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The role of replicates for error mitigation in next-generation sequencing

Nature Reviews Genetics volume 15, pages 56–62 (2014)Cite this article

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Abstract

Advances in next-generation sequencing (NGS) technologies have rapidly improved sequencing fidelity and substantially decreased sequencing error rates. However, given that there are billions of nucleotides in a human genome, even low experimental error rates yield many errors in variant calls. Erroneous variants can mimic true somatic and rare variants, thus requiring costly confirmatory experiments to minimize the number of false positives. Here, we discuss sources of experimental errors in NGS and how replicates can be used to abate such errors.

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Figure 1: Sources of and tools to cope with unexpected or erroneous variants.
Figure 2: Platform-independent method for choosing quality score thresholds.
Figure 3: An example application of plotting replicate scores to assess filter efficiency.

References

  1. O'Rawe, J. et al. Low concordance of multiple variant-calling pipelines: practical implications for exome and genome sequencing. Genome Med. 5, 28 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Kircher, M., Heyn, P. & Kelso, J. Addressing challenges in the production and analysis of Illumina sequencing data. BMC Genomics 12, 382 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Metzker, M. L. Sequencing technologies — the next generation. Nature Rev. Genet. 11, 31–46 (2010).

    CAS  PubMed  Google Scholar 

  4. Sboner, A., Mu, X. J., Greenbaum, D., Auerbach, R. K. & Gerstein, M. B. The real cost of sequencing: higher than you think! Genome Biol. 12, 125 (2011).

    PubMed  PubMed Central  Google Scholar 

  5. Ratan, A. et al. Comparison of sequencing platforms for single nucleotide variant calls in a human sample. PLoS ONE 8, e55089 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Peters, B. A. et al. Accurate whole-genome sequencing and haplotyping from 10 to 20 human cells. Nature 487, 190–195 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Williams, C. et al. A high frequency of sequence alterations is due to formalin fixation of archival specimens. Am. J. Pathol. 155, 1467–1471 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Yost, S. E. et al. Identification of high-confidence somatic mutations in whole genome sequence of formalin-fixed breast cancer specimens. Nucleic Acids Res. 40, e107 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Akbari, M., Hansen, M. D., Halgunset, J., Skorpen, F. & Krokan, H. E. Low copy number DNA template can render polymerase chain reaction error prone in a sequence-dependent manner. J. Mol. Diagn. 7, 36–39 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Lawrence, M. S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Leal, S. M. Detection of genotyping errors and pseudo-SNPs via deviations from Hardy–Weinberg equilibrium. Genet. Epidemiol. 29, 204–214 (2005).

    PubMed  PubMed Central  Google Scholar 

  12. Walsh, P. S., Erlich, H. A. & Higuchi, R. Preferential PCR amplification of alleles: mechanisms and solutions. PCR Methods Appl. 1, 241–250 (1992).

    CAS  PubMed  Google Scholar 

  13. Hutchison, C. A. 3rd, Smith, H. O., Pfannkoch, C. & Venter, J. C. Cell-free cloning using phi29 DNA polymerase. Proc. Natl Acad. Sci. USA 102, 17332–17336 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Hodges, E. et al. Genome-wide in situ exon capture for selective resequencing. Nature Genet. 39, 1522–1527 (2007).

    CAS  PubMed  Google Scholar 

  15. Aird, D. et al. Analyzing and minimizing PCR amplification bias in Illumina sequencing libraries. Genome Biol. 12, R18 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Bystrykh, L. V. Generalized DNA barcode design based on Hamming codes. PLoS ONE 7, e36852 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Koboldt, D. C., Ding, L., Mardis, E. R. & Wilson, R. K. Challenges of sequencing human genomes. Brief Bioinform. 11, 484–498 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Xuan, J., Yu, Y., Qing, T., Guo, L. & Shi, L. Next-generation sequencing in the clinic: promises and challenges. Cancer Lett. 340, 284–295 (2012).

    PubMed  PubMed Central  Google Scholar 

  19. Nakamura, K. et al. Sequence-specific error profile of Illumina sequencers. Nucleic Acids Res. 39, e90 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Fuller, C. W. et al. The challenges of sequencing by synthesis. Nature Biotech. 27, 1013–1023 (2009).

    CAS  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  22. Yang, X., Chockalingam, S. P. & Aluru, S. A survey of error-correction methods for next-generation sequencing. Brief Bioinform. 14, 56–66 (2013).

