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Comprehensive comparison of Pacific Biosciences and Oxford Nanopore Technologies and their applications to transcriptome analysis

Weirather, Jason L, de Cesare, Mariateresa, Wang, Yunhao, Piazza, Paolo, Sebastiano, Vittorio, Wang, Xiu-Jie, Buck, David, Au, Kin Fai·2017·F1000Research
DOI 10.12688/f1000research.10571.2PMC PMC5553090
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Abstract

Background: Given the demonstrated utility of Third Generation Sequencing [Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT)] long reads in many studies, a comprehensive analysis and comparison of their data quality and applications is in high demand. Methods: Based on the transcriptome sequencing data from human embryonic stem cells, we analyzed multiple data features of PacBio and ONT, including error pattern, length, mappability and technical improvements over previous platforms. We also evaluated their application to transcriptome analyses, such as isoform identification and quantification and characterization of transcriptome complexity, by comparing the performance of size-selected PacBio, non-size-selected ONT and their corresponding Hybrid-Seq strategies (PacBio+Illumina and ONT+Illumina). Results: PacBio shows overall better data quality, while ONT provides a higher yield. As with data quality, PacBio performs marginally better than ONT in most aspects for both long reads only and Hybrid-Seq strategies in transcriptome analysis. In addition, Hybrid-Seq shows superior performance over long reads only in most transcriptome analyses. Conclusions: Both PacBio and ONT sequencing are suitable for full-length single-molecule transcriptome analysis. As this first use of ONT reads in a Hybrid-Seq analysis has shown, both PacBio and ONT can benefit from a combined Illumina strategy. The tools and analytical methods developed here provide a resource for future applications and evaluations of these rapidly-changing technologies.

