![]() ![]() However, NGS can be affected by a range of artifacts that arise during the library preparation and sequencing processes, which can negatively impact the quality of the raw data for downstream analyses. Next-generation sequencing (NGS) technologies have expanded the scope of genomics research by increasing throughput many fold compared to traditional Sanger sequencing, at a much lower cost per base ( Pareek et al., 2011) With genome-scale studies now possible in virtually any species within the budget of a standard grant, NGS data are being generated in non-model organisms at an unprecedented pace. Until 5 years ago, genomic research was largely confined to a relatively small number of taxonomic groups in which sequencing efforts were focused on a handful of model organisms. We use benchmark datasets generated from control samples across a range of genome sizes to illustrate that QC inferences made using draft assemblies are broadly equivalent to those made using a well-established reference, and describe QC tools routinely used in our production facility to assess the quality of NGS data from non-model organisms. Here we show that by generating a rapid, non-optimized draft assembly of raw reads, it is possible to obtain reliable and informative QC metrics, thus removing the need for a high quality reference. Yet non-model organisms often lack a suitable reference to map sequence reads against, making alignment-based quality control (QC) of NGS data more challenging than cases where a well-assembled genome is already available. Genome-scale data, once restricted to a small number of biomedical model organisms, can now be generated for virtually any species at remarkable speed and low cost. Next-generation sequencing (NGS) technologies have dramatically expanded the breadth of genomics. 2Institute of Evolutionary Biology, Ashworth Laboratories, University of Edinburgh, Edinburgh, UK.1Edinburgh Genomics, Ashworth Laboratories, University of Edinburgh, Edinburgh, UK. ![]() Trivedi 1 *, Timothée Cézard 1, Stephen Bridgett 1, Anna Montazam 1, Jenna Nichols 1, Mark Blaxter 1,2 and Karim Gharbi 1,2 ![]()
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