    CAS  PubMed  Google Scholar 

  23. Lynch, M. Rate, molecular spectrum, and consequences of human mutation. Proc. Natl Acad. Sci. USA 107, 961–968 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Laurie, C. C. et al. Detectable clonal mosaicism from birth to old age and its relationship to cancer. Nature Genet. 44, 642–650 (2012).

    CAS  PubMed  Google Scholar 

  25. Schmitt, M. W. et al. Detection of ultra-rare mutations by next-generation sequencing. Proc. Natl Acad. Sci. USA 109, 14508–14513 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Luo, C., Tsementzi, D., Kyrpides, N., Read, T. & Konstantinidis, K. T. Direct comparisons of Illumina versus Roche 454 sequencing technologies on the same microbial community DNA sample. PLoS ONE 7, e30087 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nature Genet. 43, 491–498 (2011).

    CAS  PubMed  Google Scholar 

  28. Ajay, S. S., Parker, S. C., Abaan, H. O., Fajardo, K. V. & Margulies, E. H. Accurate and comprehensive sequencing of personal genomes. Genome Res. 21, 1498–1505 (2011).

    PubMed  PubMed Central  Google Scholar 

  29. Meynert, A. M., Bicknell, L. S., Hurles, M. E., Jackson, A. P. & Taylor, M. S. Quantifying single nucleotide variant detection sensitivity in exome sequencing. BMC Bioinformatics 14, 195 (2013).

    PubMed  PubMed Central  Google Scholar 

  30. Leek, J. T. et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nature Rev. Genet. 11, 733–739 (2010).

    CAS  PubMed  Google Scholar 

  31. Baranzini, S. E. et al. Genome, epigenome and RNA sequences of monozygotic twins discordant for multiple sclerosis. Nature 464, 1351–1356 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Reumers, J. et al. Optimized filtering reduces the error rate in detecting genomic variants by short-read sequencing. Nature Biotech. 30, 61–68 (2012).

    CAS  Google Scholar 

  33. Lam, H. Y. et al. Performance comparison of whole-genome sequencing platforms. Nature Biotech. 30, 78–82 (2012).

    CAS  Google Scholar 

  34. Jung, H., Bleazard, T., Lee, J. & Hong, D. Systematic investigation of cancer-associated somatic point mutations in SNP databases. Nature Biotech. 31, 787–789 (2013).

    CAS  Google Scholar 

  35. Drmanac, R. et al. Human genome sequencing using unchained base reads on self-assembling DNA nanoarrays. Science 327, 78–81 (2010).

    CAS  PubMed  Google Scholar 

  36. Pelak, K. et al. The characterization of twenty sequenced human genomes. PLoS Genet. 6, e1001111 (2010).

    PubMed  PubMed Central  Google Scholar 

  37. Li, H., Ruan, J. & Durbin, R. Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res. 18, 1851–1858 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Lee, W. et al. The mutation spectrum revealed by paired genome sequences from a lung cancer patient. Nature 465, 473–477 (2010).

    CAS  PubMed  Google Scholar 

  39. Ball, M. P. et al. A public resource facilitating clinical use of genomes. Proc. Natl Acad. Sci. USA 109, 11920–11927 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Laurie, C. C. et al. Quality control and quality assurance in genotypic data for genome-wide association studies. Genet. Epidemiol. 34, 591–602 (2010).

    PubMed  PubMed Central  Google Scholar 

  41. Ye, K., Schulz, M. H., Long, Q., Apweiler, R. & Ning, Z. Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics 25, 2865–2871 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Jurka, J. et al. Repbase Update, a database of eukaryotic repetitive elements. Cytogenet. Genome Res. 110, 462–467 (2005).

    CAS  PubMed  Google Scholar 

  43. Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010).

    PubMed  PubMed Central  Google Scholar 

  44. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    PubMed  PubMed Central  Google Scholar 

  45. Lindgreen, S. AdapterRemoval: easy cleaning of next-generation sequencing reads. BMC Res. Notes 5, 337 (2012).

    PubMed  PubMed Central  Google Scholar 

  46. Degner, J. F. et al. Effect of read-mapping biases on detecting allele-specific expression from RNA-sequencing data. Bioinformatics 25, 3207–3212 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Genovese, G. et al. Using population admixture to help complete maps of the human genome. Nature Genet. 45, 406–414 (2013).

