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Computational tools

Piercing the dark matter: bioinformatics of long-range sequencing and mapping

Nature Reviews Geneticsvolume 19pages329346 (2018) | Download Citation

Abstract

Several new genomics technologies have become available that offer long-read sequencing or long-range mapping with higher throughput and higher resolution analysis than ever before. These long-range technologies are rapidly advancing the field with improved reference genomes, more comprehensive variant identification and more complete views of transcriptomes and epigenomes. However, they also require new bioinformatics approaches to take full advantage of their unique characteristics while overcoming their complex errors and modalities. Here, we discuss several of the most important applications of the new technologies, focusing on both the currently available bioinformatics tools and opportunities for future research.

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Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Nature Reviews thanks Heng Li, René Warren and the other anonymous reviewer(s) for their contribution to the peer review of this work.

10X Genomics: https://www.10xgenomics.com/

Bionano Genomics: https://bionanogenomics.com/

Illumina: https://www.illumina.com/

Oxford Nanopore Technologies: https://nanoporetech.com/

Pacific Biosciences: http://www.pacb.com/

Proposed solutions to corruption of long-read sequencing files: https://github.com/samtools/hts-specs/issues/40

References

  1. 1.

    Goodwin, S., McPherson, J. D. & McCombie, W. R. Coming of age: ten years of next-generation sequencing technologies. Nat. Rev. Genet. 17, 333–351 (2016). This is a comprehensive Review of all major sequencing and mapping platforms, including a detailed discussion of their relative strengths and weaknesses.

  2. 2.

    The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).

  3. 3.

    Nik-Zainal, S. et al. Landscape of somatic mutations in 560 breast cancer whole-genome sequences. Nature 534, 47–54 (2016).

  4. 4.

    The Encode Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

  5. 5.

    Celniker, S. E. et al. Unlocking the secrets of the genome. Nature 459, 927–930 (2009).

  6. 6.

    Chaisson, M. J. et al. Resolving the complexity of the human genome using single-molecule sequencing. Nature 517, 608–611 (2015). This is the first major publication describing how PacBio long reads could be used for human genetics, showing that over 20,000 SVs are present in a typical human genome.

  7. 7.

    Roberts, R. J., Carneiro, M. O. & Schatz, M. C. The advantages of SMRT sequencing. Genome Biol. 14, 405 (2013).

  8. 8.

    Jain, M., Olsen, H. E., Paten, B. & Akeson, M. The Oxford Nanopore MinION: delivery of nanopore sequencing to the genomics community. Genome Biol. 17, 239 (2016).

  9. 9.

    Zheng, G. X. et al. Haplotyping germline and cancer genomes with high-throughput linked-read sequencing. Nat. Biotechnol. 34, 303–311 (2016).

  10. 10.

    Putnam, N. H. et al. Chromosome-scale shotgun assembly using an in vitro method for long-range linkage. Genome Res. 26, 342–350 (2016).

  11. 11.

    Burton, J. N. et al. Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions. Nat. Biotechnol. 31, 1119–1125 (2013).

  12. 12.

    Dudchenko, O. et al. De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science 356, 92–95 (2017).

  13. 13.

    Edge, P., Bafna, V. & Bansal, V. HapCUT2: robust and accurate haplotype assembly for diverse sequencing technologies. Genome Res. 27, 801–812 (2017). This paper describes the very flexible HapCUT2 phasing algorithm for use with short, long or linked reads, as well as Hi-C-based mate pairs.

  14. 14.

    Cao, H. et al. Rapid detection of structural variation in a human genome using nanochannel-based genome mapping technology. Gigascience 3, 34 (2014).

  15. 15.

    Jiao, Y. et al. Improved maize reference genome with single-molecule technologies. Nature 546, 524–527 (2017).

  16. 16.

    Berlin, K. et al. Assembling large genomes with single-molecule sequencing and locality-sensitive hashing. Nat. Biotechnol. 33, 623–630 (2015).

  17. 17.

