipeline, becomes a difficult problem. Evaluating the assembly quality for an assembler during the designing stage is essential as well. Therefore, fast and effective evaluation of genome assembly quality is of crucial importance and a number of solutions have been proposed [1217]. The most comprehensive evaluation is currently provided by the QUAST program [17]. QUAST quickly became the current state-of-the-art in assembly evaluation. Its thorough evaluation, new metrics, and useful visualizations made it achieve widespread use. Its only drawback is the high time and memory usage for large genome assemblies. In most cases, it requires over 4 days and 120 GB of RAM to assess the quality of a single human genome assembly.

To remedy this problem we have designed LASER: Large genome ASsembly EvaluatoR. LASER’s code is based on that of QUAST, inheriting all its features and advantages. We describe below the essential improvements implemented in LASER and compare its performance with that of QUAST on several human datasets.

Methods

The most time consuming stage of QUAST is, by far, the maximal exact match (MEM) computation step of the alignment process, performed using the NUCmer aligner from MUMmer v3.23 [18]. Our recent E-MEM tool [19] clearly outperforms not only MUMmer but also the currently best tools for MEM computation in large genomes: [2024]. It was therefore a natural choice for replacing MUMmer.

Besides using E-MEM, we performed a number of other improvements as well. A large number of redundant string copy operations on large strings in the ‘show-snp’ utility program of the MUMmer toolkit have been avoided. The memory and performance of Python code was improved by replacing class objects with tuples.

The rest of QUAST code has been reused in LASER. MUMmer and GlimmerHMM [25] are open source and the authors of GeneMarkS [26] have kindly allowed us to use their code in LASER.

Results

As mentioned before, all features of QUAST have been preserved and LASER has been designed to be used exactly the same way as QUAST. That is, LASER produces exactly the same output. The advantage of LASER consists of greatly increased speed and reduced memory usage. To prove these claims, we have compared LASER and QUAST on several datasets, presented in Table 1. As we are interested in improvement when it really matters, that is, for large genomes, all datasets are human. They were all produced by Illumina HiSeq2000 machines. All datasets were assembled using SOAPdenovo2 [6]. We used SOAPdenovo2 because of its good speed. The k-mer size producing the best assembly (as indicated by the aligned N50 size) was used. This was \(k=65\) for \(\mathrm H_1\) and \(k=71\) for the other datasets. The assemblies are available for download from the website of LASER.

All tests were performed on a DELL PowerEdge R620 computer with 12 cores Intel Xeon at 2.0'GHz and 256 GB of RAM, running Linux Red Hat, CentOS 6.3.
Table 1

The datasets used for comparison; accession numbers are included for the datasets and for the corresponding reference genomes

Dataset

Organism

Accession number

Read length

Number of reads

Total bp

Depth of coverage

Reference genome

Genome length

\(\mathrm H_1\)

Homo sapiens

SRR1302280

101

1,287,175,558

130,004,731,358

41

Build 38

3,209,286,105

\(\mathrm H_2\)

Homo sapiens

ERR194146

101

1,626,361,156

164,262,476,756

51

Build 38

3,209,286,105

\(\mathrm H_3\)

Homo sapiens

ERR194147

101

1,574,530,218

159,027,552,018

50

Build 38

3,209,286,105

\(\mathrm H_4\)

Homo sapiens

ERR324433

101

1,614,713,636

163,086,077,236

51

Build 38

3,209,286,105

\(\mathrm H_5\)

Homo sapiens

ERX069505

101

1,708,169,546

172,525,124,146

54

Build 38

3,209,286,105

Fig. 1

Comparison. Visual comparison of the time (left plot) and memory (right plot) between QUAST and LASER

Figure 1 gives the time and memory comparison between QUAST and LASER on the SOAPdenovo2 assemblies produced from the datasets in Table 1. LASER is 5.6 times faster than QUAST while using half the memory.