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Figures

Figure 1. Length distribution of reads. The length distribution of Oxford Nanopore Technologies (ONT) 2D and 1D reads (top) and Pacific Biosciences (PacBio) CCS and subreads (bottom). Aligned reads are color-coded to indicate fraction of reads that are: single best alignments (gray), gapped alignments consisting of multiple paths (red), self-chimeric alignments (purple) where different read segments map to overlapping sequences, and trans-chimeric alignments (blue) where read segments map to different loci; white color represents unaligned reads. The leftmost bar represents all reads, the middle portion reads from 0–4kb in length, and the rightmost are reads greater than 4kb. PacBio libraries were size-selected, while ONT libraries were not; this provides PacBio with a larger proportion of longer reads. The total number of reads sequenced and the number of aligned reads from each sequencing platform are available in Supplementary Table 2.
Figure 1. Length distribution of reads. The length distribution of Oxford Nanopore Technologies (ONT) 2D and 1D reads (top) and Pacific Biosciences (PacBio) CCS and subreads (bottom). Aligned reads are color-coded to indicate fraction of reads that are: single best alignments (gray), gapped alignments consisting of multiple paths (red), self-chimeric alignments (purple) where different read segments map to overlapping sequences, and trans-chimeric alignments (blue) where read segments map to different loci; white color represents unaligned reads. The leftmost bar represents all reads, the middle portion reads from 0–4kb in length, and the rightmost are reads greater than 4kb. PacBio libraries were size-selected, while ONT libraries were not; this provides PacBio with a larger proportion of longer reads. The total number of reads sequenced and the number of aligned reads from each sequencing platform are available in Supplementary Table 2.CC BY · PubMed Central
Figure 2. Mappability of different length bins. The leftmost bar represents the fraction of the mappable read length out of the total read length for all reads. The middle section shows the mappable fraction for 500bp increments ranging from 0–4kb read lengths, and the rightmost bar represents the mappable fraction of reads greater than 4kb. ONT: non-size-selected Oxford Nanopore Technologies reads; PacBio: size-selected Pacific Biosciences reads. The numbers of aligned reads contributing to the box plots in each panel are listed above each panel: total aligned reads, aligned reads <4kb and aligned reads >4kb (from left to right).
Figure 2. Mappability of different length bins. The leftmost bar represents the fraction of the mappable read length out of the total read length for all reads. The middle section shows the mappable fraction for 500bp increments ranging from 0–4kb read lengths, and the rightmost bar represents the mappable fraction of reads greater than 4kb. ONT: non-size-selected Oxford Nanopore Technologies reads; PacBio: size-selected Pacific Biosciences reads. The numbers of aligned reads contributing to the box plots in each panel are listed above each panel: total aligned reads, aligned reads <4kb and aligned reads >4kb (from left to right).CC BY · PubMed Central
Figure 3. Context-specific errors. Context specific-errors are shown for Oxford Nanopore Technologies (ONT) 2D and 1D reads (top), and Pacific Biosciences (PacBio) CCS and subreads (bottom). The error types shown are insertions, deletions and mismatches. For insertions, the large base above the plot indicates the inserted base, and for deletions, the deleted base. For mismatch errors, the large base to the left indicates the expected reference base, and the large base above indicates the base observed in the read. A block of color tiles shows the error frequency within specific contexts for each error; the small base to the left of the tiles indicates the base preceding the error, and the small base above is the base following error. Error frequency is plotted on separate scales for insertions, deletions, and mismatches. Homopolymer error patterns are highlighted with a bold cross- or L-shaped outlines in the ONT 2D, PacBio CCS and PacBio Subreads plots. Context-specific insertions and mismatches of interest in the ONT 1D, 2D and PacBio CCS reads are highlighted by a bold outlines. For a better contrast of lower error rate in PacBio CCS reads and ONT 2D reads, Supplementary Figure S4 displays each result with its own scale.
Figure 3. Context-specific errors. Context specific-errors are shown for Oxford Nanopore Technologies (ONT) 2D and 1D reads (top), and Pacific Biosciences (PacBio) CCS and subreads (bottom). The error types shown are insertions, deletions and mismatches. For insertions, the large base above the plot indicates the inserted base, and for deletions, the deleted base. For mismatch errors, the large base to the left indicates the expected reference base, and the large base above indicates the base observed in the read. A block of color tiles shows the error frequency within specific contexts for each error; the small base to the left of the tiles indicates the base preceding the error, and the small base above is the base following error. Error frequency is plotted on separate scales for insertions, deletions, and mismatches. Homopolymer error patterns are highlighted with a bold cross- or L-shaped outlines in the ONT 2D, PacBio CCS and PacBio Subreads plots. Context-specific insertions and mismatches of interest in the ONT 1D, 2D and PacBio CCS reads are highlighted by a bold outlines. For a better contrast of lower error rate in PacBio CCS reads and ONT 2D reads, Supplementary Figure S4 displays each result with its own scale.CC BY · PubMed Central
