Classification Of Mixed Plant Samples By Next-Generation Sequencing release_7egqhiydwbazrfwkz5mek4d2lq

Abstract

<strong>Classification of Mixed Plant Samples by Next-Generation Sequencing</strong><br> Identification of species in mixed plant samples plays an important role in ecology and sheds light on problems from various research fields. Examples for such samples are pollens (also in bee collections and honey), algae in water samples, food or detritus. The advent of high throughput experiments rendered it possible to obtain sequence data for such samples as an alternative to manual, microscopic classification by experts. But tools for the automated classification of such samples originating from multiple plant species have not been established yet. We developed a bioinformatical workflow to analyze sequences of mixed plant samples through the highly variable species-specific internal transcribed spacer 2 (ITS2) region of the nuclear ribosomal DNA. ITS2 sequences are classified with the naive bayesian RDPclassifier specifically trained with reference sequences from the ITS2 database. To evaluate the performance, we compared results from classical identification based on light microscopy with our sequencing results for 16 bee collected pollen samples. The sequencing technique resulted in higher taxon richness (deeper assignments and more identified taxa) compared to light microscopy. Simulation analyses of taxon specificity and sensitivity indicate that 96% of taxa present in the database are correctly identifiable at the genus level and 70% at the species level. The pipeline thus presents a useful and efficient workflow to identify pollen at the genus and species level without requiring specialized expert knowledge and with high throughput.
In text/plain format

Archived Files and Locations