CoChat
Sign Up
Cited by 2,135

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

PacBio Sequencing and its Applications

Rhoads, Anthony, Au, Kin Fai·2015·Genomics, Proteomics & Bioinformatics
DOI 10.1016/j.gpb.2015.08.002PMC PMC4678779
Open this paper in CoChat — free account

Abstract

Single-molecule, real-time sequencing developed by Pacific BioSciences offers longer read lengths than the second-generation sequencing (SGS) technologies, making it well-suited for unsolved problems in genome, transcriptome, and epigenetics research. The highly-contiguous de novo assemblies using PacBio sequencing can close gaps in current reference assemblies and characterize structural variation (SV) in personal genomes. With longer reads, we can sequence through extended repetitive regions and detect mutations, many of which are associated with diseases. Moreover, PacBio transcriptome sequencing is advantageous for the identification of gene isoforms and facilitates reliable discoveries of novel genes and novel isoforms of annotated genes, due to its ability to sequence full-length transcripts or fragments with significant lengths. Additionally, PacBio’s sequencing technique provides information that is useful for the direct detection of base modifications, such as methylation. In addition to using PacBio sequencing alone, many hybrid sequencing strategies have been developed to make use of more accurate short reads in conjunction with PacBio long reads. In general, hybrid sequencing strategies are more affordable and scalable especially for small-size laboratories than using PacBio Sequencing alone. The advent of PacBio sequencing has made available much information that could not be obtained via SGS alone.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.gpb.2015.08.002

Cites

10 of 10 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 SMRTbell template Hairpin adaptors (green) are ligated to the end of a double-stranded DNA molecule (yellow and purple), forming a closed circle. The polymerase (gray) is anchored to the bottom of a ZMW and incorporates bases into the read strand (orange). The image is adapted from [2] with permission from the Oxford University Press.
Figure 1 SMRTbell template Hairpin adaptors (green) are ligated to the end of a double-stranded DNA molecule (yellow and purple), forming a closed circle. The polymerase (gray) is anchored to the bottom of a ZMW and incorporates bases into the read strand (orange). The image is adapted from [2] with permission from the Oxford University Press.CC BY · PubMed Central
Figure 2 A single SMRT cell Each SMRT cell contains 150,000 ZMWs. Approximately 35,000–75,000 of these wells produce a read in a run lasting 0.5–4 h, resulting in 0.5–1 Gb of sequence. The image is adapted with permission from Pacific Biosciences [3]. ZMW, zero-mode waveguide.
Figure 2 A single SMRT cell Each SMRT cell contains 150,000 ZMWs. Approximately 35,000–75,000 of these wells produce a read in a run lasting 0.5–4 h, resulting in 0.5–1 Gb of sequence. The image is adapted with permission from Pacific Biosciences [3]. ZMW, zero-mode waveguide.CC BY · PubMed Central
Figure 3 Sequencing via light pulses A. A SMRTbell (gray) diffuses into a ZMW, and the adaptor binds to a polymerase immobilized at the bottom. B. Each of the four nucleotides is labeled with a different fluorescent dye (indicated in red, yellow, green, and blue, respectively for G, C, T, and A) so that they have distinct emission spectrums. As a nucleotide is held in the detection volume by the polymerase, a light pulse is produced that identifies the base. (1) A fluorescently-labeled nucleotide associates with the template in the active site of the polymerase. (2) The fluorescence output of the color corresponding to the incorporated base (yellow for base C as an example here) is elevated. (3) The dye-linker-pyrophosphate product is cleaved from the nucleotide and diffuses out of the ZMW, ending the fluorescence pulse. (4) The polymerase translocates to the next position. (5) The next nucleotide associates with the template in the active site of the polymerase, initiating the next fluorescence pulse, which corresponds to base A here. The figure is adapted from [4] with permission from The American Association for the Advancement of Science.
Figure 3 Sequencing via light pulses A. A SMRTbell (gray) diffuses into a ZMW, and the adaptor binds to a polymerase immobilized at the bottom. B. Each of the four nucleotides is labeled with a different fluorescent dye (indicated in red, yellow, green, and blue, respectively for G, C, T, and A) so that they have distinct emission spectrums. As a nucleotide is held in the detection volume by the polymerase, a light pulse is produced that identifies the base. (1) A fluorescently-labeled nucleotide associates with the template in the active site of the polymerase. (2) The fluorescence output of the color corresponding to the incorporated base (yellow for base C as an example here) is elevated. (3) The dye-linker-pyrophosphate product is cleaved from the nucleotide and diffuses out of the ZMW, ending the fluorescence pulse. (4) The polymerase translocates to the next position. (5) The next nucleotide associates with the template in the active site of the polymerase, initiating the next fluorescence pulse, which corresponds to base A here. The figure is adapted from [4] with permission from The American Association for the Advancement of Science.CC BY · PubMed Central
Figure 4 PacBio RS II read length distribution using P6-C4 chemistry Data are based on a 20 kb size-selected E. coli library using a 4-h movie. Each SMRT cell produces 0.5–1 billion bases. The P6-C4 chemistry is currently the most advanced sequencing chemistry offered by PacBio. The figure is adapted with permission from Pacific Biosciences [8].
Figure 4 PacBio RS II read length distribution using P6-C4 chemistry Data are based on a 20 kb size-selected E. coli library using a 4-h movie. Each SMRT cell produces 0.5–1 billion bases. The P6-C4 chemistry is currently the most advanced sequencing chemistry offered by PacBio. The figure is adapted with permission from Pacific Biosciences [8].CC BY · PubMed Central
Figure 5 Detection of methylated bases using PacBio sequencing PacBio sequencing can detect modified bases, including m6A (also known as 6mA), by analyzing variation in the time between base incorporations in the read strand. The figure is adapted with permission from Pacific Biosciences [72]. a.u. stands for arbitrary unit.
Figure 5 Detection of methylated bases using PacBio sequencing PacBio sequencing can detect modified bases, including m6A (also known as 6mA), by analyzing variation in the time between base incorporations in the read strand. The figure is adapted with permission from Pacific Biosciences [72]. a.u. stands for arbitrary unit.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 10 references

