Data from: Automated DNA-based plant identification for large-scale biodiversity assessment

Rapid degradation of tropical forests urges to improve our efficiency in
large-scale biodiversity assessment. DNA-barcoding can assist greatly in
this task, but commonly used phenetic approaches for DNA-based
identifications rely on the existence of comprehensive reference
databases, which are infeasible for hyperdiverse tropical ecosystems.
Alternatively, phylogenetic methods are more robust to sparse taxon
sampling but time-consuming, while multiple alignment of
species-diagnostic, typically length-variable markers can be problematic
across divergent taxa. We advocate the combination of phylogenetic and
phenetic methods for taxonomic assignment of DNA-barcode sequences against
incomplete reference databases such as GenBank, and we developed a
pipeline to implement this approach on large-scale plant diversity
projects. The pipeline workflow includes several steps: database
construction and curation, query sequence clustering, sequence retrieval,
distance calculation, multiple alignment and phylogenetic reconstruction.
We describe the strategies used to establish these steps and the
optimisation of parameters to fit the selected psbA-trnH marker. We tested
the pipeline using infertile plant samples and herbivore diet sequences
from the highly threatened Nicaraguan seasonally dry forest and exploiting
a valuable purpose-built resource: a partial local reference database of
plant psbA-trnH. The selected methodology proved efficient and reliable
for high-throughput taxonomic assignment, and our results corroborate the
advantage of applying ‘strict’ tree-based criteria to avoid false
positives. The pipeline tools are distributed as the scripts suite
‘BAGpipe’ (pipeline for Biodiversity Assessment using GenBank data), which
can be readily adjusted to the purposes of other projects and applied to
sequence-based identification for any marker or taxon.