Figure 4. Isoform identification in human embryonic stem cells. ( a) Length distribution of isoforms identified by full-length by long read only and Hybrid-Seq strategies. ( b) Numbers of identified isoforms with single exon (singleton isoform) and multiple exons (multi-exon isoform). ( c) Overlap between isoforms identified by two Hybrid-Seq strategies. ( d) Accuracy of splice sites detected by four strategies. Perfect means the detected splice sites exactly match known splice sites annotated by Gencode (version 24). Imperfect means the detected splice sites are shorter or longer than known splice sites annotated by Gencode (version 24). ( e) Overlap between novel isoforms identified by two Hybrid-Seq strategies. ( f) Numbers of identified isoforms with different ratios of repetitive elements. ONT: Oxford Nanopore Technologies; PacBio: Pacific Biosciences.
Figure 4. Isoform identification in human embryonic stem cells. ( a) Length distribution of isoforms identified by full-length by long read only and Hybrid-Seq strategies. ( b) Numbers of identified isoforms with single exon (singleton isoform) and multiple exons (multi-exon isoform). ( c) Overlap between isoforms identified by two Hybrid-Seq strategies. ( d) Accuracy of splice sites detected by four strategies. Perfect means the detected splice sites exactly match known splice sites annotated by Gencode (version 24). Imperfect means the detected splice sites are shorter or longer than known splice sites annotated by Gencode (version 24). ( e) Overlap between novel isoforms identified by two Hybrid-Seq strategies. ( f) Numbers of identified isoforms with different ratios of repetitive elements. ONT: Oxford Nanopore Technologies; PacBio: Pacific Biosciences.CC BY · PubMed Central
Figure 5. Estimation errors of isoform abundance estimation in Spike-in RNA Variant data. The X axis shows 7 strategies. The label “correct”, “insufficient” and “over-annotated” in parentheses represent three different SIRV annotation libraries, respectively. The Y axis shows the euclidean distance between real relative expression percentage (1/68≈0.15) and estimated relative expression percentage (for more details see Methods). ONT: Oxford Nanopore Technologies; PacBio: Pacific Biosciences.
Figure 5. Estimation errors of isoform abundance estimation in Spike-in RNA Variant data. The X axis shows 7 strategies. The label “correct”, “insufficient” and “over-annotated” in parentheses represent three different SIRV annotation libraries, respectively. The Y axis shows the euclidean distance between real relative expression percentage (1/68≈0.15) and estimated relative expression percentage (for more details see Methods). ONT: Oxford Nanopore Technologies; PacBio: Pacific Biosciences.CC BY · PubMed Central
Figure 6. Numbers of different alternative splicing (AS) events in human embryonic stem cells transcriptome. A5SS: alternative 5’ splicing site; A3SS: alternative 3’ splicing site; ES: exon skipping; RI: retained intron; MXE: mutually exclusive exons; ONT: Oxford Nanopore Technologies; PacBio: Pacific Biosciences.
Figure 6. Numbers of different alternative splicing (AS) events in human embryonic stem cells transcriptome. A5SS: alternative 5’ splicing site; A3SS: alternative 3’ splicing site; ES: exon skipping; RI: retained intron; MXE: mutually exclusive exons; ONT: Oxford Nanopore Technologies; PacBio: Pacific Biosciences.CC BY · PubMed Central
Figure 7. Functional analysis of identified isoforms. ( a) Feature statistics of isoforms annotated by Gencode (version 24). ( b) Length distribution of open reading frames (ORFs) of novel isoforms identified by two Hybrid-Seq strategies. ( c) Gene enrichment analysis of genes with at least one novel isoform identified by two Hybrid-Seq strategies. ( d) Five novel isoforms (red tracks) of the human embryonic stem cell-relevant gene ESRG were identified by two Hybrid-Seq strategies. The topmost isoform (blue track) is annotated by Gencode (version 24). ESRG: Embryonic Stem Cell Related Gene; ONT: Oxford Nanopore Technologies; PacBio: Pacific Biosciences.
Figure 7. Functional analysis of identified isoforms. ( a) Feature statistics of isoforms annotated by Gencode (version 24). ( b) Length distribution of open reading frames (ORFs) of novel isoforms identified by two Hybrid-Seq strategies. ( c) Gene enrichment analysis of genes with at least one novel isoform identified by two Hybrid-Seq strategies. ( d) Five novel isoforms (red tracks) of the human embryonic stem cell-relevant gene ESRG were identified by two Hybrid-Seq strategies. The topmost isoform (blue track) is annotated by Gencode (version 24). ESRG: Embryonic Stem Cell Related Gene; ONT: Oxford Nanopore Technologies; PacBio: Pacific Biosciences.CC BY · PubMed Central