Research works printed but not linked: 52

  1. Schadt, 2010, A window into third-generation sequencing, Hum Mol Genet, 19, R227, 10.1093/hmg/ddq416 · DOI
  2. Travers, 2010, A flexible and efficient template format for circular consensus sequencing and SNP detection, Nucleic Acids Res, 38, e159, 10.1093/nar/gkq543 · DOI
  3. Eid, 2009, Real-time DNA sequencing from single polymerase molecules, Science, 323, 133, 10.1126/science.1162986 · DOI · Paper page
  4. Koren, 2015, One chromosome, one contig: complete microbial genomes from long-read sequencing and assembly, Curr Opin Microbiol, 23, 110, 10.1016/j.mib.2014.11.014 · DOI
  5. Myers, 2015, PacBio AGBT 2015 live workshop
  6. Detter, 2014, Biological identification: DNA amplification and sequencing, optical sensing, Lab-On-Chip and portable systems, 1
  7. Liu, 2012, Comparison of next-generation sequencing systems, J Biomed Biotechnol, 2012, 251364, 10.1155/2012/251364 · DOI
  8. Niedringhaus, 2011, Landscape of next-generation sequencing technologies, Anal Chem, 83, 4327, 10.1021/ac2010857 · DOI
  9. Shendure, 2008, Next-generation DNA sequencing, Nat Biotechnol, 26, 1135, 10.1038/nbt1486 · DOI · Paper page
  10. Glenn, 2011, Field guide to next-generation DNA sequencers, Mol Ecol Resour, 11, 759, 10.1111/j.1755-0998.2011.03024.x · DOI · Paper page
  11. Laver, 2015, Assessing the performance of the Oxford Nanopore Technologies MinION, Biomol Detect Quantif, 3, 1, 10.1016/j.bdq.2015.02.001 · DOI
  12. Mikheyev, 2014, A first look at the Oxford Nanopore MinION sequencer, Mol Ecol Resour, 14, 1097, 10.1111/1755-0998.12324 · DOI
  13. Chin, 2013, Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data, Nat Methods, 10, 563, 10.1038/nmeth.2474 · DOI · Paper page
  14. Liao, 2015, Completing bacterial genome assemblies: strategy and performance comparisons, Sci Rep, 5
  15. Bashir, 2012, A hybrid approach for the automated finishing of bacterial genomes, Nat Biotechnol, 30, 701, 10.1038/nbt.2288 · DOI
  16. Berlin, 2014, Assembling large genomes with single-molecule sequencing and locality sensitive hashing, Nat Biotechnol, 33, 623, 10.1038/nbt.3238 · DOI
  17. Chaisson, 2015, Resolving the complexity of the human genome using single-molecule sequencing, Nature, 517, 608, 10.1038/nature13907 · DOI
  18. Ferrarini, 2013, An evaluation of the PacBio RS platform for sequencing and de novo assembly of a chloroplast genome, BMC Genomics, 14, 670, 10.1186/1471-2164-14-670 · DOI
  19. Koren, 2012, Hybrid error correction and de novo assembly of single-molecule sequencing reads, Nat Biotechnol, 30, 693, 10.1038/nbt.2280 · DOI
  20. Satou, 2014, Complete genome sequences of eight Helicobacter pylori strains with different virulence factor genotypes and methylation profiles, isolated from patients with diverse gastrointestinal diseases on Okinawa island, Japan, determined using PacBio single-molecule real-time technology, Genome Announc, 2, 10.1128/genomeA.00286-14 · DOI
