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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.

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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)

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    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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