    Pendleton, M. et al. Assembly and diploid architecture of an individual human genome via single-molecule technologies. Nat. Methods 12, 780–786 (2015).

  18. 18.

    Merker, J. D. et al. Long-read genome sequencing identifies causal structural variation in a Mendelian disease. Genet. Med. https://doi.org/10.1038/gim.2017.86 (2017).

  19. 19.

    Spies, N. et al. Genome-wide reconstruction of complex structural variants using read clouds. Nat. Methods 9, 915–920 (2017).

  20. 20.

    Sharon, D., Tilgner, H., Grubert, F. & Snyder, M. A single-molecule long-read survey of the human transcriptome. Nat. Biotechnol. 31, 1009–1014 (2013). This is one of the first reports describing how long-read sequencing can be used to detect novel isoforms in the human transcriptome.

  21. 21.

    Rand, A. C. et al. Mapping DNA methylation with high-throughput nanopore sequencing. Nat. Methods 14, 411–413 (2017). This paper presents one of the first methods able to detect methylation changes directly from Oxford Nanopore long-read sequencing. It can detect three cytosine variants and two adenine variants.

  22. 22.

    Simpson, J. T. et al. Detecting DNA cytosine methylation using nanopore sequencing. Nat. Methods 14, 407–410 (2017). This paper presents one of the first methods able to detect 5mC methylation changes directly from Oxford Nanopore long-read sequencing.

  23. 23.

    Phillippy, A. M. New advances in sequence assembly. Genome Res 27, xi–xiii (2017).

  24. 24.

    Bradnam, K. R. et al. Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species. Gigascience 2, 10 (2013).

  25. 25.

    Phillippy, A. M., Schatz, M. C. & Pop, M. Genome assembly forensics: finding the elusive mis-assembly. Genome Biol. 9, R55 (2008).

  26. 26.

    Nagarajan, N. & Pop, M. Sequence assembly demystified. Nat. Rev. Genet. 14, 157–167 (2013).

  27. 27.

    Ling, H. Q. et al. Draft genome of the wheat A-genome progenitor Triticum urartu. Nature 496, 87–90 (2013).

  28. 28.

    Li, R. et al. The sequence and de novo assembly of the giant panda genome. Nature 463, 311–317 (2010).

  29. 29.

    Koren, S. et al. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res. 27, 722–736 (2017). This study describes Canu, one of the most commonly used long-read assemblers supporting both PacBio and Oxford Nanopore data.

  30. 30.

    Chin, C. S. et al. Phased diploid genome assembly with single-molecule real-time sequencing. Nat. Methods 13, 1050–1054 (2016). This study describes FALCON-Unzip, the first long-read-based assembler reporting phased diploid contigs.

  31. 31.

    Jain, M. et al. Nanopore sequencing and assembly of a human genome with ultra-long reads. Nat. Biotechnol. https://doi.org/10.1038/nbt.4060 (2018).

  32. 32.

    Koren, S. et al. Hybrid error correction and de novo assembly of single-molecule sequencing reads. Nat. Biotechnol. 30, 693–700 (2012).

  33. 33.

    Goodwin, S. et al. Oxford Nanopore sequencing, hybrid error correction, and de novo assembly of a eukaryotic genome. Genome Res. 25, 1750–1756 (2015).

  34. 34.

    Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).

  35. 35.

    Zimin, A. V. et al. The MaSuRCA genome assembler. Bioinformatics 29, 2669–2677 (2013).

  36. 36.

    Chin, C. S. et al. Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data. Nat. Methods 10, 563–569 (2013). This study describes HGAP, the first non-hybrid long-read de novo assembler.

  37. 37.

    Nowoshilow, S. et al. The axolotl genome and the evolution of key tissue formation regulators. Nature 554, 50–55 (2018).

  38. 38.

    Broder, A. in SEQUENCES ‘97 Proceedings of the Compression and Complexity of Sequences. 21 (Washington, DC, 1997).

  39. 39.

