Molecular sampling at logarithmic rates for next-generation sequencing

Caroline Horn and Julia Salzman

PLOS Computational Biology, 2019, vol. 15, issue 12, 1-12

Abstract: Next-generation sequencing is a cutting edge technology, but to quantify a dynamic range of abundances for different RNA or DNA species requires increasing sampling depth to levels that can be prohibitively expensive due to physical limits on molecular throughput of sequencers. To overcome this problem, we introduce a new general sampling theory which uses biophysical principles to functionally encode the abundance of a species before sampling, SeQUential depletIon and enriCHment (SQUICH). In theory and simulation, SQUICH enables sampling at a logarithmic rate to achieve the same precision as attained with conventional sequencing. A simple proof of principle experimental implementation of SQUICH in a controlled complex system of ~262,000 oligonucleotides already reduces sequencing depth by a factor of 10. SQUICH lays the groundwork for a general solution to a fundamental problem in molecular sampling and enables a new generation of efficient, precise molecular measurement at logarithmic or better sampling depth.Author summary: Next-generation sequencing enables measurement of chemical and biological signals at high throughput and falling cost. Conventional sequencing uses a process called simple random sampling which requires increasing the number of samples to be able to detect a signal precisely. We have developed a new way to sample, by first performing computations with DNA and then only sampling the output of the computations, requiring a much smaller number of samples to estimate at the same precision as without this method. In common applications such as RNA sequencing or biomarker detection, the method requires 100–1000 fold less sampling, and so reduces cost by 100–1000 fold. This means that the scale and precision of molecular measurement can be dramatically increased, enabling new efficiency in detecting biological molecules.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1007537

DOI: 10.1371/journal.pcbi.1007537

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