A new generation of technologies is poised to reduce DNA sequencing costs by several orders of magnitude. But our ability to fully leverage the power of these technologies is crippled by the absence of suitable 'front-end' methods for isolating complex subsets of a mammalian genome at a scale that matches the throughput at which these platforms will routinely operate. We show that targeting oligonucleotides released from programmable microarrays can be used to capture and amplify approximately 10,000 human exons in a single multiplex reaction. Additionally, we show integration of this protocol with ultra-high-throughput sequencing for targeted variation discovery. Although the multiplex capture reaction is highly specific, we found that nonuniform capture is a key issue that will need to be resolved by additional optimization. We anticipate that highly multiplexed methods for targeted amplification will enable the comprehensive resequencing of human exons at a fraction of the cost of whole-genome resequencing.

We have achieved the ability to synthesize thousands of unique, long oligonucleotides (150mers) in fmol amounts using parallel synthesis of DNA on microarrays. The sequence accuracy of the oligonucleotides in such large-scale syntheses has been limited by the yields and side reactions of the DNA synthesis process used. While there has been significant demand for libraries of long oligos (150mer and more), the yields in conventional DNA synthesis and the associated side reactions have previously limited the availability of oligonucleotide pools to lengths <100 nt. Using novel array based depurination assays, we show that the depurination side reaction is the limiting factor for the synthesis of libraries of long oligonucleotides on Agilent Technologies’ SurePrint® DNA microarray platform. We also demonstrate how depurination can be controlled and reduced by a novel detritylation process to enable the synthesis of high quality, long (150mer) oligonucleotide libraries and we report the characterization of synthesis efficiency for such libraries. Oligonucleotide libraries prepared with this method have changed the economics and availability of several existing applications (e.g. targeted resequencing, preparation of shRNA libraries, site-directed mutagenesis), and have the potential to enable even more novel applications (e.g. high-complexity synthetic biology).

DNA has recently emerged as an attractive medium for archival data storage. Recent work has demonstrated proof-of-principle prototype systems; however, very uneven (biased) sequencing coverage has been reported, which indicates inefficiencies in the storage process. Deviations from the average coverage in the sequence copy distribution can either cause wasteful provisioning in sequencing or excessive number of missing sequences. Here, we use millions of unique sequences from a DNA-based digital data archival system to study the oligonucleotide copy unevenness problem and show that the two paramount sources of bias are the synthesis and amplification (PCR) processes. Based on these findings, we develop a statistical model for each molecular process as well as the overall process. We further use our model to explore the trade-offs between synthesis bias, storage physical density, logical redundancy, and sequencing redundancy, providing insights for engineering efficient, robust DNA data storage systems.

Main textEngineering biology with recombinant DNA, broadly called synthetic biology, is a growing economic force. As of 2018, 98 companies raised 3.8 billion dollars ( 1 ) to catalyze the development of customized biological solutions for therapeutics, agriculture, energy and materials.Synthetic biology has progressed tremendously in the last decade, owing to continued industrialization of DNA synthesis, discovery and development of molecular tools and organisms, as well as increasingly more sophisticated modeling and analytic software tools. However, we have yet to understand the full potential of engineering biology due to our inability to write and test whole genomes, which we call synthetic genomics.