    Chu, J., Mohamadi, H., Warren, R. L., Yang, C. & Birol, I. Innovations and challenges in detecting long read overlaps: an evaluation of the state-of-the-art. Bioinformatics 33, 1261–1270 (2017).

  40. 40.

    Myers, E. W. et al. A whole-genome assembly of Drosophila. Science 287, 2196–2204 (2000).

  41. 41.

    Venter, J. C. et al. The sequence of the human genome. Science 291, 1304–1351 (2001).

  42. 42.

    Miller, J. R. et al. Aggressive assembly of pyrosequencing reads with mates. Bioinformatics 24, 2818–2824 (2008).

  43. 43.

    Myers, G. Efficient local alignment discovery amongst noisy long reads. Lect. Notes Bioinf. 8701, 52–67 (2014).

  44. 44.

    Myers, E. W. The fragment assembly string graph. Bioinformatics 21 (Suppl. 2), ii79–ii85 (2005).

  45. 45.

    Li, H. Minimap and miniasm: fast mapping and de novo assembly for noisy long sequences. Bioinformatics 32, 2103–2110 (2016).

  46. 46.

    Loman, N. J., Quick, J. & Simpson, J. T. A complete bacterial genome assembled de novo using only nanopore sequencing data. Nat. Methods 12, 733–735 (2015).

  47. 47.

    Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE 9, e112963 (2014).

  48. 48.

    Gajer, P., Schatz, M. & Salzberg, S. L. Automated correction of genome sequence errors. Nucleic Acids Res. 32, 562–569 (2004).

  49. 49.

    Boza, V., Brejova, B. & Vinar, T. DeepNano: deep recurrent neural networks for base calling in MinION nanopore reads. PLoS ONE 12, e0178751 (2017).

  50. 50.

    Teng, H. et al. Chiron: Translating nanopore raw signal directly into nucleotide sequence using deep learning. Preprint at bioRxiv https://doi.org/10.1101/179531 (2017).

  51. 51.

    Mendelowitz, L. & Pop, M. Computational methods for optical mapping. Gigascience 3, 33 (2014).

  52. 52.

    Weisenfeld, N. I., Kumar, V., Shah, P., Church, D. M. & Jaffe, D. B. Direct determination of diploid genome sequences. Genome Res. 27, 757–767 (2017). This study describes the Supernova assembler for 10X Genomics linked reads, which reports phased diploid genomes.

  53. 53.

    Kuleshov, V., Snyder, M. P. & Batzoglou, S. Genome assembly from synthetic long read clouds. Bioinformatics 32, i216–i224 (2016).

  54. 54.

    Yeo, S., Coombe, L., Chu, J., Warren, R. L. & Birol, I. ARCS: scaffolding genome drafts with linked reads. Bioinformatics https://doi.org/10.1093/bioinformatics/btx675 (2017).

  55. 55.

    Adey, A. et al. In vitro, long-range sequence information for de novo genome assembly via transposase contiguity. Genome Res. 24, 2041–2049 (2014).

  56. 56.

    Ghurye, J., Pop, M., Koren, S., Bickhart, D. & Chin, C. S. Scaffolding of long read assemblies using long range contact information. BMC Genomics 18, 527 (2017).

  57. 57.

    Bickhart, D. M. et al. Single-molecule sequencing and chromatin conformation capture enable de novo reference assembly of the domestic goat genome. Nat. Genet. 49, 643–650 (2017).

  58. 58.

    English, A. C. et al. Mind the gap: upgrading genomes with Pacific Biosciences RS long-read sequencing technology. PLoS ONE 7, e47768 (2012).

  59. 59.

    Warren, R. L. RAILS and Cobbler: scaffolding and automated finishing of draft genomes using long DNA sequences. J. Open Source Software 1, 116 (2016).

  60. 60.

    Weischenfeldt, J., Symmons, O., Spitz, F. & Korbel, J. O. Phenotypic impact of genomic structural variation: insights from and for human disease. Nat. Rev. Genet. 14, 125–138 (2013).

  61. 61.