    CAS  PubMed  Google Scholar 

  49. Church, D. M. et al. Modernizing reference genome assemblies. PLoS Biol. 9, e1001091 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Pruitt, K. D., Tatusova, T. & Maglott, D. R. NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 35, D61–D65 (2007).

    CAS  PubMed  Google Scholar 

  51. Rusk, N. One genome, two haplotypes. Nature Methods 8, 107 (2011).

    CAS  PubMed  Google Scholar 

  52. Fan, H. C., Wang, J., Potanina, A. & Quake, S. R. Whole-genome molecular haplotyping of single cells. Nature Biotech. 29, 51–57 (2011).

    CAS  Google Scholar 

  53. Kitzman, J. O. et al. Haplotype-resolved genome sequencing of a Gujarati Indian individual. Nature Biotech. 29, 59–63 (2011).

    CAS  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  55. Bansal, V. & Bafna, V. HapCUT: an efficient and accurate algorithm for the haplotype assembly problem. Bioinformatics 24, i153–i159 (2008).

    PubMed  Google Scholar 

  56. Chen, R. et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 148, 1293–1307 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Roach, J. C. et al. Analysis of genetic inheritance in a family quartet by whole-genome sequencing. Science 328, 636–639 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Lupski, J. R. et al. Whole-genome sequencing in a patient with Charcot-Marie-Tooth neuropathy. N. Engl. J. Med. 362, 1181–1191 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Chapman, S. J. & Hill, A. V. Human genetic susceptibility to infectious disease. Nature Rev. Genet. 13, 175–188 (2012).

    CAS  PubMed  Google Scholar 

  60. Ott, J., Kamatani, Y. & Lathrop, M. Family-based designs for genome-wide association studies. Nature Rev. Genet. 12, 465–474 (2011).

    CAS  PubMed  Google Scholar 

  61. Gibson, G. Rare and common variants: twenty arguments. Nature Rev. Genet. 13, 135–145 (2011).

    Google Scholar 

  62. Wang, K., Li, M. & Hakonarson, H. Analysing biological pathways in genome-wide association studies. Nature Rev. Genet. 11, 843–854 (2010).

    CAS  PubMed  Google Scholar 

  63. Schloissnig, S. et al. Genomic variation landscape of the human gut microbiome. Nature 493, 45–50 (2013).

    PubMed  Google Scholar 

  64. Robins, W. P., Faruque, S. M. & Mekalanos, J. J. Coupling mutagenesis and parallel deep sequencing to probe essential residues in a genome or gene. Proc. Natl Acad. Sci. USA 110, E848–857 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Conrad, T. M., Lewis, N. E. & Palsson, B. O. Microbial laboratory evolution in the era of genome-scale science. Mol. Syst. Biol. 7, 509 (2011).

    PubMed  PubMed Central  Google Scholar 

  66. Shendure, J. et al. Accurate multiplex polony sequencing of an evolved bacterial genome. Science 309, 1728–1732 (2005).

    CAS  PubMed  Google Scholar 

  67. Barrick, J. E. & Lenski, R. E. Genome dynamics during experimental evolution. Nature Rev. Genet. 14, 827–839 (2013).

    CAS  PubMed  Google Scholar 

  68. Xu, X. et al. The genomic sequence of the Chinese hamster ovary (CHO)-K1 cell line. Nature Biotech. 29, 735–741 (2011).

    CAS  Google Scholar 

  69. Lewis, N. E. et al. Genomic landscapes of Chinese hamster ovary cell lines as revealed by the Cricetulus griseus draft genome. Nature Biotech. 31, 759–765 (2013).

    CAS  Google Scholar 

  70. Brinkrolf, K. et al. Chinese hamster genome sequenced from sorted chromosomes. Nature Biotech. 31, 694–695 (2013).

    CAS  Google Scholar 

  71. Becker, J. et al. Unraveling the Chinese hamster ovary cell line transcriptome by next-generation sequencing. J. Biotechnol. 156, 227–235 (2011).

    CAS  PubMed  Google Scholar 

  72. Kildegaard, H. F., Baycin-Hizal, D., Lewis, N. E. & Betenbaugh, M. J. The emerging CHO systems biology era: harnessing the 'omics revolution for biotechnology. Curr. Opin. Biotechnol. 24, 1102–1107 (2013).

    PubMed  Google Scholar 

  73. Furey, T. S. ChIP-seq and beyond: new and improved methodologies to detect and characterize protein–DNA interactions. Nature Rev. Genet. 13, 840–852 (2012).