  21. Broder, 1998, In: Compression and complexity of sequences 1997—Proceedings. Salerno, 11 Jun 1997–13 Jun 1997., 21
  22. Chaisson, 2012, Mapping single molecule sequencing reads using basic local alignment with successive refinement (BLASR): application and theory, BMC Bioinformatics, 13, 238, 10.1186/1471-2105-13-238 · DOI
  23. Blasco, 2005, Telomeres and human disease: ageing, cancer and beyond, Nat Rev Genet, 6, 611, 10.1038/nrg1656 · DOI · Paper page
  24. Coupland, 2012, Direct sequencing of small genomes on the Pacific Biosciences RS without library preparation, Biotechniques, 53, 365, 10.2144/000113962 · DOI
  25. Zhang, 2012, Improving genome assemblies by sequencing PCR products with PacBio, Biotechniques, 53, 61, 10.2144/0000113891 · DOI
  26. Loomis, 2013, Sequencing the unsequenceable: expanded CGG-repeat alleles of the fragile X gene, Genome Res, 23, 121, 10.1101/gr.141705.112 · DOI
  27. Jin, 2000, Understanding the molecular basis of fragile X syndrome, Hum Mol Genet, 9, 901, 10.1093/hmg/9.6.901 · DOI
  28. Guo, 2014, Genome reference and sequence variation in the large repetitive central exon of human MUC5AC, Am J Respir Cell Mol Biol, 50, 223, 10.1165/rcmb.2013-0235OC · DOI
  29. Kreda, 2012, CFTR, mucins, and mucus obstruction in cystic fibrosis, Cold Spring Harb Perspect Med, 2, a009589, 10.1101/cshperspect.a009589 · DOI
  30. Doi, 2014, Rapid detection of expanded short tandem repeats in personal genomics using hybrid sequencing, Bioinformatics, 30, 815, 10.1093/bioinformatics/btt647 · DOI
  31. Patel, 2014, Amplification and thrifty single-molecule sequencing of recurrent somatic structural variations, Genome Res, 24, 318, 10.1101/gr.161497.113 · DOI
  32. Liu, 2015, Structural variation discovery in the cancer genome using next generation sequencing: computational solutions and perspectives, Oncotarget, 6, 5477, 10.18632/oncotarget.3491 · DOI
  33. English, 2015, Assessing structural variation in a personal genome-towards a human reference diploid genome, BMC Genomics, 16, 286, 10.1186/s12864-015-1479-3 · DOI
  34. Ritz, 2014, Characterization of structural variants with single molecule and hybrid sequencing approaches, Bioinformatics, 30, 3458, 10.1093/bioinformatics/btu714 · DOI
  35. Hampton, 2009, A sequence-level map of chromosomal breakpoints in the MCF-7 breast cancer cell line yields insights into the evolution of a cancer genome, Genome Res, 19, 167, 10.1101/gr.080259.108 · DOI
  36. Hampton, 2011, Long-range massively parallel mate pair sequencing detects distinct mutations and similar patterns of structural mutability in two breast cancer cell lines, Cancer Genet, 204, 447, 10.1016/j.cancergen.2011.07.009 · DOI
  37. Steijger, 2013, Assessment of transcript reconstruction methods for RNA-seq, Nat Methods, 10, 1177, 10.1038/nmeth.2714 · DOI · Paper page