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Show all 8 references

Research works printed but not linked: 40

  1. A McCarthy, 2010, Third generation DNA sequencing: pacific biosciences' single molecule real time technology., Chem Biol., 17, 675-6, 10.1016/j.chembiol.2010.07.004 · DOI
  2. T Laver, 2015, Assessing the performance of the Oxford Nanopore Technologies MinION., Biomol Detect Quantif., 3, 1-8, 10.1016/j.bdq.2015.02.001 · DOI
  3. A Rhoads, 2015, PacBio Sequencing and Its Applications., Genomics Proteomics Bioinformatics., 13, 278-89, 10.1016/j.gpb.2015.08.002 · DOI · Paper page
  4. H Lu, 2016, Oxford Nanopore MinION Sequencing and Genome Assembly., Genomics Proteomics Bioinformatics., 14, 265-79, 10.1016/j.gpb.2016.05.004 · DOI
  5. J Reuter, 2015, High-throughput sequencing technologies., Mol Cell., 58, 586-97, 10.1016/j.molcel.2015.05.004 · DOI
  6. E van Dijk, 2014, Ten years of next-generation sequencing technology., Trends Genet., 30, 418-26, 10.1016/j.tig.2014.07.001 · DOI
  7. L Liu, 2012, Comparison of next-generation sequencing systems., J Biomed Biotechnol., 2012, 10.1155/2012/251364 · DOI
  8. R McCoy, 2014, Illumina TruSeq synthetic long-reads empower de novo assembly and resolve complex, highly-repetitive transposable elements., PLoS One., 9, e106689, 10.1371/journal.pone.0106689 · DOI
  9. G Zheng, 2016, Haplotyping germline and cancer genomes with high-throughput linked-read sequencing., Nat Biotechnol., 34, 303-11, 10.1038/nbt.3432 · DOI
  10. M Pendleton, 2015, Assembly and diploid architecture of an individual human genome via single-molecule technologies., Nat Methods., 12, 780-6, 10.1038/nmeth.3454 · DOI
  11. J Weirather, 2015, Characterization of fusion genes and the significantly expressed fusion isoforms in breast cancer by hybrid sequencing., Nucleic Acids Res., 43, e116, 10.1093/nar/gkv562 · DOI · Paper page
  12. B Deonovic, 2016, IDP-ASE: haplotyping and quantifying allele-specific expression at the gene and gene isoform level by hybrid sequencing., Nucleic Acids Res., 10.1093/nar/gkw1076 · DOI
  13. C Ip, 2015, MinION Analysis and Reference Consortium: Phase 1 data release and analysis [version 1; referees: 2 approved]., F1000Res., 4, 1075, 10.12688/f1000research.7201.1 · DOI
  14. J Quick, 2014, A reference bacterial genome dataset generated on the MinIONTM portable single-molecule nanopore sequencer., Gigascience., 3, 22, 10.1186/2047-217X-3-22 · DOI
  15. Z Feng, 2013, Detecting DNA modifications from SMRT sequencing data by modeling sequence context dependence of polymerase kinetic., PLoS Comput Biol., 9, e1002935, 10.1371/journal.pcbi.1002935 · DOI
  16. S Koren, 2012, Hybrid error correction and de novo assembly of single-molecule sequencing reads., Nat Biotechnol., 30, 693-700, 10.1038/nbt.2280 · DOI
  17. L Salmela, 2014, LoRDEC: accurate and efficient long read error correction., Bioinformatics., 30, 3506-14, 10.1093/bioinformatics/btu538 · DOI
  18. G Tevz, 2016, Identification of a novel fusion transcript between human relaxin-1 (RLN1) and human relaxin-2 (RLN2) in prostate cancer., Mol Cell Endocrinol., 420, 159-68, 10.1016/j.mce.2015.10.011 · DOI
  19. H Tilgner, 2014, Defining a personal, allele-specific, and single-molecule long-read transcriptome., Proc Natl Acad Sci U S A., 111, 9869-74, 10.1073/pnas.1400447111 · DOI · Paper page
  20. S Abdel-Ghany, 2016, A survey of the sorghum transcriptome using single-molecule long reads., Nat Commun., 7, 11706, 10.1038/ncomms11706 · DOI
  21. A Minoche, 2015, Exploiting single-molecule transcript sequencing for eukaryotic gene prediction., Genome Biol., 16, 184, 10.1186/s13059-015-0729-7 · DOI
  22. S Thomas, 2014, Long-read sequencing of chicken transcripts and identification of new transcript isoforms., PLoS One., 9, e94650, 10.1371/journal.pone.0094650 · DOI