    Alkan, C., Coe, B. P. & Eichler, E. E. Genome structural variation discovery and genotyping. Nat. Rev. Genet. 12, 363–376 (2011).

  62. 62.

    Lupski, J. R. Structural variation mutagenesis of the human genome: Impact on disease and evolution. Environ. Mol. Mutag. 56, 419–436 (2015).

  63. 63.

    Chiang, C. et al. The impact of structural variation on human gene expression. Nat. Genet. 49, 692–699 (2017).

  64. 64.

    Jeffares, D. C. et al. Transient structural variations have strong effects on quantitative traits and reproductive isolation in fission yeast. Nat. Commun. 8, 14061 (2017).

  65. 65.

    Carvalho, C. M. & Lupski, J. R. Mechanisms underlying structural variant formation in genomic disorders. Nat. Rev. Genet. 17, 224–238 (2016).

  66. 66.

    Moncunill, V. et al. Comprehensive characterization of complex structural variations in cancer by directly comparing genome sequence reads. Nat. Biotechnol. 32, 1106–1112 (2014).

  67. 67.

    Trask, B. J. Human cytogenetics: 46 chromosomes, 46 years and counting. Nat. Rev. Genet. 3, 769–778 (2002).

  68. 68.

    Sebat, J. et al. Large-scale copy number polymorphism in the human genome. Science 305, 525–528 (2004).

  69. 69.

    Huddleston, J. et al. Discovery and genotyping of structural variation from long-read haploid genome sequence data. Genome Res. 27, 677–685 (2017).

  70. 70.

    Sudmant, P. H. et al. An integrated map of structural variation in 2,504 human genomes. Nature 526, 75–81 (2015).

  71. 71.

    English, A. C., Salerno, W. J. & Reid, J. G. PBHoney: identifying genomic variants via long-read discordance and interrupted mapping. BMC Bioinformatics 15, 180 (2014).

  72. 72.

    English, A. C. et al. Assessing structural variation in a personal genome-towards a human reference diploid genome. BMC Genomics 16, 286 (2015).

  73. 73.

    Sedlazeck, F. J. et al. Accurate detection of complex structural variations using single molecule sequencing. Preprint at bioRxiv https://doi.org/10.1101/169557 (2017). This study introduces an improved long-read mapping algorithm NGMLR and a comprehensive structural variation detection pipeline Sniffles.

  74. 74.

    Harewood, L. et al. Hi-C as a tool for precise detection and characterisation of chromosomal rearrangements and copy number variation in human tumours. Genome Biol. 18, 125 (2017).

  75. 75.

    Chaisson, M. J. & Tesler, G. Mapping single molecule sequencing reads using basic local alignment with successive refinement (BLASR): application and theory. BMC Bioinformatics 13, 238 (2012).

  76. 76.

    Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at arXiv arXiv:1303.3997 (2013).

  77. 77.

    Li, H. Minimap2: fast pairwise alignment for long nucleotide sequences. Preprint at arXiv arXiv:1708.01492 (2017). This paper introduces the very fast Minimap2 long-read aligner for both PacBio and Oxford Nanopore sequencing.

  78. 78.

    Bishara, A. et al. Read clouds uncover variation in complex regions of the human genome. Genome Res. 25, 1570–1580 (2015).

  79. 79.

    Kurtz, S. et al. Versatile and open software for comparing large genomes. Genome Biol. 5, R12 (2004).

  80. 80.

    Kielbasa, S. M., Wan, R., Sato, K., Horton, P. & Frith, M. C. Adaptive seeds tame genomic sequence comparison. Genome Res. 21, 487–493 (2011).

  81. 81.

    Nattestad, M. & Schatz, M. C. Assemblytics: a web analytics tool for the detection of variants from an assembly. Bioinformatics 32, 3021–3023 (2016).

  82. 82.

    Mohiyuddin, M. et al. MetaSV: an accurate and integrative structural-variant caller for next generation sequencing. Bioinformatics 31, 2741–2744 (2015).

  83. 83.

