bioRxivEvolutionary BiologyPreprint
Field-based species identification in eukaryotes using real-time nanopore sequencing.
Joe ParkeriD, Andrew J. HelmstetteriD, Dion Devey, Alexander S.T. Papadopulos
Abstract
Advances in DNA sequencing and informatics have revolutionised biology over the past four decades, but technological limitations have left many applications unexplored1,2. Recently, portable, real-time, nanopore sequencing (RTnS) has become available. This offers opportunities to rapidly collect and analyse genomic data anywhere3-5. However, the generation of datasets from large, complex genomes has been constrained to laboratories6,7. The portability and long DNA sequences of RTnS offer great potential for field-based species identification, but the feasibility and accuracy of these technologies for this purpose have not been assessed. Here, we show that a field-based RTnS analysis of closely-related plant species (Arabidopsis spp.)8 has many advantages over laboratory-based high-throughput sequencing (HTS) methods for species level identification-by-sequencing and de novo phylogenomics. Samples were collected and sequenced in a single day by RTnS using a portable, \"al fresco\" laboratory. Our analyses demonstrate that correctly identifying unknown reads from matches to a reference database with RTnS reads enables rapid and confident species identification. Individually annotated RTnS reads can be used to infer the evolutionary relationships of A. thaliana. Furthermore, hybrid genome assembly with RTnS and HTS reads substantially improved upon a genome assembled from HTS reads alone. Field-based RTnS makes real-time, rapid specimen identification and genome wide analyses possible. These technological advances are set to revolutionise research in the biological sciences9 and have broad implications for conservation, taxonomy, border agencies and citizen science.
- organism: Arabidopsis
- assay: long-read sequencing
- research focus: phylogenetics / evolution
- research focus: genome assembly
- research focus: cell type annotation
- research focus: clustering
- approach: experimental assay
- approach: deep learning
- approach: statistical model
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