  23. Z Xu, 2015, Full-length transcriptome sequences and splice variants obtained by a combination of sequencing platforms applied to different root tissues of Salvia miltiorrhiza and tanshinone biosynthesis., Plant J., 82, 951-61, 10.1111/tpj.12865 · DOI
  24. L Shi, 2016, Long-read sequencing and de novo assembly of a Chinese genome., Nat Commun., 7, 12065, 10.1038/ncomms12065 · DOI
  25. S Gordon, 2015, Widespread Polycistronic Transcripts in Fungi Revealed by Single-Molecule mRNA Sequencing., PLoS One., 10, e0132628, 10.1371/journal.pone.0132628 · DOI
  26. P Larsen, 2014, The utility of PacBio circular consensus sequencing for characterizing complex gene families in non-model organisms., BMC Genomics., 15, 720, 10.1186/1471-2164-15-720 · DOI
  27. W Zhang, 2014, PacBio sequencing of gene families - a case study with wheat gluten genes., Gene., 533, 541-6, 10.1016/j.gene.2013.10.009 · DOI
  28. M Bolisetty, 2015, Determining exon connectivity in complex mRNAs by nanopore sequencing., Genome Biol., 16, 204, 10.1186/s13059-015-0777-z · DOI
  29. S Oikonomopoulos, 2016, Benchmarking of the Oxford Nanopore MinION sequencing for quantitative and qualitative assessment of cDNA populations., Sci Rep., 6, 31602, 10.1038/srep31602 · DOI
  30. V Sebastiano, 2011, In situ genetic correction of the sickle cell anemia mutation in human induced pluripotent stem cells using engineered zinc finger nucleases., Stem Cells., 29, 1717-26, 10.1002/stem.718 · DOI
  31. S Lamble, 2013, Improved workflows for high throughput library preparation using the transposome-based Nextera system., BMC Biotechnol., 13, 104, 10.1186/1472-6750-13-104 · DOI
  32. S Picelli, 2014, Full-length RNA-seq from single cells using Smart-seq2., Nat Protoc., 9, 171-81, 10.1038/nprot.2014.006 · DOI · Paper page
  33. W Kent, 2002, The human genome browser at UCSC., Genome Res., 12, 996-1006, 10.1101/gr.229102 · DOI · Paper page
  34. T Wu, 2005, GMAP: a genomic mapping and alignment program for mRNA and EST sequences., Bioinformatics., 21, 1859-75, 10.1093/bioinformatics/bti310 · DOI · Paper page
  35. M Pertea, 2015, StringTie enables improved reconstruction of a transcriptome from RNA-seq reads., Nat Biotechnol., 33, 290-5, 10.1038/nbt.3122 · DOI
  36. W Huang da, 2009, Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources., Nat Protoc., 4, 44-57, 10.1038/nprot.2008.211 · DOI · Paper page
  37. E Loomis, 2013, Sequencing the unsequenceable: expanded CGG-repeat alleles of the fragile X gene., Genome Res., 23, 121-8, 10.1101/gr.141705.112 · DOI
  38. R Elkon, 2013, Alternative cleavage and polyadenylation: extent, regulation and function., Nat Rev Genet., 14, 496-506, 10.1038/nrg3482 · DOI
  39. J Wang, 2014, Primate-specific endogenous retrovirus-driven transcription defines naive-like stem cells., Nature., 516, 405-9, 10.1038/nature13804 · DOI
  40. S Goodwin, 2015, Oxford Nanopore sequencing, hybrid error correction, and de novo assembly of a eukaryotic genome., Genome Res., 25, 1750-6, 10.1101/gr.191395.115 · DOI

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Metadata

Authors
Weirather, J. L., de Cesare, M., Wang, Y., Piazza, P., Sebastiano, V., Wang, X.-J., Buck, D., & Au, K. F.
ORCID
Venue
F1000Research
Publisher
F1000 Research Ltd
Published
2017-06-19
Volume
6
Pages
100
ISSN
2046-1402
DOI
10.12688/f1000research.10571.2
PMC
PMC5553090
Cited by
418
References
8
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (unspecified)
Funders
  • National Human Genome Research Institute (R01HG008759)
  • National Natural Science Foundation of China (91540204)
  • University of Iowa
  • Multidisciplinary Lung Research Career Development Program (T32HL007638)

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