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A systematic comparison of error correction enzymes by next-generation sequencing

View ORCID ProfileNathan B. Lubock, Di Zhang, George M. Church, View ORCID ProfileSriram Kosuri
doi: https://doi.org/10.1101/100685
Nathan B. Lubock
1Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California, USA
2UCLA-DOE Institute for Genomics and Proteomics, Los Angeles, California, USA
3Molecular Biology Institute, University of California, Los Angeles, California, USA
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  • ORCID record for Nathan B. Lubock
Di Zhang
4Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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George M. Church
5Wyss Institute for Biologically Inspired Engineering, Boston, Massachusetts, USA
6Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA
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Sriram Kosuri
1Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California, USA
2UCLA-DOE Institute for Genomics and Proteomics, Los Angeles, California, USA
3Molecular Biology Institute, University of California, Los Angeles, California, USA
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  • For correspondence: sri@ucla.edu
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Abstract

Gene synthesis, the process of assembling gene-length fragments from shorter groups of oligonucleotides (oligos), is becoming an increasingly important tool in molecular and synthetic biology. The length, quality, and cost of gene synthesis is limited by errors produced during oligo synthesis and subsequent assembly. Enzymatic error correction methods are cost-effective means to ameliorate errors in gene synthesis. Previous analyses of these methods relied on cloning and Sanger sequencing to evaluate their efficiencies, limiting quantitative assessment and throughput. Here we develop a method to quantify errors in synthetic DNA by next-generation sequencing. We analyzed errors in a model gene assembly and systematically compared six different error correction enzymes across 11 conditions. We find that ErrASE and T7 Endonuclease I are the most effective at decreasing average error rates (up to 5.8-fold relative to the input), whereas MutS is the best for increasing the number of perfect assemblies (up to 25.2-fold). We are able to quantify differential specificities such as ErrASE preferentially corrects C/G → G/C transversions whereas T7 Endonuclease I preferentially corrects A/T → T/A transversions. More generally, this experimental and computational pipeline is a fast, scalable, and extensible way to analyze errors in gene assemblies, to profile error correction methods, and to benchmark DNA synthesis methods.

Footnotes

  • ↵† The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted January 15, 2017.
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A systematic comparison of error correction enzymes by next-generation sequencing
Nathan B. Lubock, Di Zhang, George M. Church, Sriram Kosuri
bioRxiv 100685; doi: https://doi.org/10.1101/100685
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A systematic comparison of error correction enzymes by next-generation sequencing
Nathan B. Lubock, Di Zhang, George M. Church, Sriram Kosuri
bioRxiv 100685; doi: https://doi.org/10.1101/100685

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