A framework and an algorithm to detect low-abundance DNA by a handy sequencer and a palm-sized computer
26th July 2018 - Bioinformatics
Detection of DNA at low abundance with respect to the entire sample is an important problem in areas such as epidemiology and ﬁeld research, as these samples are highly contaminated with non-target DNA. To solve this problem, many methods have been developed to date, but all require additional time-consuming and costly procedures. Meanwhile, the MinION sequencer developed by Oxford Nanopore Technology (ONT) is considered a powerful tool for tackling this problem, as it allows selective sequencing of target DNA. The main technology employed involves rejection of an undesirable read from a speciﬁc pore by inverting the voltage of that pore, which is referred to as "Read Until". Despite its usefulness, several issues remain to be solved in real situations. Firstly, limited computational resources are available in ﬁeld research and epidemiological applications. In addition, a high-speed online classiﬁcation algorithm is required to make a prompt decision. Lastly, the lack of a theoretical approach for modelling of selective sequencing makes it difﬁcult to analyze and justify a given algorithm.
In this paper, we introduced a statistical model of selective sequencing, proposed an efﬁcient constant-time classiﬁer for any background DNA proﬁle, and validated its optimal precision. To conﬁrm the feasibility of the proposed method in practice, for a pre-recorded mock sample, we demonstrate that the method can selectively sequence a 100-kbp region, consisting of 0.1% of the entire read pool, and achieve approximately 500-fold ampliﬁcation. Furthermore, the algorithm is shown to process 26 queries per second with a $500 palm-sized next unit of computing box using an Intel®Core™i7 CPU without extended computer resources such as a GPU or high-performance computing. Next, we prepared a mixed DNA pool composed of Saccharomyces cerevisiae and lambda phage, in which any 200-kbp region of S. cerevisiae consists of 0.1% of the whole sample. From this sample, a 30-to 230-kbp region of S. cerevisiae chromosome 1 was ampliﬁed approximately 30-fold. In addition, this method allowed on-the-ﬂy changing of the ampliﬁed region according to the uncovered characteristics of a given DNA sample.
Availability and Implementation
The source code is available at: https://bitbucket.org/ban-m/dyss.
Supplementary data are available at Bioinformatics online.