    Thorvaldsdottir, H., Robinson, J. T. & Mesirov, J. P. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief. Bioinform. 14, 178–192 (2013).

  84. 84.

    Nattestad, M., Chin, C. S. & Schatz, M. C. Ribbon: visualizing complex genome alignments and structural variation. Preprint at bioRxiv https://doi.org/10.1101/082123 (2016).

  85. 85.

    Narzisi, G. et al. Accurate de novo and transmitted indel detection in exome-capture data using microassembly. Nat. Methods 11, 1033–1036 (2014).

  86. 86.

    Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

  87. 87.

    Browning, S. R. & Browning, B. L. Haplotype phasing: existing methods and new developments. Nat. Rev. Genet. 12, 703–714 (2011).

  88. 88.

    Tewhey, R., Bansal, V., Torkamani, A., Topol, E. J. & Schork, N. J. The importance of phase information for human genomics. Nat. Rev. Genet. 12, 215–223 (2011).

  89. 89.

    McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

  90. 90.

    Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

  91. 91.

    Luo, R., Schatz, M. C. & Salzberg, S. L. 16GT: a fast and sensitive variant caller using a 16-genotype probabilistic model. Gigascience 6, 1–4 (2017).

  92. 92.

    Cilibrasi, R., Iersel, L. v., Kelk, S. & Tromp, J. The complexity of the single individual SNP haplotyping problem. Algorithmica 49, 13–36 (2007).

  93. 93.

    Lo, C., Bashir, A., Bansal, V. & Bafna, V. Strobe sequence design for haplotype assembly. BMC Bioinformatics 12, S24 (2011).

  94. 94.

    Rozowsky, J. et al. AlleleSeq: analysis of allele-specific expression and binding in a network framework. Mol. Syst. Biol. 7, 522 (2011).

  95. 95.

    Lynch, K. W. & Maniatis, T. Assembly of specific SR protein complexes on distinct regulatory elements of the Drosophila doublesex splicing enhancer. Genes Dev. 10, 2089–2101 (1996).

  96. 96.

    Pan, Q., Shai, O., Lee, L. J., Frey, B. J. & Blencowe, B. J. Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat. Genet. 40, 1413–1415 (2008).

  97. 97.

    Harrow, J. et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774 (2012).

  98. 98.

    Wang, Z., Gerstein, M. & Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57–63 (2009).

  99. 99.

    Conesa, A. et al. A survey of best practices for RNA-seq data analysis. Genome Biol. 17, 13 (2016).

  100. 100.

    Abdel-Ghany, S. E. et al. A survey of the sorghum transcriptome using single-molecule long reads. Nat. Commun. 7, 11706 (2016).

  101. 101.

    Byrne, A. et al. Nanopore long-read RNAseq reveals widespread transcriptional variation among the surface receptors of individual B cells. Nat. Commun. 8, 16027 (2017).

  102. 102.

    Garalde, D. R. et al. Highly parallel direct RNA sequencing on an array of nanopores. Nat. Methods https://doi.org/10.1038/nmeth.4577 (2018). This is the first demonstration of direct RNA sequencing on an Oxford Nanopore MinION sequencer.

  103. 103.

    Tilgner, H. et al. Comprehensive transcriptome analysis using synthetic long-read sequencing reveals molecular co-association of distant splicing events. Nat. Biotechnol. 33, 736–742 (2015).

  104. 104.

    Wang, B. et al. Unveiling the complexity of the maize transcriptome by single-molecule long-read sequencing. Nat. Commun. 7, 11708 (2016).

  105. 105.

    Gordon, S. P. et al. Widespread polycistronic transcripts in fungi revealed by single-molecule mRNA sequencing. PLoS ONE 10, e0132628 (2015). This paper describes the ToFU algorithm for studying alternative splicing and isoform diversity using long-read sequencing.

  106. 106.

    Tardaguila, M. et al. SQANTI: extensive characterization of long read transcript sequences for quality control in full-length transcriptome identification and quantification. Genome Res. https://doi.org/10.1101/gr.222976.117 (2018).

