All-in-one sequencing: an improved library preparation method for cost-effective and high-throughput next-generation sequencing

<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>Next generation sequencing (NGS) has been widely used in biological research, due to its rapid decrease in cost and increasing ability to generate data. However, while the sequence generation step has seen many improvements over time, the library preparation step has not, resulting in low-efficiency library preparation methods, especially for the most time-consuming and labor-intensive steps: size-selection and quantification. Consequently, there can be bottlenecks in projects with large sample cohorts.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>We have described the all-in-one sequencing (AIO-seq) method, where instead of performing size-selection and quantification for samples individually, one sample one tube, up to 116 samples are pooled and analyzed in a single tube, ‘All-In-One’. The AIO-seq method pools libraries based on the samples’ expected data yields and the calculated concentrations of the size selected regions (target region), which can easily be obtained with the Agilent 2100 Bioanalyzer and Qubit Fluorometer. AIO-seq was applied to whole genome sequencing and RNA-seq libraries successfully, and it is envisaged that it could be applied to any type of NGS library, such as chromatin immunoprecipitation coupled with massively parallel sequencing, assays for transposase-accessible chromatin with high-throughput sequencing, and high-throughput chromosome conformation capture. We also demonstrated that for genetic population samples with low coverage sequences, like recombinant inbred lines (RIL), AIO-seq could be further simplified, by mixing the libraries immediately after PCR, without calculating the target region concentrations.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>The AIO-seq method is thus labor saving and cost effective, and suitable for projects with large sample cohorts, like those used in plant breeding or population genetics research.</jats:p></jats:sec>