Conclusions

We hope that the improvement in genome assembly evaluation provided by LASER will further boost the use of thorough quality evaluation. N50 is still used as the most important parameter. (N50 is the length l such that the sum of the lengths of all contigs of length l or more is at least half of the total length of all contigs.) An aggressive assembler will produce a high N50 but at the cost of many misassemblies, thus lowering the overall quality. Therefore, a combination of parameters, as provided by QUAST or LASER, gives a much better evaluation of the actual assembly quality.

Availability and requirements

  • Project name: LASER

  • Project home page: http://www.csd.uwo.ca/~ilie/LASER/

  • Operating system(s): UNIX, Linux, Mac OS X

  • Programming language: C++, OpenMP

  • License: see web page

  • Any restrictions to use by non-academics: licence needed.

Declarations

Authors' contributions

LI suggested the improved assembly evaluation software by using E-MEM and wrote the manuscript. NK designed the other improvements, implemented and tested LASER, and performed all comparisons. Both authors read and approved the final manuscript.

Acknowledgements

Evaluation has been performed on our Shadowfax cluster, which is part of the Shared Hierarchical Academic Research Computing Network (SHARCNET: http://www.sharcnet.ca) and Compute/Calcul Canada. We would like to thank Mark Borodovsky for allowing the use of GeneMarkS.

Funding

L.I. has been partially supported by a Discovery Grant and a Research Tools and Instruments Grant from the Natural Sciences and Engineering Research Council of Canada (NSERC).

Competing interests

The authors declare that they have no competing interests.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.