    CAS  PubMed  Google Scholar 

  74. Meaburn, E. & Schulz, R. Next generation sequencing in epigenetics: insights and challenges. Semin. Cell Dev. Biol. 23, 192–199 (2012).

    CAS  PubMed  Google Scholar 

  75. Ley, T. J. et al. DNA sequencing of a cytogenetically normal acute myeloid leukaemia genome. Nature 456, 66–72 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Rios, J., Stein, E., Shendure, J., Hobbs, H. H. & Cohen, J. C. Identification by whole-genome resequencing of gene defect responsible for severe hypercholesterolemia. Hum. Mol. Genet. 19, 4313–4318 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Schneeberger, K. et al. SHOREmap: simultaneous mapping and mutation identification by deep sequencing. Nature Methods 6, 550–551 (2009).

    CAS  PubMed  Google Scholar 

  78. Cooper, G. M. & Shendure, J. Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data. Nature Rev. Genet. 12, 628–640 (2011).

    CAS  PubMed  Google Scholar 

  79. Gonzalez-Perez, A. et al. Computational approaches to identify functional genetic variants in cancer genomes. Nature Methods 10, 723–729 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. Reva, B., Antipin, Y. & Sander, C. Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res. 39, e118 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Lewis, N. E. & Abdel-Haleem, A. M. The evolution of genome-scale models of cancer metabolism. Front. Physiol. 4, 237 (2013).

    PubMed  PubMed Central  Google Scholar 

  82. Ala-Korpela, M., Kangas, A. J. & Inouye, M. Genome-wide association studies and systems biology: together at last. Trends Genet. 27, 493–498 (2011).

    CAS  PubMed  Google Scholar 

  83. Moreau, Y. & Tranchevent, L. C. Computational tools for prioritizing candidate genes: boosting disease gene discovery. Nature Rev. Genet. 13, 523–536 (2012).

    CAS  PubMed  Google Scholar 

  84. Zamft, B. M. et al. Measuring cation dependent DNA polymerase fidelity landscapes by deep sequencing. PLoS ONE 7, e43876 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Drukier, A. et al. New dark matter detectors using DNA for nanometer tracking. arXiv 1206.6809 (2012).

  86. Hubisz, M. J., Lin, M. F., Kellis, M. & Siepel, A. Error and error mitigation in low-coverage genome assemblies. PLoS ONE 6, e17034 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Macabeo-Ong, M. et al. Effect of duration of fixation on quantitative reverse transcription polymerase chain reaction analyses. Mod. Pathol. 15, 979–987 (2002).

    PubMed  Google Scholar 

  88. Kerick, M. et al. Targeted high throughput sequencing in clinical cancer settings: formaldehyde fixed-paraffin embedded (FFPE) tumor tissues, input amount and tumor heterogeneity. BMC Med. Genom. 4, 68 (2011).

    CAS  Google Scholar 

  89. Lin, M. T. et al. Quantifying the relative amount of mouse and human DNA in cancer xenografts using species-specific variation in gene length. Biotechniques 48, 211–218 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  90. Innis, M. A., Gelfand, D. H., Sninsky, J. J. & White, T. J. PCR protocols: a guide to methods and applications (Academic press, 1990).

    Google Scholar 

  91. Wojdacz, T. K., Hansen, L. L. & Dobrovic, A. A new approach to primer design for the control of PCR bias in methylation studies. BMC Res. Notes 1, 54 (2008).

    PubMed  PubMed Central  Google Scholar 

  92. Kanagawa, T. Bias and artifacts in multitemplate polymerase chain reactions (PCR). J. Biosci. Bioeng. 96, 317–323 (2003).

    CAS  PubMed  Google Scholar 

  93. Nagalakshmi, U. et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 320, 1344–1349 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. Pont-Kingdon, G. et al. Design and analytical validation of clinical DNA sequencing assays. Arch. Pathol. Lab Med. 136, 41–46 (2012).

    CAS  PubMed  Google Scholar 

  95. Gogol-Doring, A. & Chen, W. An overview of the analysis of next generation sequencing data. Methods Mol. Biol. 802, 249–257 (2012).

    CAS  PubMed  Google Scholar 

  96. Whiteford, N. et al. Swift: primary data analysis for the Illumina Solexa sequencing platform. Bioinformatics 25, 2194–2199 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. Loman, N. J. et al. Performance comparison of benchtop high-throughput sequencing platforms. Nature Biotech. 30, 434–439 (2012).