  38. Chen, 2014, Transcriptional diversity during lineage commitment of human blood progenitors, Science, 345, 1251033, 10.1126/science.1251033 · DOI
  39. Weirather, 2015, Characterization of fusion genes and the significantly expressed fusion isoforms in breast cancer by hybrid sequencing, Nucleic Acids Res, 10.1093/nar/gkv562 · DOI · Paper page
  40. Fernandez-Cuesta, 2015, Identification of novel fusion genes in lung cancer using breakpoint assembly of transcriptome sequencing data, Genome Biol, 16, 7, 10.1186/s13059-014-0558-0 · DOI
  41. Kim, 2011, TopHat-Fusion: an algorithm for discovery of novel fusion transcripts, Genome Biol, 12, R72, 10.1186/gb-2011-12-8-r72 · DOI
  42. Tilgner, 2014, Defining a personal, allele-specific, and single-molecule long-read transcriptome, Proc Natl Acad Sci U S A, 111, 9869, 10.1073/pnas.1400447111 · DOI · Paper page
  43. Tilgner, 2013, Accurate identification and analysis of human mRNA isoforms using deep long read sequencing, G3 (Bethesda), 3, 387, 10.1534/g3.112.004812 · DOI
  44. Bierne, 2012, Epigenetics and bacterial infections, Cold Spring Harb Perspect Med, 2, a010272, 10.1101/cshperspect.a010272 · DOI
  45. Murray, 2012, The methylomes of six bacteria, Nucleic Acids Res, 40, 11450, 10.1093/nar/gks891 · DOI
  46. Ratel, 2006, N6-methyladenine: the other methylated base of DNA, BioEssays, 28, 309, 10.1002/bies.20342 · DOI
  47. Beckmann, 2014, Detecting epigenetic motifs in low coverage and metagenomics settings, BMC Bioinformatics, 15, S16, 10.1186/1471-2105-15-S9-S16 · DOI
  48. Vilfan, 2013, Analysis of RNA base modification and structural rearrangement by single-molecule real-time detection of reverse transcription, J Nanobiotechnol, 11, 8, 10.1186/1477-3155-11-8 · DOI
  49. 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
  50. Feng, 2014, QDNAmod: a statistical model-based tool to reveal intercellular heterogeneity of DNA modification from SMRT sequencing data, Nucleic Acids Res, 42, 13488, 10.1093/nar/gku1097 · DOI
  51. Hayden, 2014, Data from pocket-sized genome sequencer unveiled, Nature, 10.1038/nature.2014.14724 · DOI
  52. Pacific Biosciences, 2015, Sequel system: high-throughput

Cited by

Sorted by citations

See all 2,135

Metadata

Authors
Rhoads, A., & Au, K. F.
Affiliations
Department of Biostatistics, University of Iowa , Iowa City, IA, 52242 , USA
ORCID
Venue
Genomics, Proteomics & Bioinformatics
Publisher
Oxford University Press (OUP)
Published
2015-10-01
Volume
13
Issue
5
Pages
278-289
ISSN
  • 1672-0229
  • 2210-3244
DOI
10.1016/j.gpb.2015.08.002
PMC
PMC4678779
Cited by
2,135
References
10
Language
en
Licence
Funders
University of Iowa

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: PacBio Sequencing and its Applications. https://app.cochat.ai/papers/10.1016/j.gpb.2015.08.002
The paper
Rhoads, A., & Au, K. F. (2015). PacBio Sequencing and its Applications. Genomics, Proteomics & Bioinformatics. https://doi.org/10.1016/j.gpb.2015.08.002