Authors’ Affiliations

(1)
Department of Computer Science, University of Western Ontario

References

  1. Dohm JC, Lottaz C, Borodina T, Himmelbauer H. SHARCGS, a fast and highly accurate short-read assembly algorithm for de novo genomic sequencing. Genome Res. 2007;17(11):1697–706.PubMed CentralView ArticlePubMedGoogle Scholar
  2. Zerbino DR, Birney E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 2008;18(5):821–9.PubMed CentralView ArticlePubMedGoogle Scholar
  3. Butler J, MacCallum I, Kleber M, et al. ALLPATHS: de novo assembly of whole-genome shotgun microreads. Genome Res. 2008;18:810–20.PubMed CentralView ArticlePubMedGoogle Scholar
  4. Simpson JT, Wong K, Jackman SD, et al. ABySS: a parallel assembler for short read sequence data. Genome Res. 2009;19:1117–23.Google Scholar
  5. Li R, Zhu H, Ruan J, et al. De novo assembly of human genomes with massively parallel short read sequencing. Genome Res. 2010;20:265–72.Google Scholar
  6. Luo R, Liu B, Xie Y, Li Z, Huang W, Yuan J, He G, Chen Y, Pan Q, Liu Y, et al. SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. GigaScience. 2012;1(1):18.PubMed CentralView ArticlePubMedGoogle Scholar
  7. Simpson JT, Durbin R. Efficient de novo assembly of large genomes using compressed data structures. Genome Res. 2012;22:549–56.PubMed CentralView ArticlePubMedGoogle Scholar
  8. Li H. Exploring single-sample SNP and INDEL calling with whole-genome de novo assembly. Bioinformatics. 2012;28(14):1838–44.PubMed CentralView ArticlePubMedGoogle Scholar
  9. Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, Lesin VM, Nikolenko SI, Pham S, Prjibelski AD, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19(5):455–77.PubMed CentralView ArticlePubMedGoogle Scholar
  10. Zimin AV, Marçais G, Puiu D, Roberts M, Salzberg SL, Yorke JA. The MaSuRCA genome assembler. Bioinformatics. 2013;29(21):2669–77.PubMed CentralView ArticlePubMedGoogle Scholar
  11. Ilie L, Haider B, Molnar M, Solis-Oba R. SAGE: String-overlap Assembly of GEnomes. BMC Bioinf. 2014;15(1):302.View ArticleGoogle Scholar
  12. Barthelson R, McFarlin AJ, Rounsley SD, Young S. Plantagora: modeling whole genome sequencing and assembly of plant genomes. PLoS One. 2011;6(12):28436.View ArticleGoogle Scholar
  13. Earl D, Bradnam K, John JS, Darling A, Lin D, Fass J, Yu HOK, Buffalo V, Zerbino DR, Diekhans M, et al. Assemblathon 1: a competitive assessment of de novo short read assembly methods. Genome Res. 2011;21(12):2224–41.PubMed CentralView ArticlePubMedGoogle Scholar
  14. Salzberg SL, Phillippy AM, Zimin A, Puiu D, Magoc T, Koren S, Treangen TJ, Schatz MC, Delcher AL, Roberts M, et al. GAGE: A critical evaluation of genome assemblies and assembly algorithms. Genome Res. 2012;22(3):557–67.PubMed CentralView ArticlePubMedGoogle Scholar
  15. Bradnam KR, Fass JN, Alexandrov A, Baranay P, Bechner M, Birol I, Boisvert S, Chapman JA, Chapuis G, Chikhi R, et al. Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species. GigaScience. 2013;2(1):1–31.View ArticleGoogle Scholar
  16. Magoc T, Pabinger S, Canzar S, Liu X, Su Q, Puiu D, Tallon LJ, Salzberg SL. GAGE-B: an evaluation of genome assemblers for bacterial organisms. Bioinformatics. 2013;29(14):1718–25.PubMed CentralView ArticlePubMedGoogle Scholar
  17. Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality assessment tool for genome assemblies. Bioinformatics. 2013;29(8):1072–5.PubMed CentralView ArticlePubMedGoogle Scholar
  18. Kurtz S, Phillippy A, Delcher AL, Smoot M, Shumway M, Antonescu C, Salzberg SL. Versatile and open software for comparing large genomes. Genome Biol. 2004;5(2):12.View ArticleGoogle Scholar
  19. Khiste N, Ilie L. E-MEM: efficient computation of maximal exact matches for very large genomes. Bioinformatics. 2015;31(4):509–14.View ArticlePubMedGoogle Scholar
  20. Abouelhoda MI, Kurtz S, Ohlebusch E. Replacing suffix trees with enhanced suffix arrays. J Discret Algorithms. 2004;2(1):53–86.View ArticleGoogle Scholar
  21. Vyverman M, De Baets B, Fack V, Dawyndt P. essaMEM: finding maximal exact matches using enhanced sparse suffix arrays. Bioinformatics. 2013;29(6):802–4.View ArticlePubMedGoogle Scholar
  22. Fernandes F, Freitas AT. slaMEM: efficient retrieval of maximal exact matches using a sampled LCP array. Bioinformatics. 2013;706.Google Scholar
  23. Ohlebusch E, Gog S, Kügel A. Computing matching statistics and maximal exact matches on compressed full-text indexes. In: String processing and information retrieval. 2010. Berlin: Springer. p. 347–58. Google Scholar
  24. Khan Z, Bloom JS, Kruglyak L, Singh M. A practical algorithm for finding maximal exact matches in large sequence datasets using sparse suffix arrays. Bioinformatics. 2009;25(13):1609–16.PubMed CentralView ArticlePubMedGoogle Scholar
  25. Majoros WH, Pertea M, Salzberg SL. TigrScan and GlimmerHMM: two open source ab initio eukaryotic gene-finders. Bioinformatics. 2004;20(16):2878–9.View ArticlePubMedGoogle Scholar
  26. Besemer J, Lomsadze A, Borodovsky M. Genemarks: a self-training method for prediction of gene starts in microbial genomes. implications for finding sequence motifs in regulatory regions. Nucleic Acids Res. 2001;29(12):2607–18.PubMed CentralView ArticlePubMedGoogle Scholar

Copyright

© Khiste and Ilie. 2015