    CAS  Google Scholar 

  98. Huse, S. M., Huber, J. A., Morrison, H. G., Sogin, M. L. & Welch, D. M. Accuracy and quality of massively parallel DNA pyrosequencing. Genome Biol. 8, R143 (2007).

    PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors thank T. Gianoulis for her feedback and inspiration, and J. Dupuis, Professor of Biostatistics at Boston University, Massachusetts, USA, for her encouragement and feedback during the nascent stages of replicate analysis. They also thank W. Jones, Global Head of Genomic Bioinformatics, Quintiles, and E. Aronesty, author of the ea-utils FASTQ processing package, for critical review of the manuscript. Some of this work was supported by the US National Institutes of Health grant P50HG005550.

Author information

Author notes

  1. George M. Church

    Present address: Department of Genetics, Harvard Medical School, and the Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, Massachusetts 02115, USA.,

  2. Kimberly Robasky

    Present address: Program in Bioinformatics, Boston University, Massachusetts 02115, USA.Department of Genetics, Harvard Medical School, and the Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, Massachusetts 02115, USA. Present address: Expression Analysis, a Quintiles Company, Durham, North Carolina 27713, USA.,

  3. Nathan E. Lewis

    Present address: Department of Genetics, Harvard Medical School, and the Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, Massachusetts 02115, USA. Department of Biology, Brigham Young University, Provo, Utah 84602, USA. Present address: Division of Pediatric Pharmacology and Drug Discovery, University of California, San Diego School of Medicine, La Jolla, California 92093, USA.,

  4. Kimberly Robasky and Nathan E. Lewis: These authors contributed equally to this work.

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    1. Kimberly Robasky
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    Correspondence to Nathan E. Lewis.

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    Competing interests

    K.R. is currently under employment by Expression Analysis, a Quintiles company. G.M.C. has advisory roles in and research sponsorships from several companies that are involved in genome sequencing technology and personal genomics. For a list of G.M.C's tech transfer, advisory roles and funding sources, see http://arep.med.harvard.edu/gmc/tech.html.

    Supplementary information

    Supplementary information S1 (box)

    Datasets (PDF 268 kb)

    Supplementary information S2 (box)

    Method for Assessing Specificity/Sensitivity with Replicates (PDF 237 kb)

    PowerPoint slides

    Glossary

    Barcodes

    Known DNA sequences that are appended to the ends of DNA fragments before sequencing for the purpose of pooling samples together to reduce cost.

    Base call

    Identification of the nitrogenous base (A, G, C or T) that is added to the short read during sequencing.

    Batch effect

    The statistical bias of indeterminate cause observed in samples that are processed together with the same sample preparation, the same library preparation and the same sequencing experiment.

    Homopolymer

    A sequence of multiple consecutive identical nucleotides.

    Insertions and deletions

    (Indels). Variants that are created by either the insertion or the deletion of nucleotides with respect to a matching reference.

    Misalignment

    The alignment of a sequencing read to an incorrect location on a reference genome. This can occur when reads align equally well to multiple genomic locations owing to indels, repeats and low-complexity regions of the genome.

    Multiple displacement amplification

    (MDA). A technique that is used for amplifying DNA sequences by synthesizing DNA from random hexamer primers.

    Read clipping

    Removal of adaptor and barcode sequences or of low-quality bases near read ends following sequencing.

    Sequencing errors

    Errors that are seen in the base call of short reads from next-generation sequencing technology.

    Sequencing read depth

    The number of reads that contributes to the variant call at a single location; also known as read depth, fold coverage and depth of coverage. It can also refer to the average read depth across the entire targeted sequence area.

    Short reads

    Short sequences of nucleotide bases and their respective quality scores that are obtained through next-generation sequencing from longer target sequences.

    Somatic mosaicism

    Genetic diversity among cells of a single organism.

    Substitution errors

    Errors that occur when one base is substituted for another during sequencing.

    Variant call errors

    An accumulation of misaligned reads or of reads with base call errors over a particular locus, which results in that locus being called a variant when it truly matches the reference, and vice versa.

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    Robasky, K., Lewis, N. & Church, G. The role of replicates for error mitigation in next-generation sequencing. Nat Rev Genet 15, 56–62 (2014). https://doi.org/10.1038/nrg3655

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