  107. 107.

    Au, K. F. et al. Characterization of the human ESC transcriptome by hybrid sequencing. Proc. Natl Acad. Sci. USA 110, E4821–E4830 (2013).

  108. 108.

    Deonovic, B., Wang, Y., Weirather, J., Wang, X. J. & Au, K. F. IDP-ASE: haplotyping and quantifying allele-specific expression at the gene and gene isoform level by hybrid sequencing. Nucleic Acids Res. 45, e32 (2017).

  109. 109.

    Zheng, G. X. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).

  110. 110.

    Lister, R. & Ecker, J. R. Finding the fifth base: genome-wide sequencing of cytosine methylation. Genome Res. 19, 959–966 (2009).

  111. 111.

    Dinh, H. Q. et al. Advanced methylome analysis after bisulfite deep sequencing: an example in Arabidopsis. PLoS ONE 7, e41528 (2012).

  112. 112.

    Flusberg, B. A. et al. Direct detection of DNA methylation during single-molecule, real-time sequencing. Nat. Methods 7, 461–465 (2010). This is one of the first demonstrations of the ability to directly detect methylated bases using PacBio long-read sequencing.

  113. 113.

    Fang, G. et al. Genome-wide mapping of methylated adenine residues in pathogenic Escherichia coli using single-molecule real-time sequencing. Nat. Biotechnol. 30, 1232–1239 (2012).

  114. 114.

    Greer, E. L. et al. DNA methylation on N6-adenine in C. elegans. Cell 161, 868–878 (2015).

  115. 115.

    Graralde, D. R. et al. Highly parallel direct RNA sequencing on an array of nanopores. Nat Methods https://doi.org/10.1038/nmeth.4577 (2018).

  116. 116.

    Zimin, A. V. et al. The first near-complete assembly of the hexaploid bread wheat genome, Triticum aestivum. Gigascience 6, 1–7 (2017).

  117. 117.

    Poplin, R. et al. Creating a universal SNP and small indel variant caller with deep neural networks. Preprint at bioRxiv https://doi.org/10.1101/092890 (2016).

  118. 118.

    Danko, C. D., Meleshko, D., Bezcan, D., Mason, C. E. & Hajirasouliha, I. Minerva: an alignment and reference free approach to deconvolve linked-reads for metagenomics. Preprint at bioRxiv https://doi.org/10.1101/217869 (2017).

  119. 119.

    Tsai, Y. C. et al. Resolving the complexity of human skin metagenomes using single-molecule sequencing. MBio 7, e01948–01915 (2016).

  120. 120.

    Burton, J. N., Liachko, I., Dunham, M. J. & Shendure, J. Species-level deconvolution of metagenome assemblies with Hi-C-based contact probability maps. G3 4, 1339–1346 (2014).

  121. 121.

    Novak, A. M. et al. Genome graphs. bioRxiv https://doi.org/10.1101/101378 (2017).

  122. 122.

    Church, D. M. et al. Extending reference assembly models. Genome Biol. 16, 13 (2015).

  123. 123.

    Matzaraki, V., Kumar, V., Wijmenga, C. & Zhernakova, A. The MHC locus and genetic susceptibility to autoimmune and infectious diseases. Genome Biol. 18, 76 (2017).

  124. 124.

    Mayor, N. P. et al. HLA typing for the next generation. PLoS ONE 10, e0127153 (2015).

  125. 125.

    Hayward, D. R., Bultitude, W. P., Mayor, N. P., Madrigal, J. A. & Marsh, S. G. The novel HLA-B*44 allele, HLA-B*44:220, identified by single molecule real-time DNA sequencing in a British caucasoid male. Tissue Antigens 86, 61–63 (2015).

  126. 126.

    Wang, M. et al. PacBio-LITS: a large-insert targeted sequencing method for characterization of human disease-associated chromosomal structural variations. BMC Genomics 16, 214 (2015).

  127. 127.

