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Predicting the molecular complexity of sequencing libraries

Abstract

Predicting the molecular complexity of a genomic sequencing library is a critical but difficult problem in modern sequencing applications. Methods to determine how deeply to sequence to achieve complete coverage or to predict the benefits of additional sequencing are lacking. We introduce an empirical Bayesian method to accurately characterize the molecular complexity of a DNA sample for almost any sequencing application on the basis of limited preliminary sequencing.

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Figure 1: Difficulties in predicting library complexity from initial shallow sequencing.
Figure 2: Library complexity can be estimated in terms of distinct molecules sequenced or distinct loci identified.

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Acknowledgements

We thank S. Tavaré, M. Waterman, P. Calabrese, G. Hannon, and members of the Hannon lab and the Smith lab for their help, advice and input. This work was supported by US National Institutes of Health National Human Genome Research Institute grants (R01 HG005238 and P50 HG002790).

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Authors

Contributions

T.D. and A.D.S. designed the method, implemented the software, performed the analysis and wrote the manuscript.

Corresponding author

Correspondence to Andrew D Smith.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Note, Supplementary Figures 1–2, Supplementary Tables 2–3 (PDF 4640 kb)

Supplementary Table 1

Properties of data sets used in evaluating estimates of library complexity. (XLSX 50 kb)

Supplementary Software

Preseq source code and manual. (ZIP 165 kb)

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Daley, T., Smith, A. Predicting the molecular complexity of sequencing libraries. Nat Methods 10, 325–327 (2013). https://doi.org/10.1038/nmeth.2375

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