    Nattestad, M. et al. Complex rearrangements and oncogene amplifications revealed by long-read DNA and RNA sequencing of a breast cancer cell line. Preprint at bioRxiv https://doi.org/10.1101/174938 (2017).

  128. 128.

    Quick, J. et al. Real-time, portable genome sequencing for Ebola surveillance. Nature 530, 228–232 (2016).

  129. 129.

    Faria, N. R. et al. Mobile real-time surveillance of Zika virus in Brazil. Genome Med. 8, 97 (2016).

  130. 130.

    Schatz, M. C. & Phillippy, A. M. The rise of a digital immune system. Gigascience 1, 4 (2012).

  131. 131.

    Biesecker, L. G. & Green, R. C. Diagnostic clinical genome and exome sequencing. N. Engl. J. Med. 370, 2418–2425 (2014).

  132. 132.

    Schatz, M. C., Witkowski, J. & McCombie, W. R. Current challenges in de novo plant genome sequencing and assembly. Genome Biol. 13, 243 (2012).

  133. 133.

    Eberle, M. A. et al. A reference data set of 5.4 million phased human variants validated by genetic inheritance from sequencing a three-generation 17-member pedigree. Genome Res. 27, 157–164 (2017).

  134. 134.

    Schatz, M. C. Nanopore sequencing meets epigenetics. Nat. Methods 14, 347–348 (2017).

  135. 135.

    Kamath, G. M., Shomorony, I., Xia, F., Courtade, T. A. & Tse, D. N. HINGE: long-read assembly achieves optimal repeat resolution. Genome Res. 27, 747–756 (2017).

  136. 136.

    Xiao, C. L. et al. MECAT: fast mapping, error correction, and de novo assembly for single-molecule sequencing reads. Nat. Methods 14, 1072–1074 (2017).

  137. 137.

    Lin, Y. et al. Assembly of long error-prone reads using de Bruijn graphs. Proc. Natl Acad. Sci. USA 113, E8396–E8405 (2016).

  138. 138.

    Warren, R. L. et al. LINKS: Scalable, alignment-free scaffolding of draft genomes with long reads. Gigascience 4, 35 (2015).

  139. 139.

    Cao, M. D. et al. Scaffolding and completing genome assemblies in real-time with nanopore sequencing. Nat. Commun. 8, 14515 (2017).

  140. 140.

    Vaser, R., Sovic, I., Nagarajan, N. & Sikic, M. Fast and accurate de novo genome assembly from long uncorrected reads. Genome Res. 27, 737–746 (2017).

  141. 141.

    Sovic, I. et al. Fast and sensitive mapping of nanopore sequencing reads with GraphMap. Nat. Commun. 7, 11307 (2016).

  142. 142.

    Lin, H. N. & Hsu, W. L. Kart: a divide-and-conquer algorithm for NGS read alignment. Bioinformatics 33, 2281–2287 (2017).

  143. 143.

    Liu, B., Gao, Y. & Wang, Y. LAMSA: fast split read alignment with long approximate matches. Bioinformatics 33, 192–201 (2017).

  144. 144.

    Elyanow, R., Wu, H. T. & Raphael, B. J. Identifying structural variants using linked-read sequencing data. Bioinformatics https://doi.org/10.1093/bioinformatics/btx712 (2017).

  145. 145.

    Patterson, M. et al. WhatsHap: weighted haplotype assembly for future-generation sequencing reads. J. Comput. Biol. 22, 498–509 (2015). This study describes WhatsHap, a widely used and very fast phasing algorithm for long reads.

  146. 146.

    Kent, W. J. BLAT—the BLAST-like alignment tool. Genome Res. 12, 656–664 (2002).

  147. 147.

    Wu, T. D. & Watanabe, C. K. GMAP: a genomic mapping and alignment program for mRNA and EST sequences. Bioinformatics 21, 1859–1875 (2005).

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Acknowledgements

The authors thank A. Phillippy, W. Timp, W. R. McCombie, S. Goodwin and R. Gibbs for helpful discussions. This work was supported, in part, by awards from the National Science Foundation (DBI-1350041) and from the National Institutes of Health (R01-HG006677 and UM1-HG008898). Also, this work was completed in part while H.L. was visiting the Simons Institute for the Theory of Computing, University of California, Berkeley, USA.

Author information

Affiliations

  1. Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA

    • Fritz J. Sedlazeck
  2. Department of Genetics, Stanford University, Stanford, CA, USA

    • Hayan Lee
  3. Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA

    • Charlotte A. Darby
    •  & Michael C. Schatz
  4. Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA

    • Michael C. Schatz

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Contributions

All authors contributed to all aspects of this manuscript, including researching data, discussing content and writing, reviewing and editing the manuscript before submission.

Competing interests

M.C.S. and F.J.S. have participated in Pacific Biosciences (PacBio) sponsored meetings over the past few years and have received travel reimbursement and honoraria for presenting at these events. PacBio had no role in decisions relating to the study and/or work to be published, data collection and analysis or the decision to publish.

Corresponding author

Correspondence to Michael C. Schatz.

Glossary

Mate pairs

A molecular technique to generate a pair of sequencing reads separated by an approximately known distance. The typical separation distance for mate pairs is a few kilobases, as opposed to paired-end sequencing, which separates the reads by a few hundred bases at most.

Optical mapping

A microscopy technique used to visualize the characteristics of DNA, especially the physical lengths or the position of fluorescent probes.

Indels

A type of DNA sequence variation marked by the insertion or deletion of nucleotides.

Structural variants

(SVs). DNA sequence variants that are 50 bp or larger, including insertions, deletions, inversions, duplications and translocations.

Linked reads

Also known as a read cloud. A set of barcoded short reads derived from the same DNA molecule and therefore highly localized in the genome.

Phased

Grouping together variants located on the same molecule, such as to identify variants from the maternal or the paternal genome in a diploid sample.

Scaffolding

The process of assembling sequences of DNA into a scaffold. A scaffold is similar to a contig but may contain gaps, typically represented as Ns in the sequence.

Contigs

Contiguously assembled sequences of DNA.

N50

A weighted average length; specifically, the N50 length is the length such that 50% of the genome has been assembled into contig or scaffold sequences of this length or longer.

Cis-regulation

Any molecular interaction that regulates the transcription of nearby genes on the same DNA molecule, such as the role of a gene promoter.

Trans-regulation

Any molecular interaction that regulates the transcription of genes on a different DNA molecule, such as a transcription factor regulating both alleles of a target gene or genes.

Topologically associating domains

(TADs). Regions of the genome that are enriched for interactions with other elements within the same domain.

Synteny blocks

Genomic regions that are conserved among multiple species.

Fragile sites

Regions of the DNA molecule that are prone to physical shearing, especially when multiple nicking sites targeted by a nicking enzyme are located in close proximity.

Nested

Two or more adjacent or even overlapping variants in the same region of the genome, such as a deletion within the middle of a larger inverted sequence.

Chromothripsis

A phenomenon by which many chromosomal rearrangements occur in a single event in a localized region of the genome. Also called chromosome shattering.

Chromoplexy

A complex mutation where genetic material from multiple chromosomes is broken and ligated to each other in a new configuration, especially in cancer.

Polyploid

Cells and organisms that contain more than two paired (homologous) sets of chromosomes.

Polymorphisms

Variants observed in the genome that are present to some appreciable degree within a population (for example, >1%).

Compound heterozygous

Two different mutant alleles at a particular gene locus, one on each chromosome of a pair.

Hemizygous mutations

Two or more heterozygous mutations, especially loss-of-function mutations, occurring on the same chromosome so that they disrupt one copy of the gene but leave one functional copy.

Private mutations

Rare variants observed only within a single person or family.

NP-hard

In computational complexity theory, this is a class of problems in which no fast solutions are known to exist.

Metagenomes

The genomes of all the species present in a sample, studied without culturing or otherwise isolating any individual.

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DOI

https://doi.org/10.1038/s41576-018-0003-4

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